The Health Information Workforce: Current and Future Developments (Health Informatics) 3030818497, 9783030818494

Table of contents :
Contents
Part I: Introduction
Chapter 1: The Specialised Data, Information, and Knowledge Workforce in Health: Present and Future
Specialising in Data, Information and Knowledge Work in Health
Ways of Exploring HIDDIN Work
The Present Position of HIDDIN Work
An Overview of HIDDIN Work
Health Informatics
Digital Health
Data
Information
Knowledge
The Future: A Coherent Framework for HIDDIN Work
Professionalisation of HIDDIN Work
The Future: Deepening Insights into HIDDIN Work
References
Part II: Identity
Chapter 2: Health Information Work: A Scoping Review
Introduction
Methods
Results
Bibliometric Analysis
Topic Analysis
Role Analysis
Recurring Concerns
Discussion
Conclusion
Appendix
Bibliography
Chapter 3: The Socio-technical Foundations of Health Information Work
Origins, Underlying Values and Principles
Theories and Concepts for Design and Analysis
Theory-Based Analysis of Social and Technical Interchange
Relevance of Socio-technical Principles to Digital Health
Relevance of Socio-technical Principles to the HIDDIN Workforce
Promoting Digital Health Literacy
Advocating for Users of Technology in Healthcare
Contributing to Research and Knowledge About Digital Health
References
Chapter 4: Occupational Classifications in the Health Information Disciplines
Introduction
How Occupations Are Classified Internationally
Occupational Classification of HIDDIN Work
Categories Derived from Global Job Listings
Categorising Roles or Competencies
Conclusion
References
Chapter 5: Competencies, Education, and Accreditation of the Health Information Workforce
Introduction
Competency
Competency Frameworks
Competency-Based Education
Accreditation
Professionalisation
Roles and Responsibilities: Specialisation, Convergence, Overlap
Competencies and Information Governance
Education and Training: Traditional Academic Accredited Model or Alternative Pathways?
Conclusion
References
Chapter 6: Professional and Industry Certifications for the Health Information Workforce
Introduction
Why and How Individuals Are Certified?
Comparisons with Health Care and Information Professions
Certifications for the HIDDIN Workforce
Discussion
Conclusions
References
Chapter 7: Professional Learning and Development for the Health Information Workforce
Introduction
Professional Development and Professional Learning
Evidence-Based Strategies for Professional Learning
Current Professional Development Practices
Evaluating Before Choosing Professional Learning Options
Conclusion
References
Part III: Innovation
Chapter 8: Health Workforce Learning in Response to Artificial Intelligence
Introduction
A Framework for Professional Learning
Discussion
References
Chapter 9: The Rise of the Consumer Health Information Specialist
Introduction
The Work of Consumer Health Information Specialists
Pathways into Consumer Health Information Work
Where Do Consumer Health Information Specialists Work?
Principles of Professional Practice
Conclusion
References
Chapter 10: The Globalisation of Health Information Work
Introduction
A Rapid Review of the Literature
The Globalised Health Information Workforce
HIDDIN Workforce Implications
References
Part IV: Impact
Chapter 11: Leadership Roles in the Specialist Digital Health Workforce
Introduction
Leadership Traits and Skills
Digital Health Leadership for Workforce Well-Being
Leadership for Diversity, Equity, and Inclusion
Archetypal Leadership Roles
Conclusion
References
Chapter 12: The Specialist Digital Health Workforce Impact on Access and Equity
Introduction
Digital Health as a Facilitator of Access and Equity
Digital Health Work in US Communities
Digital Health Work in Australian Communities
Digital Health Work in Responses to COVID-19
Bringing the HIDDIN Workforce Out of the Clinic
References
Chapter 13: The Impact on Safety and Quality of Care of the Specialist Digital Health Workforce
Introduction
Responding to a Pandemic
Building a Cohesive Workforce
Managing Risk
Improving Transparency
Operating a Virtual Clinic
Sharing Knowledge Globally
Discussion and Conclusions
References
Part V: Case Studies
Chapter 14: Working as a CIO in Healthcare
Introduction
Emergence of the CIO
Key Functions of CIOs
Becoming a CIO
Challenges and Directions for CIO Roles
References
Chapter 15: Working as a Health Cybersecurity Specialist
Introduction
The Challenge of Being a Healthcare Cybersecurity Specialist
Simon Cowley, Department of Health, Australia
Christopher Bolan, St John of God Healthcare, Australia
Ken Fowle, Child and Adolescent Health Service, Australia
Richard Staynings, Cylera, USA
Trish Williams, Flinders University, Australia
Conclusion
References
Chapter 16: Working as a Health Data Scientist
Introduction
Natasha Donnolley, University of New South Wales, Australia
Lachlan Rudd, eHealth New South Wales, Australia
Oisin Fitzgerald, University of New South Wales, Australia
Miranda Davies-Tuck, Hudson Institute of Medical Research, Melbourne
Conclusion
References
Chapter 17: Working as a Health AI Specialist
Introduction
Case Study 1: Feasibility of ML for Chemotherapy Screening
Case Study 2: Predicting Outcomes in Children’s Brain Tumours
Case Study 3: Precision Medicine for Congenital Hearing Loss
Ethics and Governance in Cases 2 and 3
Training the Humans, in Cases 2 and 3
Impact in Cases 2 and 3
Conclusions Regarding Cases 2 and 3
Case Study 4: Building a Learning System in the Intensive Care Unit
Case Study 5: Automating Vertebral Fracture Detection and Reporting
Conclusions and Recommendations
References
Chapter 18: Working as a Health Information Manager
Introduction
Lorraine Fernandes, Fernandes Healthcare Insights, USA
Cameron Barnes, Cabrini Health, Australia
Deneice Marshall, Barbados Community College, Barbados
Mandy Burns, Manchester University, UK
Sabu Karakka Mandapam, Manipal Academy of Higher Education, India
Gemala Hatta, University of Indonesia and Repati Indonesia University
Oknam Kim, Sungkyunkwan University, Republic of Korea
Conclusion
References
Chapter 19: Working as a Health Librarian
Introduction
Sarah Hayman, Barwon Health, Australia
Aoife Lawton, National Health Service Executive Librarian, Ireland
Gemma Siemensma, Ballarat Health Services, Australia
Helen Baxter, Austin Health, Australia
Meena Gupta, Australian Catholic University, Australia
Blair Kelly, Deakin University, Australia
Conclusion
References
Chapter 20: Working as a Health Research Information Specialist
Introduction
Steve McDonald, Cochrane Australia, Australia
Suzanne Lewis, NSW Central Coast Local Health District, Australia
Cecily Gilbert, University of Melbourne, Australia
Terena Solomons, Western Australian Group of Evidence-Informed Healthcare Practice, Australia
Kristan Kang, Australian Research Data Commons, Australia
Mari Elisa Kuusniemi, Helsinki University Library, Finland
Conclusion
References
Chapter 21: Working as an Allied Health Informatician
Introduction
Implications and Recommendations
References
Chapter 22: Working as a Medical Informatician
Daniel Luna: Hospital Italiano, Buenos Aires
Rebecca Grainger: Hutt Hospital and University of Otago, New Zealand
Daniel Capurro: University of Melbourne
References
Chapter 23: Working as a Nursing and Midwifery Informatician
Introduction
Sally Britnell, Auckland University of Technology, New Zealand
Lisa Livingstone, Nelson Marlborough District Health Board, New Zealand
Abin Chacko, Waitemata District Health Board, New Zealand
Karen Blake, healthAlliance NZ Ltd, New Zealand
Karen Day, University of Auckland, New Zealand
Discussion and Conclusion
References
Chapter 24: Working as a Public Health Informatician
Introduction
Robyn Whittaker, Waitemata District Health Board, New Zealand
Vicki Bennett, Australian Institute of Health and Welfare, Australia
Vanessa Selak, University of Auckland, New Zealand
Brian Stokes, University of Tasmania, Australia
Discussion and Conclusion
References
Chapter 25: Journeys into Becoming a Digital Health Specialist
Introduction
Urooj Raza Khan: “I wanted my health records anywhere anytime.”
Leanna (Lee) Woods: “Administration should not absorb one third of my time as a nurse.”
Gerardo Luis (Ikee) Dimaguila: “…passionate about patient empowerment and bridging healthcare gaps through technological innovation.”
Mohamed Khalifa: “I could add greater value in population health through informatics, as compared to treating individual patients.”
Greig Russell: “The daylight is slowly creeping in.”
Elizabeth (Liz) Schoff: “Technology has changed, but people are still key to leveraging its value.”
Saswata (Sas) Ray: “Some beautiful paths can’t be discovered without getting lost.” (Erol Ozan)
Discussion and Conclusion
References
Index

Citation preview

Health Informatics

Kerryn Butler-Henderson Karen Day Kathleen Gray   Editors

The Health Information Workforce Current and Future Developments

Health Informatics

This series is directed to healthcare professionals leading the transformation of healthcare by using information and knowledge. For over 20 years, Health Informatics has offered a broad range of titles: some address specific professions such as nursing, medicine, and health administration; others cover special areas of practice such as trauma and radiology; still other books in the series focus on interdisciplinary issues, such as the computer based patient record, electronic health records, and networked healthcare systems. Editors and authors, eminent experts in their fields, offer their accounts of innovations in health informatics. Increasingly, these accounts go beyond hardware and software to address the role of information in influencing the transformation of healthcare delivery systems around the world. The series also increasingly focuses on the users of the information and systems: the organizational, behavioral, and societal changes that accompany the diffusion of information technology in health services environments. Developments in healthcare delivery are constant; in recent years, bioinformatics has emerged as a new field in health informatics to support emerging and ongoing developments in molecular biology. At the same time, further evolution of the field of health informatics is reflected in the introduction of concepts at the macro or health systems delivery level with major national initiatives related to electronic health records (EHR), data standards, and public health informatics. These changes will continue to shape health services in the twenty-first century. By making full and creative use of the technology to tame data and to transform information, Health Informatics will foster the development and use of new knowledge in healthcare. More information about this series at http://www.springer.com/series/1114

Kerryn Butler-Henderson Karen Day  •  Kathleen Gray Editors

The Health Information Workforce Current and Future Developments

Editors Kerryn Butler-Henderson Digital Health Hub, STEM College RMIT University Bundoora VIC, Australia

Karen Day School of Population Health University of Auckland Auckland, New Zealand

Kathleen Gray Centre for Digital Transformation of Health University of Melbourne Melbourne VIC, Australia

ISSN 1431-1917     ISSN 2197-3741 (electronic) Health Informatics ISBN 978-3-030-81849-4    ISBN 978-3-030-81850-0 (eBook) https://doi.org/10.1007/978-3-030-81850-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

Part I Introduction 1 The Specialised Data, Information, and Knowledge Workforce in Health: Present and Future ��������������������������������������������    3 Kerryn Butler-Henderson, Karen Day, and Kathleen Gray Part II Identity 2 Health Information Work: A Scoping Review��������������������������������������   23 Cecily Gilbert, Kathleen Gray, and Simone Pritchard 3 The Socio-technical Foundations of Health Information Work����������������������������������������������������������������������������������������������������������   55 Carey A. Mather and Sue Whetton 4 Occupational Classifications in the Health Information Disciplines ��������������������������������������������������������������������������   71 David T. Marc, Prerna Dua, Susan H. Fenton, Karima Lalani, and Kerryn Butler-Henderson 5 Competencies, Education, and Accreditation of the Health Information Workforce ��������������������������������������������������������������   79 Ann Ritchie, Gemma Siemensma, Susan H. Fenton, and Kerryn Butler-Henderson 6 Professional and Industry Certifications for the Health Information Workforce ��������������������������������������������������������������   97 Kathleen Gray 7 Professional Learning and Development for the Health Information Workforce����������������������������������������������������������������������������  115 Joseph Crawford and Kerryn Butler-Henderson

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Contents

Part III Innovation 8 Health Workforce Learning in Response to Artificial Intelligence ������������������������������������������������������������������������������  129 Sandeep Reddy and Paul Cooper 9 The Rise of the Consumer Health Information Specialist ��������������������������������������������������������������������������������������������������  139 Rachel de Sain 10 The Globalisation of Health Information Work������������������������������������  151 Kathleen Gray Part IV Impact 11 Leadership Roles in the Specialist Digital Health Workforce��������������  171 Tiffany I. Leung, Karen H. Wang, Terika McCall, and Frits van Merode 12 The Specialist Digital Health Workforce Impact on Access and Equity��������������������������������������������������������������������������������������������������������  185 Anna G. Shillabeer, Lawrence Sambrooks, and Aydan C. Shillabeer 13 The Impact on Safety and Quality of Care of the Specialist Digital Health Workforce������������������������������������������������������������������������  201 Angela Ryan, Brendan Loo Gee, Susan H. Fenton, and Meredith Makeham Part V Case Studies 14 Working as a CIO in Healthcare������������������������������������������������������������  217 Meredith Makeham, Angela Ryan, Richard Taggart, Clair Sullivan, Peter Sprivulis, and Keith McNeil 15 Working as a Health Cybersecurity Specialist��������������������������������������  225 Patricia A. H. Williams, Simon Cowley, Christopher Bolan, Ken Fowle, and Richard Staynings 16 Working as a Health Data Scientist��������������������������������������������������������  237 Natasha Donnolley, Lachlan Rudd, Oisin Fitzgerald, and Miranda Davies-Tuck 17 Working as a Health AI Specialist����������������������������������������������������������  247 Angela C. Davies, Alan Davies, Anthony Wilson, Haroon Saeed, Catherine Pringle, Iliada Eleftheriou, and Paul A. Bromiley 18 Working as a Health Information Manager������������������������������������������  269 Trixie Kemp, Lorraine Fernandes, Cameron Barnes, Deneice Marshall, Mandy Burns, Sabu Karakka Mandapam, Gemala Hatta, Oknam Kim, and Kerryn Butler-Henderson

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19 Working as a Health Librarian��������������������������������������������������������������  281 Ann Ritchie, Sarah Hayman, Aoife Lawton, Gemma Siemensma, Helen Baxter, Meena Gupta, and Blair Kelly 20 Working as a Health Research Information Specialist������������������������  295 Ann Ritchie, Steve McDonald, Suzanne Lewis, Cecily Gilbert, Terena Solomons, Kristan Kang, and Mari-Elisa Kuusniemi 21 Working as an Allied Health Informatician������������������������������������������  309 Mark Merolli, Kirsty Maunder, Dawn Choo, Khye Davey, and Yasmine Probst 22 Working as a Medical Informatician ����������������������������������������������������  319 Daniel Capurro, Rebecca Grainger, and Daniel Luna 23 Working as a Nursing and Midwifery Informatician ��������������������������  327 Karen Day, Sally Britnell, Lisa Livingstone, Abin Chacko, and Karen Blake 24 Working as a Public Health Informatician��������������������������������������������  339 Karen Day, Robyn Whittaker, Vicki Bennett, Vanessa Selak, and Brian Stokes 25 Journeys into Becoming a Digital Health Specialist ����������������������������  349 Urooj R. Khan, Leanna Woods, Gerardo Luis C. Dimaguila, Mohamed Khalifa, Elizabeth Schoff, Greig Russell, and Saswata Ray Index������������������������������������������������������������������������������������������������������������������  361

Part I

Introduction

Chapter 1

The Specialised Data, Information, and Knowledge Workforce in Health: Present and Future Kerryn Butler-Henderson, Karen Day, and Kathleen Gray

Abstract  The health information workforce has existed for more than a century yet remains one of the most hidden workforces in health. This workforce supports the planning, delivery, and improvement of healthcare services by analysing, designing, developing, implementing, maintaining, managing, operating, evaluating, or governing health data, information, or knowledge. Lack of awareness about this workforce has flow-on effects: shortages of skilled workers, inadequate skills training opportunities, and ultimately suboptimal health information and communication technology implementation and scaling up. Even in the era of digital health, this essential workforce supporting the safe and efficient management of health and care is a hidden workforce. We call it the HIDDIN workforce. The HIDDIN workforce comprises the practitioners who have key responsibility for the specialised work in Health Informatics, Digital, Data, Information, and kNowledge (HIDDIN). This chapter examines each of these parts of the HIDDIN workforce and defines the framework for this workforce used throughout this book. This chapter also defines the purpose of this book and presents the three c­ onceptual

K. Butler-Henderson (*) Digital Health Hub, STEM College, RMIT University, Bundoora, VIC, Australia e-mail: [email protected] K. Day School of Population Health, University of Auckland, Auckland, New Zealand e-mail: [email protected] K. Gray Centre for Digital Transformation of Health, University of Melbourne, Melbourne, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_1

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lenses that have been used to frame the structure of this book. This book provides a clearer and more comprehensive view than ever before of the specialised workforce required to manage and govern the health data, information, and knowledge infrastructure now and in the future. Keywords  Health data science · Health informatics · Health information management · Health knowledge management · Health librarianship

 pecialising in Data, Information and Knowledge Work S in Health Information is critical in the planning, delivery, and improvement of healthcare services, increasingly so in the twenty-first century era of health big data analytics, digital, and Internet-supported hospitals, artificial intelligence in health, and health self-quantification. Information means many things to many people in the health sector. Broadly, it can be the data of a patient’s diagnosis and treatment, such as a fasting blood sugar level. It can be a record of information to inform a prognosis, such as pelvic pain, amenorrhoea, increased CA125 levels, imaging showing an ovarian mass indicating ovarian cancer. It can be the knowledge gained from the outcomes that a healthcare system is achieving, such as the infant mortality rate in place X in decade Y. It can be the wisdom from health messages that are designed to support responsible public health behaviours, such as how to avoid sexually transmitted infections. It can be the summary of evidence from a field of health research, such as what is known about the effects of calcium supplements on osteoarthritis. It can be business intelligence about health care services and how people use them, such as comparative hospital emergency department waiting times, or how rapidly regional governments are rolling out COVID-19 vaccines for people in residential aged care. For the health sector to get full value from health information, the sector needs more than common sense or basic health and information literacy among its professionals; consequently, there is increasing recognition that everyone in the health workforce must acquire basic digital health capabilities. Even so, the need remains for a health workforce group that possesses specialised knowledge and skills, with the capabilities to manage the sophisticated structures, systems, and processes that support and advance everyone’s ability to work safely and efficiently with health information. Who are the health information specialists? What kind of work do they do, where, and when are they needed? Although this work is fundamental, it is hard to see and even harder to understand and define, even for some of the people who do it and certainly for many of the other people who work in the health sector and

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beyond it. Unlike people in more commonly recognised healthcare roles, these specialists and their employers face challenges in finding one another. Specialised health information workers are not easy to locate in any healthcare organisation, nor is their value to health systems clearly articulated as a component of the overall health workforce (Martín-Sánchez and Gray 2014; Gray et  al. 2019). Lack of awareness about this workforce has flow-on effects: shortages of skilled workers, inadequate skills training opportunities, and ultimately suboptimal health information and communication technology implementation and scaling up. Even in the era of digital health, this essential workforce supporting the safe and efficient management of health and care is a hidden workforce. We call it the HIDDIN workforce. The HIDDIN workforce comprises the practitioners who have key responsibility for the specialised work in Health Informatics, Digital, Data, Information, and kNowledge. Roles in the HIDDIN workforce include functions not only as users of health data, information, or knowledge; rather they are managers, policymakers, clinicians, educators, researchers, and leaders who are tasked with analysing, designing, developing, implementing, maintaining, operating, evaluating, and governing the formats, technologies, systems, and services that mobilise health data, information, and knowledge. Typically, but not exclusively, this may include people who practice, administer, teach, or research in areas such as Biomedical engineering, Biomedical informatics, Biostatistics, Clinical coding, Clinical costings, Clinical documentation improvement, Clinical informatics, Consumer health information services, Digital health infrastructure, eHealth systems, Epidemiology, Health analytics, Health app development, Health artificial intelligence, Health cybersecurity, Health data science, Health informatics, Health information governance, Health information management, Health information systems or services, Health information technology, Health innovation, Health interoperability, Health librarianship, Health simulation, Health technology assessment, Medical research data management, Telehealth platform services, and Translational bioinformatics. The aim of this book is to make the HIDDIN workforce visible and to explore its place in the health sector. The book begins by setting out the foundations of current knowledge about the HIDDIN workforce—how scholars have described it, how economies have categorised it, how education, professional development and certification have shaped and influenced it. The book continues by giving consideration to some emerging approaches to the work and their implications for this workforce—artificial intelligence and machine learning, consumerism, globalisation, and other leadership challenges. The book also provides a finer-grained analysis of what this workforce contributes to the health sector, in terms of safety and quality, access and equity. The book concludes with case studies of practitioners in the HIDDIN workforce that relate their real-world roles, challenges, changes, and achievements.

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Ways of Exploring HIDDIN Work This book looks at the HIDDIN workforce through three conceptual lenses, as defined in Box 1.1. Box 1.1 Three conceptual lenses of this book Identity Who is this workforce, and what do they do; what positions do they occupy in the health workforce; what specialised knowledge and skills do they have, and what do they do to acquire these? Innovation What is this workforce doing to advance the ways that its own members practice in response to healthcare changes; what is it doing to transform the ways that other people in the health system gain value from health information? Impact What is the impact of the work done by this workforce, and what criteria can be used to measure or assess its impact; what could be the impact of a more deliberately structured and supported workforce?

The motifs of identity, innovation, and impact (adapted from Bärnighausen and Bloom 2009) frame this book, as applied to specialised work in support of health data, health information, and health knowledge. The three concepts of data, information, and knowledge are fundamental distinctions in the discipline of information science (Zins 2007), and they align with many definitions used in models of this field of work (for example Georgiou 2002). Data, information, and knowledge management work are not easy to define or differentiate, and this is advantageous for our research. This allows us to explore a broad spectrum of people who may identify with this workforce, either exclusively or jointly with another health workforce identity (for example does a chief clinical information officer identify as an information manager, health informatician, as a clinician or as a combination of all three?). This means that we include work which may be called “information technology,” but we are not limited to it. Even as data, information, and digital capabilities in health professionals increase, and as the technology changes to automate certain functions, we anticipate that there will be a continuing need for specialised knowledge and skills, even though we cannot be certain who will possess them or review them. We use the theme of identity to conceptualise the work that is being done by this workforce. We know that this workforce finds it hard to clearly characterise who the people are and what work they do, both within its various professional communities and to external stakeholders. We broadly define work in scope for this book if it meets three criteria: the work is concerned with the management of data, the management of information, and/or the management of knowledge; the work is done in, about, for and/or with health (i.e. by people employed in the system, outsourced workers, government workers, industry, or advocacy groups, and researchers); and

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the work is done in connection with clinical care, population health, health policy and health research. Innovation—in terms of what the future holds for what health information work is done, how it is done, and who does it—may be explored using concepts of workforce automation and the digital workforce. The future of work of all kinds, everywhere in the world economy, is expected to change under the influence of the increase in computing power and artificial intelligence and the spread of Internet access and the Internet of Things. (Colbert et al. 2016; OECD 2016; Smith and Page 2016). We can use the theme of innovation to conceptualise the dynamic situation in which the health information workforce finds itself in the era of digital health. The future of the health information workforce may be influenced by intersecting scenarios. Some but not all people in the workforce may keep up with the technical skills required (re-skilling). The work may become the responsibility of another role in the workforce (platform economy). The work may be restructured as some industry sectors shrink and others expand (structural transformation). Human performance of the work may be made obsolete by machines or computer programmes (automation). These considerations apply to the health information workforce as much as they do to all other parts of the health workforce. They allow us to engage with the current workforce and its stakeholders to explore constructive thinking about the work and to plan for inevitable change. Impact, in the sense of the contribution to the overall operation of the health system of the work done by health information professionals, may be viewed through the lens of health system performance. Many national governments monitor and report the operations of their publicly funded health care systems using performance indicators derived from international frameworks produced by OECD, WHO, and similar agencies (Smith et al. 2012). Typically, a nation’s selected indicators are thematically grouped and linked to essential metrics, for example accessibility, appropriateness of care, competence, and capability, comprehensiveness, continuity of care, effectiveness, efficiency, efficient resource allocation, equity, expenditure, and cost, healthy lives, health status, innovation, and capacity to improve integration, patient experience, productivity, technical efficiency, responsiveness, and trust, safety. These aspects of performance can provide a comprehensive, systematic, and contextualised research approach to understanding the impact of the health information workforce in terms of clinical safety and quality of care, the patient or client experience, and service sustainability. We can use the theme of impact to evaluate and improve the work that is done by the health information workforce with a clear view of what we want to achieve for health and care.

The Present Position of HIDDIN Work Parts of the HIDDIN workforce have been identified and organised formally for well over a century. Consider these milestones: The creation of the Index Medicus in the mid-nineteenth century, the analog-era forerunner of PubMed, led to the

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establishment of the Medical Library Association in 1898 (Birchette 1973). At a time when clinical notes were written on cards or in large ledgers, medical secretaries began to manage this form of health information and organised themselves into the American Association of Medical Record Librarians in 1928 (Huffman 1947). The International Federation for Information Processing established Technical Committee 4, Health Care and Biomedical Research, and convened the first formal meeting of informaticians in Europe in 1967 (Peterson 2014). With the third and fourth industrial revolutions, health information practitioners have continued to form new associations around new health sector demands. For example a national membership organisation and network “for people working in health information and support … for an improved experience for patients and the public” (https:// pifonline.org.uk), and a global community “for health data science and analytics that helps people connect, collaborate, share, learn, and make a meaningful impact on healthcare” (https://www.linkedin.com/company/healthdsa/). Complex work in new kinds of work environments—sometimes called digital health ecosystems (for example Iyawa et  al. 2016)—is being generated by the increasing pace of digital health information technologies in the present century. Managing and governing Internet-connected data, information, and knowledge in the service of healthcare, population health, and biomedical research, popularly called digital health since the 1990s, is critical to achieve higher order health system goals nationally and internationally (Grossmann et  al. 2011; World Health Organisation [WHO], 2010a, 2021). This entails broad new strategic planning about how health organisations shift to digitally-enabled ways of operating and how they structure their workforce to do so (Kalra et al. 2016; Topol 2019). One emerging feature of digital health is a more fluid health workforce, less routinised, and more mobile and globalised; this requires major adjustments in how healthcare organisations and health systems are structured, how they support professional learning and development, and how their cultures adapt to digitally enabled ways of working (Accenture 2016). Making these adjustments is an important aspect of managing unforeseen or unintended consequences of digital health initiatives and mitigating risks to their success (Williams 2016). It is not safe to assume that there are appropriately skilled professional practitioners behind every digital health system’s design and operation; instead, the identification and organisation of technologically skilled health information work has gaps and blind spots (French 2014). Despite major investments in digital health systems and high expectations of the benefits that will ensue (Geiger and Gross 2017), there is scant empirical research into the specialised workforce that is needed to manage and govern the associated information infrastructure. Much of the human-centred research in digital health focuses on groups of stakeholders or end users. The considerable research on the technological and economic infrastructure of digital health is mostly silent about the specialised human resources needed to create and maintain it (Baird et al. 2014). This is ironic, to say the least, considering how highly regulated many health professions are. It is known that many thousands of health information practitioners work in health systems around the world, whether professionally credentialled or

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self-­described. They work as employees, contractors, entrepreneurs, public servants; in public, private, and non-profit organisations in the health sector, in a range of roles that are rapidly being changed and globalised by the networked nature of digital health (Kluge 2017). Up to now, efforts to illuminate their actual and potential contributions to digital health have been scattered. The HIDDIN workforce census is a unique attempt to quantify and qualify the HIDDIN workforce. Originating in Australia (Butler-Henderson et  al. 2017), the census was expanded to New Zealand in 2018 (Day and Grainger 2019) and globally in 2021 (Butler-Henderson and Gray 2021). The 2018 Australian HIDDIN census (Butler-Henderson and Gray 2018) confirmed the HIDDIN composition of this workforce and provided insights into the qualifications, credentials, professional memberships, and jobs in this workforce. Results from this census are used below to highlight the differences between each area of HIDDIN.  This census is an important tool in the ongoing evaluation of the current and future configuration and development needs of this workforce.

An Overview of HIDDIN Work Health information work and, as the health sector evolves, digital health work can be seen as a continuum of specialists whose overlapping roles support and advance healthcare through health information, technology, and innovation. Here we offer an overview of the continuum of HIDDIN work.

Health Informatics Health Informatics is defined as the theory and practice of health information systems design, development, implementation, and management for the improvement of health outcomes (Friedman 2012). It is the integration of several sciences, including healthcare, computer and information science, business science and cognitive science (Sweeney 2017; Friedman 2012). Health informatics includes the design, development, and implementation of information technologies, analysis of data and information for application in health services, management, and support of information systems and services, and the provision of health services via information and communications technologies, such as video, phone, social media and wearables. Some definitions are specific to an academic field, such as bioinformatics, and others are linked to clinical professions, such as nursing, pharmacy, or medicine (Hübner et al. 2018). Other areas captured above include primary health informatics, population, or public health informatics, health app development, health information systems or services, health information technology, health technology assessment, telehealth platform services, and translational bioinformatics. This

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diversity of definitions is most likely the result of rapid advances in technology and the diverse adoption of digital and information systems in health services. An analysis of the health informatics area in the previously referenced Australian health information workforce census reported that most (51.4%) people working in these jobs have done so for less than 10 years, reflecting the relative newness of these jobs in many health organisations (Butler-Henderson et  al. 2019a). Furthermore, nearly two-thirds (63.6%) of respondents have been in their current role for less than 5 years, reflecting that most of these jobs are designed as time limited, temporary jobs. The average number of weekly hours worked was 33.5 h, further reflecting the part-time, casualisation of these jobs. A third (31.2%) of respondents undertake another role (i.e. they do not have a dedicated health informatics role), with a quarter (23.9%) reporting they are a registered health practitioners. Less than half (41.5%) of the roles are in a hospital, with roles spread across a wide variety of health settings. The majority of respondents do not hold a formal qualification in health informatics.

Digital Health The second part of HIDDIN is Digital, for digital health. Digital health is “the field of knowledge and practice associated with the development and use of digital technologies to improve health” (WHO 2021). This expands the WHO definition of ehealth to include participatory consumer methods and tools (Kukafka 2019) and a wide range of connected smart devices, such as the Internet of Things, advanced associated sciences and analytics, artificial intelligence, and associated technologies (Jayaraman et al. 2020). Digital health brings the activated and engaged health consumer into the scope of health information workers, as well as leveraging the affordances of automation and advanced technologies. The work roles span all other areas of the HIDDIN continuum, and attract people from backgrounds in enterprise, technology, and education, for example biomedical engineering, digital health infrastructure, health innovation, health interoperability, and health simulation.

Data Data form the raw materials of the HIDDIN work. Without Data, HIDDIN work would not exist. “Merely using data isn’t really what we mean by ‘data science’. A data application acquires its value from the data itself, and creates more data as a result. It’s not just an application with data; it’s a data product. Data science enables the creation of data products.” (Loukides 2011). Data scientists analyse data to access actionable insights from information. Data science activities include data gathering, preparation, and exploration, data transformation and representation, computing, data modelling, visualisation and presentation, and the science of data

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science (Donoho 2017). The data could be “big data,” for example large data collection such as routinely collected clinical data, or “little data” e.g. data in EHRs about specific patients. Since data are core to HIDDIN work, they form the foundation of all roles associated with HIDDIN work. Disciplines such as biomedical informatics, population, or public health informatics could be categorised here to the extent that they manage methods and tools for analysing micro or big data to draw meaningful conclusions. Other discipline areas that lead to work in this area include epidemiology and medical research data management, and some specialised mathematical and statistical modelling. Health artificial intelligence and machine learning roles clearly can be located here. A yet to be published analysis of data roles in the 2018 Australian health information workforce census showed an emerging and evolving field. Most (77.3%) have a formal mathematics or data analytics qualification, but none reported a specific health qualification (which may reflect the small sample included in this census). Like health informatics, the majority (57.0%) have worked in the field for less than 10 years, with 65.1% in their current role for less than 5 years, highlighting the emergence of these jobs in health. Where this area differs from health informatics is that the majority (75.4%) work solely in their data role. Their roles are across health, largely in state or local health departments or for federal government organisations.

Information Health information is data that has been given context and meaning (Zins 2007), enabling decision-making for clinical, business, and management purposes to support the improvement of health outcomes. In HIDDIN work, Information processes the raw materials of data to enable the development of meaningful insights. The field of health information ensures “…all stakeholders (providers, consumers, policy-­makers, researchers, patients, etc.) have the best data and information available to make informed decisions” (Fenton et al. 2021). One subset of health information professionals, health information managers, are responsible for the creation, analysis, management, and governance of health information (AHIMA 2020; CHIMA 2021; HIMAA 2014). Job roles in health information can include clinical coding, clinical costing, clinical documentation improvement, health cybersecurity, health information governance, and health information managers. Health information management is the only area in HIDDIN recognised in the International Standard Classification of Occupations (International Labour Office 2008), available as a formal tertiary qualification and many roles requiring the qualification. This profession is rapidly changing to include project management, data analyst and statistician influences (Dimick 2012). This area is transforming with a greater focus on information governance (privacy and data protection) and integrity, management of the information lifecycle, and data analytics (Butler-Henderson 2017). Another distinct group within the HIDDIN workforce that historically is identified strongly

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with health information is health and medical librarians. They develop, store, and make available electronic information resources used by healthcare providers and consumers and HIDDIN workers alike around the globe (Myers 2020). Their expertise has a substantial emphasis on quality assurance of information sources and synthesis of the information to form the evidence base for practice. Their roles are rapidly changing as biomedical publishing and literature search and retrieval are transformed by new technologies. For example the Network of the National Library of Medicine in the USA, and the National Health Service Library and Knowledge Service in the UK aim to provide national and global access to information that enables and supports informed decision-making for health (NNLM 2021; NHS 2021). An analysis of the subset health information managers in the 2018 Australian health information workforce census (Butler-Henderson et al. 2019b) identified the average length of service in a health information management role was 17.0 years. Over four-fifths (81.4%) of respondents work in just one health information role, with 78.6% in a permanent role. The average weekly hours were 35.6 h, highlighting that most roles are full-time. And most (67.8%) are in a hospital environment, with other settings including state and local health departments and federal government organisations. These roles were more clearly defined by job title than any other area of HIDDIN, with most job titles being health information manager or clinical coder. Three-quarters (77.6%) hold a tertiary qualification in health information management.

Knowledge Health kNowledge management is the work of systematically distilling value from raw data and tacit knowledge to provide an organised information base for trustworthy health advice, value-based care services, evidence-based clinical practices, learning health organisations, biomedical research impact, health programme evaluations, and other aspects of health systems (Nonaka and von Krogh 2009). Health knowledge management routinises the transformation of both digital data and real-­ world experiences into explicit, shareable forms of expression that enable a range of people to agree on what is occurring within their remit and to take decisive action on this basis. The central aim of health knowledge management is to keep individuals and communities safe and well; radiating out from this aim, knowledge management is undertaken as part of public health, clinical care, and health administration, research and policy work. Its facets are variously described as knowledge generation, curation, dissemination and translation, and it is essentially methodical work. The tools of the trade include ontologies, standards, guidelines, frameworks, indexes, all designed to give order and structure to cumulative biomedical science and health care knowledge, assure its quality, and make it accessible as appropriate. Health knowledge management is designed into a range of health information and communication technologies: the information architecture of an electronic health

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record system; the search engine optimisation strategies of a consumer health information website; the forum topics set out in a discussion forum for health professionals; the content included in a clinical decision support system; the form in which a health app enables personal recordkeeping. This field of work is experiencing a resurgence, influenced on the one hand by the COVID-19 data demand and infodemic, and on the other hand by the extraordinary potential of precision medicine and machine learning; overviews are offered by Bowden et al. (2020), Chettipally (2020) and Pereira et al. (2021). The work may not be described as such; the people who do it may come from health administration, health librarianship, health education, biomedical publishing, business information systems, and other disciplines. An analysis of the health librarian subset of the Australian health information workforce census identified nearly all (94.2%) of respondents are permanent employees, with the average time in the workforce being 21 years (Gilbert et al. 2020). Most respondents only do this role. Over half (58.3%) of respondents are working in a hospital, with other roles in education facilities or state or local health departments. Further, most (79.85%) hold an entry-level (bachelor) qualification in library studies (or similar) or higher, reflecting the need for qualified people in these roles. The average hours worked (28.6 h) is much lower than the average across the HIDDIN workforce (32.6 h), reflecting most roles are part-time.

The Future: A Coherent Framework for HIDDIN Work Career pathways are not yet clearly defined, although some countries have attempted to develop frameworks to describe the work. There is no specific guideline from the WHO for the health information workforce. The Workload Indicators of Staffing Need (WISN) manual (WHO 2010b) provides a guideline for all health service staffing requirements, but the only reported application of it in the HIDDIN workforce was for health information workforce planning and implementation in Ghana (Ogoe et  al. 2018). The more recent WHO (2016) global strategy for human resources for health provides a high-level strategy, yet does not specify the health information workforce. It has been used to create frameworks for specific professions in HIDDIN, such as TIGER (Hübner et al. 2018), but not HIDDIN as a whole. Shah and Mahrin (2021) describe a framework for big data analysts. Mongan et al. (2018) reported the results of a scoping literature review of workforce planning and implementation frameworks for use in strategic planning in Ireland. They indicate that the most commonly used frameworks are the RE-FRAME and PRECEDE-­ PROCEED frameworks but do not link them to the information workforce. The United Kingdom has produced a set of strategies and frameworks regarding digital health workforce planning, with a focus on health informatics and data science (Liu et al. 2019), and HIMSS has developed a digital health workforce strategy that links workforce to governance and digital health, using principles of governance to frame digital workforce planning (Snowden 2020), but again, neither describe the actual HIDDIN workforce.

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The only two frameworks to comprehensively describe the HIDDIN workforce were developed in Australia. Health Workforce Australia (2013) identifies three levels of the workforce. Level 1 consists of the specialist workforce, specifically indicating that specialists who work (usually full time) with health information systems. Level 2 consists of health professionals whose work consists of significant use of information systems in their work. Level 3 consists of all health professionals who contribute, retrieve, use, and reuse information as part of their professional work. Based on the recommendations from Health Workforce Australia for further work to delineate the workforce, the Australian Digital Health Agency (ADHA 2020) describes eight digital profiles, as presented in Box 1.2. The ADHA developed these profiles in consultation with the industry, but these profiles have not been evaluated. The ADHA describes that a job can have multiple digital profiles. Box 1.2 Australian Digital Health Agency eight digital profiles (ADHA 2020, p.54–55) 1. Patient, consumer, and carer: “maintaining health information, protecting the security and privacy of information, and adopting and advocating for new technologies that help manage their health.” 2. Frontline clinical: “expectations for lifelong learning, adoption of digital technologies, understanding security and privacy, reliable and accurate recordkeeping, ensuring clinical safety with digital technologies, and advocating for consumer use of technology to empower them.” 3. Digital champion: “a digital teacher and champion locally for a particular technology or system.” 4. Business, administration and clinical support: “learning, adoption of digital technologies, understanding security and privacy and reliable and accurate recordkeeping.” 5. Leadership and executive: “leadership of digital transformation and deployment, risk and quality assurance, and understanding sophisticated data analytics to drive better business decisions.” 6. Clinical and technology bridging: “providing advice during the design and development of new digital technologies and systems, and leveraging clinical networks for user testing and adoption.” 7. Education and research: “lifelong learning, translational research, evidence-­based review, and health reform and innovation. It also addresses expectations relating to education.” 8. Technologist: “those performing health information technology functions, including cybersecurity, programming, systems maintenance, digital design, interoperability, IT procurement, resilience and continuity planning, health information management and system testing.”

The ADHA mapped their digital profiles to the three levels detailed by Health Workforce Australia (p52), identifying profiles 1–3 as aligning with level 3, profiles 4–6 as level 2, and profiles 7–8 as level 1 (the specialist roles). This would suggest

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only roles that align with the ADHA’s description of (7) Education and research and (8) Technologist are HIDDIN roles. Yet the above definitions highlight HIDDIN is more than simply “those performing health information technology functions, including cybersecurity, programming, systems maintenance, digital design, interoperability, IT procurement, resilience and continuity planning, health information management and system testing” (ADHA 2020, p.54–55). So how can we describe these five profile descriptions within the health workforce as a collective group?

Professionalisation of HIDDIN Work HIDDIN work, as described above, comprises five parts, with many different roles in each. So how do we define what these parts mean to the overall HIDDIN workforce, and what does this mean for professionalisation? Trowler et  al. (2012) expanded on the previous work of Becher and Trowler (2001) to define a discipline as (Trowler et al. 2012, p.9): Reservoirs of knowledge resources shaping regularised behavioural practices, sets of discourses, ways of thinking, procedures, emotional responses and motivations. These provide structured dispositions for disciplinary practitioners who reshape them in different practice clusters into localised repertoires. While alternative recurrent practices may be in competition within a single discipline, there is common background knowledge about key figures, conflicts and achievements. Disciplines take organisational form, have internal hierarchies and bestow power differentially, conferring advantage and disadvantage.

This definition is appropriate to describe the parts of the HIDDIN workforce; there are discrete knowledge, behaviour, practices, and procedures for each area. As the digitisation of health increases, the uniqueness between each discipline decreases. This is evident from the HIDDIN definitions outlined above. For example health informatics “deals with the storage, retrieval, sharing, and optimal use of data that relates to human health, and it considers how we use this knowledge for problem solving and decision making” (Health Informatics New Zealand 2020). Similarly, health information management professionals “create, acquire, analyse and/or manage information to meet the medical, legal, ethical and/or administrative requirements of the health care system” (HIMAA 2014). These two definitions include the same functions, “storage, retrieval, sharing, and optimal use” versus “create, acquire, analyse and/or manage,” with the difference being “data” versus “information.” Two areas with the same functions at different levels reflects the advancement in digital health: as health move towards the capture of data through information systems as opposed to paper records, this shifts the focus from information brokering to rapid translate of data into actionable knowledge. Digital advances such as data protection, the use of new technologies, innovations such as artificial intelligence and automation of data analysis and management functions will create further overlap across all five areas of HIDDIN. So, whilst it could be argued that each area in HIDDIN is its own discipline, the changing landscape may see these disciplines either merging, transforming, or dissolving over time. Thus, it is

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possible in the future that HIDDIN itself will become the collective discipline and each of these areas a specialisation. The different disciplines attract different job titles, recognising that some jobs could be listed in multiple areas and other jobs overlap with one another. The International Standard Classification of Occupations (International Labour Office 2008, p.11) defines a job as “a set of tasks and duties performed, or meant to be performed, by one person, including for an employer or in self employment,” with an occupation defined as “a set of jobs whose main tasks and duties are characterised by a high degree of similarity.” To have a high level of similarity, there must be a set of practice, processes, and behaviours; in this sense, an occupation can be understood to be a discipline. The above definitions highlight there are specific jobs in each area of HIDDIN that can be found in different health organisations, and therefore the areas in HIDDIN can be defined as occupations. Yet, most of these areas are not defined as occupations in the International Standard Classification of Occupations. Does this make the HIDDIN workforce a profession? Using the neo-Weberian perspective, a profession “is centred on attaining a particular form of formal legal regulation with registers creating bodies of insiders and excluding outsiders” (Saks 2012, p.4). Whilst several HIDDIN areas require a formal qualification for a job, have a formally structured salary classification linked to that qualification, and have national peak bodies requiring the qualification for membership (excluding outsiders), a register of qualified practitioners is not maintained for any HIDDIN area. There is an emerging trend to link health informatics qualifications to clinical registrations (e.g. in the United States and United Kingdom). Therefore, for HIDDIN to become a profession, it first needs to become a recognised discipline, with a clear body of knowledge underpinned and informed by scholarly research and discourses, and the formalisation of ethical practice and procedures. These definitions highlight the infancy of the discipline of HIDDIN. The foundation for HIDDIN to be an occupation is present, and this is explored throughout this book through the examination of competencies, education, accreditation, professional development, certification, impact, and jobs. Yet, the only framework in existence, the ADHA digital profiles (2020), does not recognise HIDDIN as a discrete occupational group with its own norms, knowledgebase, and professional code. The emergence of leadership jobs (such as the Chief Information Office, Chief Clinical Information Officer, Chief Medical Information Officer, Chief Nursing Information Officer, Chief Digital Information Officer or the Chief Information Governance Officer) in specialised digital health areas is the start of an occupational structure, from which professionalisation can be achieved.

The Future: Deepening Insights into HIDDIN Work This book provides a clearer and more comprehensive view than ever before, of the specialised workforce required to manage and govern the health data, information, and knowledge infrastructure now and in the future. It offers a coherent and critical enquiry into a workforce that is essential for healthcare services to function, for care

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providers to practice at the top of their scope/licence, for researchers to generate significant insights, and for care consumers to be empowered participants in health systems, as digital information and communication technologies transform the health sector. It celebrates and champions those working in the HIDDIN workforce. It informs health sector health executives who need to develop and mobilise this workforce. It is a resource for health workforce planners, professional associations and educators who are responsible for setting and upholding practice and performance standards in this workforce. It sets the stage for forward-looking research and reflective practice to deepen how we understand the specialist knowledge and skills that we rely on in the digital health era. Acknowledgements  Our Universities, Auckland, Melbourne, RMIT and Tasmania, have encouraged and supported us in writing this book. Associate Professor Rebecca Grainger at the University of Otago was an especially valued collaborator in work that preceded this book. Our colleagues in academia and in the health sector around the world have been generous and genuine in providing chapters and case studies that are unique contributions to describing the HIDDIN workforce. Our reviewers have been invaluable in providing independent peer reviews. Every individual who took the time to complete the health information workforce census in 2018 and 2021 added depth to our collective understanding of this important workforce. Lastly, we wish to acknowledge and pay our respects to the first health information specialists, that is, the traditional owners of the lands on which we all live and work.

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Jayaraman PP, Forkan AR, Morshed A, Haghighi PD, Kang YB.  Healthcare 4.0: a review of frontiers in digital health. Wiley Interdisciplinary Reviews: Data Mining Knowl Discov. 2020;10(2):e1350. Kalra D, Stroetmann V, Sundgren M, Dupont D, Schlünder I, Thienpont G, Coorevitis P, De Moor G. The European Institute for Innovation through health data. Learn Health Syst. 2016;1(1):e10008. Kluge EHW. Health information professionals in a global eHealth world: ethical and legal arguments for the international certification and accreditation of health information professionals. Int J Medical Informatics. 2017;97:261–5. Kukafka R.  Digital health consumers on the road to the future. J Medical Internet Res. 2019;21(11):e16359. Liu D, Milsom R, Calder N, Harry G, Patel P.  NHS informatics workforce in England: Phase 1 project report. 2019. https://www.hee.nhs.uk/sites/default/files/documents/Informatics%20 Workforce%20Report%202014%20to%202019.pdf. Accessed 28 May 2021. Loukides M. What is data science? The future belongs to the companies and people that turn data into products. O’Reilly Media: Sebastopol, CA; 2011. Martin-Sanchez F, Gray K. Recognition of health informatics in Australian standard classifications for research, occupation and education. Stud Health Technol Informatics. 2014;204:92–6. Mongan D, Farragher L, Long J. Implementation frameworks for use by health workforce planners. Harv Bus Rev. 2018. https://www.hrb.ie/fileadmin/publications_files/Implementation_ frameworks_for_use_by_health_workforce_planners_2018.pdf. Accessed 28 May 2021. Myers B.  What we talk about when we talk about medical librarianship: an analysis of Medical Library Association annual meeting abstracts, 2001–2019. J Medical Libr Assoc: JMLA. 2020;108(3):364. NHS.  Knowledge and library services. 2021. https://www.healthcareers.nhs.uk/explore-­roles/ health-­informatics/roles-­health-­informatics/knowledge-­and-­library-­services. Accessed 28 May 2021. NNLM.  Network of the National Library of Medicine. About NNLM. 2021. https://nnlm.gov/ about. Accessed 28 May 2021. Nonaka I, von Krogh G. Tacit knowledge and knowledge conversion: controversy and advancement in organisational knowledge creation theory. Organ Sci. 2009;20(3):635–52. OECD.  Automation and independent work in a digital economy. 2016. www.oecd.org/employment/emp/Automation-­and-­independent-­work-­in-­a-­digital-­economy-­2016.pdf. Accessed 28 May 2021. Ogoe HA, Asamani JA, Hochheiser H, Douglas GP. Assessing Ghana’s eHealth workforce: implications for planning and training. Human Resour Health. 2018;16(1):1–11. Pereira V, Cooper CL, Chandwani R, Varma A, Tarba SY. Guest editorial: Evaluating and investigating knowledge management practices and ICT in health care: an emerging economies perspective. J Knowl Manag. 2021;25(3):513–24. Peterson HE. The early history of European Federation of Medical Informatics. Acta Informatica Medica. 2014;22(1):16. Saks M. Defining a profession: the role of knowledge and expertise. Professions Professionalism. 2012;2(1):1–10. Shah SMSA, Mahrin MNR.  The trend of big data in workforce frameworks and occupational standards towards an educational intelligent economy. J Techn Educ Train. 2021;13(1):176–84. Smith A, Page D. Public predictions for the future of workforce automation. USA: Pew Research Center; 2016. http://www.pewinternet.org/2016/03/10/public-­predictions-­for-­the-­future-­of-­ workforce-­automation/. Accessed 28 May 2021. Smith PC, Anell A, Busse R, Crivelli L, Healy J, Lindahl AK, Westert G, Kene T. Leadership and governance in seven developed health systems. Health Policy. 2012;106(1):37–49. Snowden A.  Digital health: a framework for healthcare transformation. HIMSS. 2020. https:// www.gs1ca.org/documents/digital_health-­affht.pdf. Accessed 28 May 2021. Sweeney J. Healthcare informatics. Online J Nurs Informatics (OJNI). 2017;21(1). Topol E.  The Topol review. Preparing the healthcare workforce to deliver the digital future. 2019;1–48.

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Trowler P, Saunders M, Bamber V. Tribes and territories in the 21st century. Rethinking the significance of disciplines in higher education. Milton Keynes: Taylor & Francis Group; 2012. Williams R.  Why is it difficult to achieve e-health systems at scale? Inf Commun Soc. 2016;19(4):540–50. WHO. Models and tools for health workforce planning and projections. (Human Resources for Health Observer, 3). Geneva: WHO Press; 2010a. WHO. Workload indicators of staffing need (WISN): a manual (No. WHO/NLM/W76). Geneva: World Health Organization; 2010b. WHO.  Global strategy on human resources for health: workforce 2030. 2016. http://apps.who. int/iris/bitstream/handle/10665/250368/9789241511131-­eng.pdf?sequence=1. Accessed 28 May 2021. WHO. Global strategy on digital health 2020-2025. Geneva: WHO Press; 2021. Zins C. Conceptual approaches for defining data, information, and knowledge. J Am Soc Inf Sci Technol. 2007;58(4):479–93.

Part II

Identity

Chapter 2

Health Information Work: A Scoping Review Cecily Gilbert, Kathleen Gray, and Simone Pritchard

Abstract  The work of managing health data, health information and health knowledge is fundamental in healthcare systems, as increasingly they are transformed by information and communication technologies. However, this work is not acknowledged or understood as commonly as other kinds of work in healthcare, even though it has been described in scholarly writing for five decades. This chapter is a scoping review of literature from the domains of health sciences, health information technology and health information sciences; bibliometric and thematic analyses explore the responsibilities and the contributions of the health information workforce. 284 publications from 1973 to 2018 outline a wide variety of occupational sub-groups, job titles, work roles and skills. The status and prospects of this kind of work are influenced by: external drivers of role changes; definitions of competency requirements; healthcare professions’ needs for general and specialised education regarding new technologies; and fragmented identities within the health information workforce. If specialised professional work is considered essential for healthcare systems to realise the benefits of information and communication technologies, then concerted health workforce planning is needed to consolidate historically disparate health information work practices and to establish a distinctive, accountable workforce that provides the human infrastructure for digital health. Keywords  Competencies · Health workforce · Historical trends · Human resources

C. Gilbert (*) · K. Gray · S. Pritchard Centre for Digital Transformation of Health, The University of Melbourne, Parkville, VIC, Australia e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_2

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Introduction “Historically, the diverse communities working in digital health—including government stakeholders, technologists, clinicians, implementers, network operators, researchers, donors—have lacked a mutually understandable language with which to assess and articulate functionality.” (World Health Organization 2018, p.  2). This incoherence is an indicator of the diffuse status of a whole subsection of the health workforce, a multiplicity of people in specialised roles who together are responsible for the systems for capturing and using health data, health information, and health knowledge, and whom we characterise as HIDDIN—referring to their collective specialisations in Health Informatics, Digital, Data, Information and kNowledge management. Their work directly fuels the provision of health services, assures the quality and safety of care and underpins the translation of research into practice. This workforce may have an increasingly important role as the health sector moves toward greater use of digital technologies. Yet precise data about it is difficult to obtain; the work is largely invisible, ill-defined, unregulated and unmonitored (Gray et  al. 2019). Further, allusions to it often convey little sense of human agents, creating an abstract impression, of unspecified work done by unspecified workers. The HIDDIN acronym is most apt because this work is so poorly recognised or understood, compared to other areas of work in the health sector. “Health information” as the description of a specialist work domain emerged in the twentieth century. Individual professions and occupations, such as medical records managers and medical librarians, arose in the early decades of the 1900s, and developed in parallel, rather than intersecting, streams. Further specialisations, in particular health informatics, emerged from the mid-century onward. More recently the field has become very fluid, due in part to technological changes that enhance collaboration between computing or IT staff and those working with health data or health information. A range of professions now claim expertise in health information work, and position titles and career paths also vary greatly. Moreover, in the current era of digital transformation of health, some people in the health information workforce are confronting issues of their relevance and sustainability in the face of possible workforce structuring. Subsections of the health information workforce have been studied occasionally over the years, for various purposes, in different parts of the world. This chapter takes a holistic approach, inspired by a government workforce planning agency report on the health information workforce (Health Workforce Australia 2013). This chapter is part of a larger research program studying the changing nature and scope of this workforce, starting with a world-first national Health Information Workforce Census in May 2018 (https://www.utas.edu.au/health/projects/hiwcensus), which invited participation by: “…anyone who self-identifies as part of the health information workforce working for/with an organisation that operates in Australia. You are

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part of the workforce if you work (including volunteer or actively seeking) in a role where the primary function is related to developing, maintaining, or governing the systems for the management of health data, health information, or health knowledge” (Butler-Henderson and Gray 2018a, b). This chapter addresses the question: How has the concept of “health information work” emerged and developed within writings about the broader workforces in health care, health information technology and health information sciences? The focus is less on abstract ideas of health information work, and more on accounts of how human agency—the capacity of humans to act, and the way that they do act—is manifested in the performance of work with health information, health data, or health knowledge systems. The objectives are to map published descriptions of an instrumental or professional human role in health information work; and to summarise what has been written about the extent, distribution and nature of such work.

Methods A scoping review methodology was appropriate to this broad research question. Scoping reviews have the goal of “summarising a range of evidence in order to convey the breadth and depth of a field” (Levac et al. 2010, p. 1), for three possible reasons: “…to examine the extent, range and nature of research activities [in a subject]; determine the value of undertaking a full systematic review; or identify research gaps in the extant literature” (Paré et al. 2015, p. 186). The present review has the first and third of these purposes. This review followed the format and stages: (1) identifying the research question; (2) identifying relevant studies; (3) describing study selection criteria; (4) charting the data; (5) collating, summarising and reporting the results (as outlined by Arksey and O’Malley 2005). A completed PRISMA-­ ScR Checklist for the scoping review (as recommended by Tricco et al. 2018) is not included for reasons of space, however all checklist elements are addressed in the structure of this chapter. The protocol for this review was agreed through consultation with team members in the Health Information Census project. Since scoping reviews were ineligible for registration in the PROSPERO database (Centre for Reviews and Dissemination n.d.), this protocol was published as a preprint (Gray and Gilbert 2019). To be included in the review, items had to describe or focus on an instrumental or professional human role in health information, health data or health knowledge work. These roles had to be involved in managing health information, health data or health knowledge, in clinical, academic, government or industry settings. Eligible study types included primary research, case studies, reviews, theoretical or analytical studies, policy or planning articles. Editorials, letters and brief items were not

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included. Descriptions of health information work performed by consumers, patients or carers were not eligible. Non-English items were excluded. The search was conducted in five online databases: Ovid Medline (1946–June 2017), and associated databases (comprising Ovid Medline Daily, Epub ahead of print, In-process and other non-indexed citations); CINAHL Full Text, searched on 28 March 2018; Embase (Elsevier version) searched on 21 March 2018; Applied Social Sciences Index and Abstracts, searched on 22 March 2018; and Library, Information Science and Technology Abstracts, searched on 27 March 2018. Since the review aimed to trace the evolution and development of the health information workforce, no date restrictions were used. The search strategies were drafted by an experienced health information practitioner (CG). After a test search in Ovid Medline and discussion with coresearcher (KG), the strategies were refined and translated to the other bibliographic databases. The primary searches were performed by two information professionals (CG and SP) between June 2017 and March 2018. The Medline search strategy can be found in Appendix. In addition, reference lists of relevant items were scanned to identify any further citations. Two grey literature resources—the CORE repository and Google Scholar—were also searched. Two additional criteria were used in screening potential items in these sources: published by a reputable organisation; and contains empirical data about the health information work (i.e. commentary, editorials or opinion items were not eligible). A final search update was conducted in Ovid Medline and CINAHL Full Text to identify any eligible items published up to December 2018. The final span of years searched was 1946–2018. The search results were exported into the Covidence software (Covidence n.d.) for decisions on selection. The results were de-duplicated in Covidence, then screened by members of the search team. At the outset, both CG and SP reviewed 100 titles and abstracts together, to test consistency of the screening decisions. Following this stage, the two reviewers divided the total set of results and screened allocated items independently. Where judgments disagreed or in the case that items were flagged as “uncertain”, these were resolved by consensus. Figure  2.1 shows the flow of items through the retrieval and screening processes. The key reason for exclusion was that the item did not detail the work or the worker in health information activities. A small number were excluded either because the full texts were not in English or they were duplicate reports of a single item. 284 items were judged as eligible for inclusion in this review and appear in the reference list along with all other items cited in this chapter. Descriptive statistical analysis was used to show bibliometric features such as years of publication, top-ranking subject terms, source journals and prominent authors. Topic analysis was performed by grouping controlled vocabulary items. Deductive thematic analysis was done, based on charting the health information work role characteristics that each item described: identity, responsibilities, functions, knowledge and skills. Open coding was used to identify the main reflections about the work and the workforce that recurred across the dataset.

Records identified through database searching (n = 3,519)

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Additional records identified through other sources (n = 50)

Records after duplicates removed (n = 3,094)

Included

Eligibility

Screening

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2  Health Information Work: A Scoping Review

Records screened (n = 3,094)

Full-text articles assessed for eligibility (n = 299)

Records excluded (n = 2,795)

Full-text articles excluded, (n = 15) Did not discuss human role = 2 Duplicate reports = 6 Not in English = 7

Studies included in systematic review (n = 284)

Fig. 2.1  PRISMA Flow diagram

Results Findings are arranged in four parts: bibliometric analysis (publications over time; publications by author; publication sources); topic analysis (controlled terms used to classify the studies); role analysis (job identities; roles and responsibilities); recurring concerns.

Bibliometric Analysis Publications explicitly about the health information workforce first appeared in 1973, though the number of items for the first two decades (1973–1990) remained low at 9. However, from 1991 the number of publications steadily increased; for

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PUBLICATIONS COUNT

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example, in 1998, 12 items were identified. Substantial growth is evident from the late 1990s onwards, with 60 items (20.9% of the total) appearing in the 1991–2000 decade, 88 items (30.6%) in the 2001–2010 decade, and 127 items (44.7%) since 2011. This was the most productive period, and 2017 with 30 publications (10.6%) was the most productive year. The average annual number of publications (rounded) is 6, or 57 per decade. In contrast, between 2011 and 2015 there were 71 publications, and a further 56 between 2016 and 2018. Figure  2.2 shows the number of retrieved items per publication year. Most of the included studies reported single cases or primary works; there were only three reviews or overviews of literature. 23 authors appeared as lead authors of more than one publication; in total, these authors contributed 64 items as lead authors (22.5%). Thirty-eight journals or serials published more than one article from the dataset; these are listed in Table 2.1. Titles with two or three articles were from a wide variety of domains: human resources in health, medical decision making, implementation science, information systems education, nursing administration and broader health services administration.

Topic Analysis Topic analysis was based on analysis of controlled vocabulary. In compiling bibliographic databases such as Medline and CINAHL, a trained indexer assigns subject descriptors from a controlled vocabulary to each newly received article. MeSH (Medical Subject Headings) is the vocabulary used in Medline. Applying the

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Table 2.1  Journals which published multiple items in the dataset No. of items 26 14 10 9

8 7 6 5 4

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Journal name and date span of items Studies in Health Technology & Informatics 1996–2017 Bulletin of the Medical Library Association 1981–2000 International Journal of Medical Informatics 1998–2017 AMIA Annual Symposium Proceedings 1999–2008 Journal of the American Medical Informatics Association 1997–2018 Journal of the Medical Library Association 2002–2018 Yearbook of Medical Informatics 1998–2017 Health Information & Libraries Journal 2004–2017 Journal of AHIMA 1993–2017 Topics in Health Information Management 1992–2002 Methods of Information in Medicine 1994–2018 Applied Clinical Informatics 2010–2015 Medical Reference Services Quarterly 1998–2013 Health Informatics Journal 2000–2017 Health Information Management Journal 2004–2016 Health Libraries Review 1993–2000 Journal of Information Systems Education 2011–2014 Proceedings of Medinfo: World Congress on Medical & Health Informatics 1974–1995 Academic Medicine 1995–2017 American Journal of Pharmaceutical Education 2016–2017 BMC Medical Informatics & Decision Making 2009–2016 Journal of Hospital Librarianship 2005–2007 Journal of Nursing Administration 2011–2017 Journal of the Canadian Health Libraries Association 2008–2017 Perspectives in Health Information Management 2012–2013 Australian Health Review 2009–2016 Evidence Based Library & Information Practice 2010–2011 Human Resources in Health 2013–2015 Implementation Science 2013–2016 Information Services & Use 2006–2017 Journal of Health Communication 2012 Journal of Healthcare Information Management 2004 Journal of Physical Therapy Education 2004–2010 Journal of Public Health Management & Practice 2015–2016 Journal of the American Society for Information Science 1987–1988 Nursing Administration Quarterly 1997–2007 Online Journal of Public Health Informatics 2012–2014 Telemedicine Journal & E-Health 2012

vocabulary terms enables uniform indexing by subject, rather than relying only on the keywords proposed by the authors. It also links the concepts in the individual record to other records in the database with the same terms, and thus suggests a shared knowledge relationship with those records. Filtering and grouping all the MeSH terms applied this way to the items included in the dataset showed that more than 500 subject descriptors or designated keywords were used. Of these, 32 were applied 10 or more times; these are listed in Table 2.2. They cover domains such as

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professions (e.g. Medical Informatics, Professional Role), education and curriculum, technological concepts (e.g. Electronic Health Records) and managerial terms (Forecasting, Program Evaluation, Trends). The term Health Personnel—defined in MeSH as “Men and women working in the provision of health services, whether as individual practitioners or employees of health institutions and programs, whether or not professionally trained, and whether or not subject to public regulation”— appears in this group of frequently applied terms, as does one named occupation—Librarians. The high count for the term “Medical Informatics” can be partly attributed to the gradual evolution of informatics descriptors in the MeSH vocabulary. “Medical Informatics” was introduced in 1987, whereas descriptors for other informatics specialties only became available more than 15 years later: Public Health Informatics Table 2.2 MeSH subject descriptors assigned to ten or more items in the dataset

No. of items 65 60 55 50 29 28 25 22 21 20 19 18 17 16 15 14 13

12

11 10

Subject descriptor Medical Informatics Education Organisation and administration Curriculum Standards Methods Libraries, Medical Professional Competence; Trends Information Services Delivery of Health Care Medical Records, Computerised Surveys and Questionnaires Computer Communication Networks Internet; Professional Role Library Science Information Systems; Statistics and Numerical Data Female; Information Management; Leadership; Librarians; Male Adult; Electronic Health Records; Program Evaluation Health Personnel; Information Storage and Retrieval Education, Medical; Forecasting; Library Services

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in 2003, Nursing Informatics and Dental Informatics in 2005, and Consumer Health Informatics in 2018. The broader (parent) term “Informatics” was also introduced in 2005, defined as “The field of information science concerned with the analysis and dissemination of data through the application of computers”; up to 2004, this concept was indexed using the term “Information Systems” (National Library of Medicine 2020).

Role Analysis The health information occupation identities reported in the publications were grouped into nine broad categories. The “years” shown for each job title in Fig. 2.3 provide an insight into the timeline of the health information work occupations. The occupational groups identified in the earliest years are health information technologists, health information managers (HIMs) and medical or health librarians.

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Bioinformatician / biomedical informatician Clinical informatician Community education Community health worker Consumer health informatician Consumer health librarian Digital health advisor Health computing IT Health data librarian Health informatician Information doctor Medical informatician Health information manager Health information professional Health librarian Information prescriber Information specialist Information therapist Informationist IAIMS researcher Knowledge manager / broker Nurse informatician Patient educator Pathology informatician Pharmacy informatician Public health informatician Research health librarian Research informatician Technology & information science educator

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Reference to clinical participation in this work by medical practitioners also began early and frequently has been linked with advocating informatics education for doctors-­in-training (Gonzalo et al. 2017). This trend was followed a decade later, in greater numbers of papers, in the nursing literature. Other occupations, not mentioned often until the twenty-first century, are in the allied health professions (pharmacists and physiotherapists) and in knowledge management. Knowledge work in health has undergone a resurgence, with the recent emergence of knowledge broker and knowledge translator roles, e.g. Glegg and Hoens (2016); Hopkins (2017). A small range of occupations were mentioned only once, such as health administrator, community health worker, researcher and student. A handful of articles described or advocated for “dual identities” that combined both a health occupation and an information occupation. Examples of these dual identities are: medical practitioner + informatician (Zimmerman et al. 1988), health librarian + informatician (Frisse et al. 1995), nurse + informatician (Bakken et al. 2004) and health information manager + informatician (Bloomrosen and Berner 2017). These illustrate the way that the work of managing information content, information retrieval and information technology in health may form “a tightly woven interdisciplinary braid” (McKnight 2005, p. 13). More than 60 distinct job titles were used in the publications. A majority matched the titles used in the identity categories listed above, while the less frequent job titles predominantly reflect contemporary or emerging terms. Some of these are creative and intriguing in the way they try to express the scope of work. Examples include decision support manpower and digital information roles (Ash et al. 2015; Dimitrov 2016), community health staff trained in “last-mile” health data collection responsibilities (Bram et al. 2015), a clinician responsible for health data security (Gaunt and Roger-France 1996), information therapist (Mettler and Kemper 2003) and health literacy expert (Vellar et al. 2016). For this analysis the job titles were grouped into 25 categories; Fig. 2.3 displays the job titles matched with their years of usage. Many articles present identity-specific perspectives on the roles, responsibilities, and functions of the nominated profession. The extracts below show examples over the years, for the top four identities: health informatician; health librarian; health information manager; health information technologist. Health informaticians’ perspectives on the work are exemplified here. Greenes and Lorenzi (1998) argued the strategic agenda for health and biomedical informatics work is in incorporating the increasing role of information technology. Sable et  al. (2000) identified medical informaticians as interdisciplinary workers who understand clinical medicine, healthcare management and information technology. Kushniruk et al. (2006) observed that informaticians have roles in health services management, development, research and evaluation. McLane and Turley (2011) compared roles of informaticians, project managers and IT professionals in healthcare organisations; speaking from a nursing informatics perspective, they maintained that all three groups ensure the health environment uses IT effectively, with information and knowledge management as core values. Mac McCullough and Goodin (2014) observed that the informatics capacity of staff in local public health departments directly influences how much use is made of the informatics

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functionality in the health informatics systems. Wholey et al. (2018) asserted that the primary function of public health informatics is to use informatics to improve the health of populations. Khan (2018) gave an example in which informaticians provided “clinical leadership” in the modernisation of the military health system to integrate military-civilian health care delivery. This report also described education of the HI workforce in the U.S.  Defense Health Agency. Valenta et  al. (2018) described health informatics as an emerging profession with a range of subdisciplines, such as bioinformatics, translational, clinical, public health, consumer health and clinical research informatics. Health librarians’ perspectives: Goffman (1981) advocated for an international health information libraries network and contemplated the personnel, financial and technological requirements for it to be efficient and successful. Bradley (1996) analysed changes in health information and HI work, noting that multiple concepts of information exist, and these depend heavily on different disciplines and professions; this author argued that health librarians’ specialty work is relevant to the electronic environment, and professionals in the health library and information science area should redefine and communicate their place in it. Deardorff et al. (2017) described a research project where health librarians tested “authoritative” answers to a real-­ world corpus of questions submitted by patients and consumers, using the MedlinePlus interface, and saw lessons for evaluating question-answering services and tools. Federer (2018) defined and scoped the knowledge used by librarians specialising in health data librarianship services, and the training and competencies required in these roles. Health information managers’ perspectives: Abdelhak (1980) forecast that the health record practitioner would have an expanded role as a health information specialist. Brunner (1992) argued that medical record professionals need to adapt to the new role of HIMs. Fenton et al. (2017) pointed out that HIM work needs to include roles such as data scientists and data stewards to ensure sound management of data analytics and digital transformation. Butler-Henderson (2017) highlighted updated work practices and educational reform as essential to ensure the on-going inclusion of HIMs in data-driven health systems; this author proposed embedded roles for HIMs in clinical, business, IT and health funder segments of the health industry, and nominated information governance and data management as a strategic focus for this work. Health Information Technologists’ perspectives: Clarke (1997) stated that the information specialist’s role was to combine clinical expertise with knowledge of current computer technology with the key tasks being to manage information and health care data. Denham et al. (2013) focused on the work of ensuring health IT safety and argued for the use of proven post-deployment performance assessment of EHR systems in healthcare organisations. Skillman et al. (2015) noted that a trained health IT workforce was required in rural primary care settings, but not likely to be available due to infrastructure weaknesses and workforce barriers. Ammenwerth et al. (2017) described the use of real-world “sub-information systems” for practical exercises in the health information systems curriculum taught in Europe since 2001. Cresswell et al. (2017) argued that expert and experienced health IT personnel were required to optimise the implementation of complex IT systems.

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A number of authors gave examples of blended or interdisciplinary roles, such as the following cases in the community and patient education context: Martin et al. (1997) outlined a project involving librarians, primary care and outreach workers, translators and IT staff to deliver health information services and technologies for community AIDS training and education. Oster and Thomas (1998) described teaching “health information internet” classes in a community setting, with providers comprising a medical librarian, a community health activist and a physician with expertise in medical informatics. Greenberg et al. (2004) outlined the roles of consumers, quality experts, search engine experts, researchers, healthcare providers, informatics specialists and others to improve the results of consumers’ internet searches for health information. Alamantariotou and Zisi (2010) presented an overview of methods and tools to present health information to consumers, and they noted the interdisciplinary nature of consumer health informatics work. Ndira et al. (2014) described an “information intervention”—a malaria prevention health information package—done in multiple villages by a team of medical students, community health workers and technical experts.

Recurring Concerns Four main themes preoccupy authors who have written about health information workers, based on an inductive analysis of publications in the dataset: role changes brought about by external drivers; competencies and new curriculum required for specialised information workers to remain relevant; informatics education for the wider health workforce (in undergraduate curricula, and professional development for existing workforce); and cooperation and combined strengths among existing professions and occupations (also for informaticians and HIMs). These themes are unfolded over the years through examples below. Authors frequently referred to rapid change, external reform and technological innovation as inexorable drivers for change in roles and competencies. Scherrer (2004) reported on new roles adopted by health librarians in the previous decade, and the changing balance of functions performed. This was accompanied by a new job title: academic health information professional. Newly adopted activities included closer faculty liaison, web site design and marketing or promotion using new technologies. Tarver et al. (2013) described how a university health service’s clinics used librarians to guide consumers accessing the facility’s patient portal, and the MedlinePlus consumer health resource which was contextually linked to it. Baird and Nowak (2014) proposed that primary care practices should extend the concept of “patient portals” by coordinating patients’ health data from multiple sources into a digital health information hub, with the added benefit that staff in the practice could provide expert advice and encourage patient engagement. Abdelhak et al. (2016) noted redefined roles for HIM and HI, in the context of a continuous Learning Health system. Butler (2016) suggested that HIM required

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updated competencies to reflect themes such as informatics, information governance, as well as new academic competencies from AHIMA. Vellar et al. (2016) described an Australian health service’s partnership with consumers to address health literacy, aiming to improve the health experience and health outcomes of consumers. Lake (2018) described the librarians’ role in offering a consumer medical apps service in a Utah health system. Their expertise was used in digital health technology selection and evaluation to advise both health care professionals and consumers. With regard to competencies and new curriculum required to remain relevant, in nursing informatics, Bakken et al. (2004) described enhanced informatics competencies and linked this with the broader focus on information technology for safe and effective health care. Further cases are provided by Fetter (2009) citing the TIGER (Technology Informatics Guiding Education Reform) project for the IT competencies needed in the implementation of EHRs, and Yen et  al. (2017) who reported the development of a nursing informatics competency assessment aimed at nursing administrators. Murphy and Goossen (2017) described nursing informatics competencies needed for “connected health”, highlighting roles including nursing informatics specialists. In health information management, Johns (1995) focused on the leadership qualities that HIM professionals required to manage changes in health care, such as systems thinking and team learning. Butler (2017) outlined skills that HIM professionals needed to practice in the twenty-first century; these included information governance, informatics, data analytics, leadership and project management. Similar competencies were identified by Grzybowski and Orlova (2017), with the addition of soft skills such as ethics, leadership, teamwork and advocacy. Public health informatics education was examined by Joshi and Perin (2012). Their review concluded there was a need for online PHI training programs that could be accessed by professionals worldwide. Drezner et al. (2016) described the skills used by the informatics workforce employed at local public health departments in the USA including: data extraction from information systems, data analytics using statistical programs, GIS systems and website content maintenance. Tremblay et al. (2016) detailed a master’s level cross-disciplinary informatics program that added business analysis topics to other skills in health informatics, information systems and data analytics. Several authors highlighted knowledge-based work. Haynes (1998) explained the use of evidence-based informatics, which combines health informatics with best evidence retrieval, summarising, disseminating and applying to improve the transfer of knowledge into health care practice. Haux (2002) described the need for health care professionals well-trained in medical informatics systems to apply medical knowledge resources in patient care. The role of “research informatician lead” was described by Embi et al. (2013), to oversee design, deployment and use of a variety of information resources to advance research and contribute to discoveries and evidence generation. The concept of a specialist health information workforce exists in relation to a general health workforce, and the distribution of expertise between the two is a

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perennial balancing act. Zimmerman et al. (1988) discussed the ambiguous and evolving field of “medical informatics”, and its place in education and training of health care professionals, in an information-intensive environment. They argued that health care professionals should retain responsibility for medical and patient information; if this is ceded “…they lose control over the quality of care delivered to patients” (p. 139). On this basis, they recommended the introduction of medical informatics training in the curricula for health science professions. The example they cited was the informatics education included in dentistry, nursing and other health professional programs at the University of Maryland at Baltimore. Terry et al. (2008) synthesised lessons from implementing EHR systems in primary care. These included a need to establish the computer literacy of intended users and to address their expectations and willingness to accept the EHR system, in part by use of a leader or champion to assist. Triola et al. (2010) urged the inclusion of core biomedical informatics competencies in medical school curricula, with emphasis on EHR systems, safety and evidence-based care. Ledikwe et al. (2013) described information skills training for health monitoring and evaluation officers in Botswana. The program resulted in improved skills in computer literacy, checking data validity, implementing data quality procedures, using data to support program planning, proposing indicators, and writing monitoring and evaluation reports. Whittaker et al. (2015) documented consensus findings on HIS competencies needed by general health workers, following a consultation process with HIS experts. Their framework enabled improved workforce capability training, particularly in low resource settings. Schleder Goncalves et al. (2015) reported observations on the computing and information literacy of nurses working in hospitals and public health units in southern Brazil. The majority of those surveyed were assessed to have novice-level skills in these areas, despite having some IT exposure in both professional and personal life activities. The authors described educational programs to address these areas in nursing education. Gonzalo et al. (2017) proposed that medical curricula should include health systems science content, to align the education with the needs of the systems currently implemented in health services. Cooperation and combined strengths among existing professions and occupations have been proposed from many angles. In 2000, a new role, dubbed the informationist, was proposed (Davidoff and Florance 2000). It was envisioned as a knowledge role that would bridge the gap between the findings in published medical literature and applying them in clinical practice. The authors proposed this role would research clinical questions efficiently and effectively using the health information resources and deliver a synopsis to the clinician at the point of care. Davidoff and Florance proposed that the informationist would be educated in both clinical and information science skills. They suggested a multidisciplinary curriculum drawing on input from many, including medical informaticians. They noted “potential turf issues in relation to library science, medical informatics, and clinical medicine” (p. 998). A response from medical informatics came via Hersh (2002, p. 76),

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who argued that “…individuals do not have to be librarians to become informationists”. This view was based on his belief that medical informatics had “fuzzy boundaries”; it did not have a common set of skills nor a common curriculum. He agreed that informationist training could be gained through a conventional library science degree complemented by clinical experience. Alternatively, he proposed training in medical informatics would be another pathway to train the informationist. Hersh argued that both fields should “work together to identify the appropriate skills for informationists and ensure that individuals of either training experience can apply and teach them effectively” (p. 79). Perry et al. (2005) offered a similar perspective on the disciplines of medical informatics and health sciences librarianship. They noted that “boundaries are disappearing between the sources and types of, and uses for, health information managed by informaticians and librarians. Definitions of the professional domains of each have been impacted by these changes in information…Professionals in these disciplines are increasingly functioning collaboratively as ‘boundary spanners’, uniting technology with health care delivery” (p.  199). Dalrymple and Roderer (2010) noted the expansion of informatics courses in the USA during that decade. Based on their view that health informatics and health information science are converging, they examined education for health information professionals. They argued that “schools of information science have an important role to play…in educating health informationists” (p. 45), alongside the other disciplines which have established themselves in the informatics sector. Similarly, Gibson et  al. (2015) suggested that boundaries between health informatics and health information management were disappearing, in response to changes in health care delivery and information technologies. They concluded with recommendations to strengthen the roles of each profession, and collaboration between them. Bloomrosen and Berner (2017) observed that the discipline of health information management “increasingly is becoming allied with the field of medical and health informatics in that both disciplines have interests in common” (p. 81). They noted that conventional HIM expertise in coding, privacy and security is also essential in contemporary electronic information exchange, and information governance is critical.

Discussion Two hundred and eighty-four peer-reviewed papers from the past five decades specifically address the question of what people do, in health information work. Synthesis of this body of literature gives a disparate picture: a workforce that is like a large family whose members barely stay in touch, with many different voices, siloed sub-groups, and little appreciation of their interdependence. This characterisation is consistent with observations from many quarters, exemplified by the World Health Organization observation that opened this chapter.

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The major strength of this study is to go beyond studying or surveying individual workforce segments—just HIMs, or just health librarians, for example—and to show how much irregularity and overlap there is in the definition and expectation of professionalism in this part of the health workforce. The contrast with other areas of the health workforce is plain, as echoed elsewhere, for example in Scott et al. (2018, p. 127): “Data science in healthcare is subject to strong regulatory and ethical controls, minimum educational qualifications, well-established methodologies, mandatory professional accreditation and evidence-based independent scrutiny. By contrast, ‘Digital Health’ has minimal substantive regulation or ethical foundation, no specified educational requirements, weak methodologies, a contested evidence base and negligible peer scrutiny.” The findings reported here have the potential to affect individual and community views. There is a broader base and longer tradition of knowledge in this kind of work than many current or intending practitioners or employers realise. In the competition for new and emerging roles in the era of digital health, individuals may not be recognised for the knowledge and skills they have, or the scholarly tradition behind them. However, the literature does not show industry or professional associations or health bureaucracies advocating to strengthen recognition of the work. Thus changes that improve this situation for people in this workforce are not certain, unless their collective voice becomes louder and clearer. Several ways forward are possible. An altogether more collaborative approach to the work may be needed to encompass emerging needs for knowledge and skills, as posited by McKnight (2005). For example, Rajamani et al. (2015) suggest collaborative practice, interprofessional education and health informatics expertise to facilitate a learning health system. Abdelhak et al. (2016, p. S8) proposes “leveraging innovative health data management policies, practices, and developing a collaborative, competent workforce”. Alternatively it is possible that one workforce sub-­group will outcompete others to become the most distinctive and credible organising force for digital health information work. Another prospect is that a more established group within the health workforce (for example, health service managers) “takes over” the claim to expertise. This review of the literature is not without limitations. The scoping review process necessarily used very broad search terms, generating a large number of retrieved items in the initial stage of research. Screening the abstracts required a tight focus; articles with scant information were rejected even though they may have made some valid contributions. In a scoping review, quality assessments of the included items are optional (Tricco et al. 2018, item 12); in this review, no quality assessment or critical appraisal was performed. The authors of the current review were unable to include results from databases specific to the engineering and computer science disciplines. The broad search strategies retrieved tens of thousands of items from these resources, which exceeded the team’s capacity for screening and selection. In this respect, it has not been possible to conform to the original protocol published in 2019, and this may mean that computer science and information technology aspects that characterise the health information workforce are under-represented in this

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study. The dataset was limited to English language publications, nevertheless the work originates from many different regions of the world and the findings are widely applicable.

Conclusion This chapter has mapped the published international literature discussing an instrumental or professional human role in health information work over five decades. This review is unparalleled in its scope and depth; to our knowledge, it offers the most comprehensive overview conducted to date, of scholarly understanding about this segment of the health workforce. The review systematically highlights the human agency in such work and comprises a rich resource for workforce planning. This review illustrates the extent, distribution and nature of the specialised work done by people trained in a variety of disciplines. Their work is described using an assortment of terms for roles and functions, and for the underlying competencies required to discharge these responsibly. It is hard to imagine any other group of specialists in the health workforce that is accountable for such crucial outcomes as health data, information and knowledge are expected to deliver in the twenty-first century, and yet is so diffuse and disjointed. In the era of digital health, an apparently mission-critical part of the health workforce is ill-defined and fragmented. The review has identified that the absence of a coherent professional identity or a pathway toward professionalisation—including a uniformly defined scope of practice, code of conduct, training and continuing education framework or system of licensing to practice—is a major gap in this part of the health workforce. This gap creates an uneven balance of power between producers and consumers of data, information and knowledge products and services in the health sector. The review has found relatively little empirical research into health information work or the workforce. Isolated project reports exist but do not appear to build toward any major lasting workforce reviews or reforms. Health services research is needed, to analyse how this work contributes to health system performance, and the relative impacts of different approaches to planning and management of the workforce. This review has highlighted the distance that this workforce still needs to travel if its members wish to play visible, vital roles in realising the anticipated benefits of digital information and communication technologies in health. It is a truism that investment in health information and communication technologies is rarely matched with investment in creating a skilled health information workforce. In this case, the investment required is not merely money, but ideas. Innovative thinking is needed, to construct a consolidated identity and community that can be recognised and understood alongside other professions in the health workforce. Notes  This work received funding support from The University of Melbourne. We acknowledge Nadine Ogonek and Patrick Condron for their assistance with elements of the data analysis.

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Appendix Medline search strategy (search performed 14 June 2017) 1. “health information”.ab,ti. 2. “healthcare information”.ab,ti. 3. “health care information”.ab,ti. 4. “health-care information”.ab,ti. 5. informatics.ab,ti. 6. management.ab,ti 7. “technolog*”.ab,ti 8. (library or libraries).ab,ti. 9. systems.ab,ti. 10. digital.ab,ti. 11. 5 and 6 12. 5 and 7 13. 5 and 8 14. 5 and 9 15. 5 and 10 16. 6 and 7 17. 6 and 8 18. 6 and 9 19. 6 and 10 20. 7 and 8 21. 7 and 9 22. 7 and 10 23. 8 and 9 24. 8 and 10 25. 9 and 10 26. 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 27. (work or worker or workers or workforce).ab,ti. 28. (profession or professional or professionals or professions).ab,ti. 29. (role or roles).ab,ti. 30. (staff or staffing).ab,ti. 31. (expert or experts or expertise).ab,ti. 32. (specialist or specialists or specialized or specialised or specialisation* or specialization*).ab,ti. 33. (leader or leaders or leadership).ab,ti. 34. (champion or champions or championing).ab,ti. 35. (manager or managers).ab,ti. 36. (“change agent” or “change agents”).ab,ti. 37. 27 or 28 or 29 or 30 or 31 or 32 or 33 or 34 or 35 or 36 38. 1 or 2 or 3 or 4 39. 26 and 38 40. 37 and 39 (1782 items)

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Chapter 3

The Socio-technical Foundations of Health Information Work Carey A. Mather and Sue Whetton

Abstract  Socio-technical values, principles, and theories are foundational knowledge for health information professionals. These emphasise that people who work with technology are augmented by it, not subordinate to it. Applying them can contribute to more effective design, development, implementation, management, and use of digital health and information systems. This chapter briefly describes the origins and underlying values of the socio-technical approach. It then discusses theories and key concepts. The second section demonstrates the relevance of socio-­ technical design to the health services in general and health information professionals, in particular. Examples from research and case studies are used to demonstrate how this workforce can apply socio-technical values and principles. Keywords  Design · Socio-technical · Systems theory · Technocentrism · Humanism

Origins, Underlying Values and Principles Socio-technical thinking is underpinned by humanistic values and principles. A socio-technical system is ‘the synergistic combination of humans, machines, environments, work activities and organisational structures and processes that comprise a given enterprise’ (Mumford 2006, p.317). This conceptualisation primarily embraces complex systems in which many humans collaborate towards a common

C. A. Mather (*) · S. Whetton College of Health and Medicine, University of Tasmania, Launceston, TAS, Australia e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_3

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goal. Social refers to individual workers and teams, and needs for coordination, control, and boundary management, while technical includes not only equipment, machines, tools, and technology but also the work organisation. The term socio-technical was used in the mid-twentieth century by researchers at the Tavistock Institute of Human Relations in London (Whetton 2005). Established in 1946, this Institute applied the social sciences to analysing issues and problems in contemporary society. One focus of their research was industry and commerce. Tavistock researchers expressed concerns about emerging scientific management practices being increasingly adopted in offices and factories in modern industrial society. They argued that these practices were having a de-humanising effect on workers. They felt there was a need to counter this trend, ensuring a more humane, or human-centred, and productive organisation of work in the modern workplace (Whetton 2005). The term socio-technical encapsulated an approach for exploring these workplace issues and trends. Tavistock researchers argued that emerging scientific management practices were prioritising the technical sub-system. They were critical of this trend arguing that the rights and needs of the employee must be given as high a priority as those of the non-human elements of the system. The term socio-­ technical was intended to emphasise that both the social and technical systems are of equal importance, and that employees are complementary to technology, not subordinate to it. Socio-technical conceptualisations of the system also emphasised that the two sub-systems were interdependent and were impacted by environmental factors (Whetton 2005; Sawyer and Jarrahi 2014; Adaba and Kebebew 2018). A primary objective of socio-technical researchers was to improve the overall quality of working life through redesigning work practices and workplace technologies. The role of employees was emphasised, with researchers arguing that employees who were to be involved in new systems should be given a voice in the design process of that system. Workers should be encouraged to explore how the new system could improve the quality of their work (Mumford 2006). Sawyer and Jarrahi (2014, p.8) argued this socio-technical approach was, ‘a rebellion against the evolution of work-design practices of the time that had adopted an instrumental view of work, workers, and the workforce’. Baxter and Sommerville (2011) further suggested that this implied challenge to existing work practices may impact on the success or otherwise, of attempts to implement socio-technical principles. Mumford (2006), a leader in the field of socio-technical analysis within information systems identified strong uptake of the approach in the 1960s and 1970s, particularly in Britain and Scandinavia. She suggested that application of the principles during this period resulted in improved working practices and agreements between workers and management. However, Mumford found less evidence of the use of socio-technical analyses in the 1980s and 1990s, when the economic focus on streamlining production, downsizing, and cost reduction was less open to discussions of worker conditions and work satisfaction (Mumford 2006). The initial focus of socio-technical design was on assembly line working conditions, and by the end of the twentieth century it was being applied to a wide range of working environments, including the design of computing and information systems in manufacturing, commerce, and health sectors. In addition, while the initial

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focus was on employees as end-users, advancements in technology have seen the focus expand to include clients, customers, and consumers as end-users. It is increasingly expected that health services include consumers as co-designers in developing information systems and applications which they may be expected to use. Attempting to implement socio-technical design without acknowledging its humanistic values and principles diminishes planning, development, and implementation of effective, safe, and appropriate health information management systems and applications (Thomassen et al. 2017; Leitch and Warren 2010).

Theories and Concepts for Design and Analysis Systems theories, which explore and explain systems, their component parts, functions, and goals, are integral to socio-technical thinking. In overview, a system is comprised of a set of elements, either natural or human-made, that are interrelated and interdependent. Systems can include sub-systems, which are themselves systems. For example, a healthcare system may include hospitals, nursing homes, and community centres, which are all sub-systems of it, yet they are each systems in their own right as well. Systems exist to fulfil a purpose which is expressed in terms of goals and objectives. It is possible for a system to have more than one purpose. For example, the purpose of a health information system may be to provide inter-­ departmental communication for patient treatment, however, it may also be used to aggregate data to facilitate decision-making and research. Individual system components combine to cooperate in achieving goals. Systems theory has long been used by biomedical science as a means of explaining and understanding the entities and processes of healthcare—the physiology of the human body is discussed in terms of skeletal, muscular, or nervous systems and so on. Health information system researchers and practitioners adopt a systems approach to assist in understanding and managing the flow of health information. General systems theory was first formulated in the 1930s and 1940s within the biological and physical sciences (Whetton 2005). Until this time, the approach to understanding complex organisms such as the human body was to examine individual component parts. Each system within the human body would be isolated and the components examined. This approach is reflected in the structuring of biomedical knowledge, which was typically organised into specialities and sub-specialities. General systems theory focuses on systems as a whole entity, arguing that complex organisms are better understood by studying not only individual components, but also the ways individual parts interact and affect each other (Yurtseven and Buchanan 2013). Since it was first espoused, systems theory has been utilised by many disciplines, including medicine, engineering, sociology, computing, information, and management systems. The approach is based on the premise that individual parts of a system cannot do what the system as a whole can do. Rather, while each individual part may perform particular activities, it is the combination of these parts into a unified whole that makes a system. Once combined, the parts of the system interrelate in a structured way that

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differs from the activities of individual components. Socio-technical systems theory builds upon general systems theory. Systems theory is part of the specialised knowledge and skills of the HIDDIN workforce. HIDDIN professionals are participants in complex health systems, and much of their work positions them to ensure that the component parts within health systems interact effectively and efficiently. Socio-technical systems are perceived as comprising social and technical sub-­ systems with the component parts of each sub-system being interlinked and interacting. The social system comprises the relationships between people, their values and behavioural styles, and formal and informal power structures, while the technical system comprises technology, processes, procedures, and physical arrangements. The term socio-technical emphasises the interrelatedness of these systems and the need to consider them conjointly. It is also necessary to consider the environment within which the socio-technical system is embedded. Interrelatedness of the social and technical sub-systems means that changing the elements in one may affect the other sub-system or the whole system. Therefore, effective implementation of new practices within organisations requires a consideration of both sub-­ systems and the way they interact. Interactions are thus a key focus in socio-technical systems analysis. The relevance and interaction of both sub-systems is demonstrated in Mather and Cummings (2019), in their digital professionalism model (4E3P) and matrix for assessing organisational readiness of capability of digital technology use by nurses. They highlighted the interconnectedness of the social/human sub-system (engagement, education) and technical sub-system (equipment, electronic access). They found that when any one of these four elements (4Es) of the two sub-systems are hindered, the opportunity for development of digital professionalism by health professionals is at risk. In particular, they noted that even when health professionals are receptive and prepared to use digital health technology, nevertheless if technical factors are unavailable, human or social elements cannot be deployed and capability in digitally professional behaviour cannot develop. Their matrix highlights that recognising interconnectedness between technical and social elements is imperative to enable implementation of technical systems into healthcare environments. Interactions between the socio-technical system and the external environment within which it is situated are also important to analyse, to achieve effective design and implementation of systems. Thus, the concept of open systems is important (Von Betalanffy 1950; Katz and Kahn 1966). Systems are embedded in wider environments, and separated from these environments by boundaries. Boundaries may be impermeable, denoting a closed system that allows no interaction with the surrounding environment—there are very few closed systems. Rather, most systems are open, having semi-permeable boundaries that allow interaction with their environment. Open systems theory argues that to fully understand the way systems operate, it is necessary to analyse the broader context. The key features of open systems are inputs, processes, and outputs. The system accepts inputs from the environment, processes them in some way, and returns them to the environment. Where a number of systems are arranged as a series, the output of one becomes the input for another. For example, patients enter a hospital (inputs), are treated (processes), and leave the hospital (outputs). Health information systems are generally open systems. The journey of the patient through hospital departments is recorded and documented as

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an electronic health record within the system. This information can be retrieved as required. It can also be passed across the permeable system boundary and shared with the patient’s general practitioner outside the hospital. Open systems exist in, and interact with, dynamic environments. Open systems need to respond to changes in the external environment if they are to continue to achieve their goals. If open systems cannot adapt, they become non-functional. Therefore, systems are in a constant state of transformation or adjustment to changes in the environment. Contemporary systems increasingly comprise a complex interaction between humans, technologies, and wider environmental aspects of work systems—not merely the immediate local environments within which the system is located. Socio-technical systems at the local level will be impacted by broader economic, political, cultural, and technical systems, and vice versa. Today, a socio-­ technical system that is embedded in one department of a hospital will need to interact with other departments and sections in the hospital. It will also need to interact with the external environment, which may include the hospital’s funding bodies and government regulators. This interaction logically extends to the society within which the hospital is located and, in today’s increasingly interconnected world, to global health and the global political economy. Although socio-technical theory and principles have been applied with varying levels of success since the 1940s, there is no consistent, widely adopted method for applying them. An early method was Mumford’s Effective Technical and Human Implementation of Computer Systems (ETHICS) which emphasised user participation in the systems design process (Mumford 2006). Initially well accepted, particularly in the United States of America when Mumford worked directly with organisations (Land et al. 1979), the method declined in popularity over several years (Baxter and Sommerville 2011). Other researchers have variously reviewed and developed approaches to socio-technical design, but have concluded that specific details are lacking, about how to implement it in practice (Baxter and Sommerville 2011; Yurtseven and Buchanan 2013; Sittig and Singh 2011; Hughes et al. 2017). Thus, Baxter and Sommerville (2011) suggest that socio-technical system design methods mostly provide advice for sympathetic systems designers rather than detailed notations and a process that should be followed. Similarly, Hughes and colleagues suggest that ‘although the socio-technical systems approach to design is well recognised and supported, it is also acknowledged that realising the approach in practice can be challenging’ (2017, p.1320). These comments echo Mumford’s (2006) observation that ‘socio-technical design methods are more akin to philosophies than the types of design methods that are usually associated with systems engineering’ (cited by Baxter and Sommerville 2011, p.5).

Theory-Based Analysis of Social and Technical Interchange Socio-technical principles are reflected in several theories and models that have been applied to analyse work settings: sociomateriality, actor network theory, the technology acceptance model, and the unified theory of acceptance and use of technology are outlined here.

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Sociomateriality theorises the complex relationship, or entanglement, between the social and the material that shapes everyday interactions. In health service organisations, ‘the social’ could refer to the relationships, values and norms, roles, and power structures—like the social sub-system in Tavistock-influenced socio-­ technical theory. The concept of ‘the material’ or materiality refers to technology— such as the diagnostic machines, intensive care monitoring systems, electronic records, or mobile communication devices that are used within healthcare organisations. Sociomateriality argues that social practices are intrinsically connected and intertwined with material artefacts and that while they may be theoretically separate, in practice they are inseparable. Technology (material) is designed and developed within the specific values, roles, and practices of organisations (social). A digital clinical information system needs to be consistent with the relationships, values and norms, roles, and power structures of the healthcare environment into which it is introduced; thus, the social influences the material. At the same time, technology is designed with certain capabilities and functions which impose themselves on social practices; while needing to conform to organisational roles and practices (the social), technology (material) shapes what is possible, or expected. For example, digital systems require reports to be typed directly into an electronic data entry form, which may take longer than filling out paper forms, so staff make briefer notes. Over time, staff normalise the expectation that reports should be concise, and when systems are updated, they request that data entry be further modified to ensure even greater conciseness. Sociomateriality allows researchers to study the social and the material simultaneously, and this enriches the evidence about digital work practices and HIDDIN professionals’ knowledge base (Leonardi 2013; Orlikowski and Scott 2016; Orlikowski and Baroudi 1991). Actor Network Theory (ANT) argues that the social and natural worlds exist in constantly shifting networks of relationships. These networks comprise and are constructed through negotiations between human and non-human elements. These elements are referred to as actants. ANT regards non-human actants—objects, ideas, processes, and other relevant factors—as important as human actants, in creating networks. A network forms as actants become closely linked with one another to achieve particular social situations, processes, or outcomes. Thus, in health organisations, technology and human actants merge to create an information system. In doing so, the actants become intertwined, so that it becomes difficult to differentiate a computer program’s technical aspects from the influence exerted by the socio-­ cultural background of the software development team (Cusumano and Selby 1997; Sahay 1997; Callon 1986). From an ANT perspective networks that form are not static and predictable. Interactions that occur cannot be accurately predicted from moment to moment when a digital clinical information system is introduced into a hospital department. Networks evolve as actants interact. ANT theory helps HIDDIN professionals to be aware of active roles of both human and non-human elements in digital health (Cresswell et al. 2010; MacLeod et al. 2019). The Technology Acceptance Model (TAM) aims to better understand why users accept or reject a given technical system, and how user acceptance can be improved through design of technical systems (Lee et  al. 2003). The model posits that an

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individual’s adoption behaviour is directly influenced by his/her attitude towards technology. Two key attitudinal factors are identified. The first is perceived usefulness—the person’s expectation that the technical system will enhance their job performance. The second is perceived ease-of-use—the perception of how user-friendly and easy to use a system will be. These two attitudinal factors are connected; users often perceive easy to use systems as more useful. TAM has been critiqued for its emphasis on only two factors (Agarwal and Prasad 1997). The model has been further criticised for its technology focus, as it appears to view user acceptance as depending mostly on the nature of the technology. It is argued that this model ignores the social processes of information systems development and implementation—workflows, roles, and power structures, for instance. It also does not explore cultural and social differences between user groups (clinicians, administrators, patients, clients, or consumers). These criticisms have resulted in revisions, including TAM2 and TAM3. The various forms of the TAM can assist HIDDIN professionals to understand some aspects of how successful the introduction of technology into an organisation has been, and to develop strategies to motivate users to accept the systems. The Unified Theory of Acceptance and Use of Technology (UTAUT), the outcome of a review of eight other theoretical frameworks, seeks to explain user intention to adopt a technology together with subsequent usage behaviour. Venkatesh et al. (2003) argue that the model developed from this review provides a basic conceptual framework that explains the individual acceptance of information and communication technology. To explore user perceptions and acceptance, UTAUT considers four constructs together with mediating factors: Performance expectancy is the degree to which an individual believes that using the system will help him or her to attain gains in job performance. Effort expectancy is the degree of ease associated with the use of the system. Social influence is the degree to which an individual perceives that important others believe he or she should use the new system. Facilitating conditions refers to the degree to which an individual believes that an organisational and technical infrastructure exists to support use of the system. Social factors considered to potentially influence these factors include gender, age, experience, and voluntariness of use. For example, effort expectancy may be moderated by gender, age, and experience, while social influence may be moderated by gender and age. Various modifications and adaptations of UTAUT have been proposed; some researchers consider it to be accumulation of various research efforts represented in different models and theories, rather than a coherent theory (Ahmed 2014; Venkatesh et al. 2013; Kim et al. 2015; Ayaz and Yanartaş 2020).

Relevance of Socio-technical Principles to Digital Health Healthcare systems in contemporary society are large and complex, incorporating a mix of community and hospital based public and private services provided for patients, clients, their families, and informal carers, by an array of administrative,

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clinical, technical, education, and research professionals. They are characterised by multiple networks of communication, governance, and responsibility. Patients typically move within and between departments and services, while clinical and administrative data and information is collected, manipulated, and communicated within and across departmental and service boundaries within healthcare environments. The bulk of this information is of a private and confidential nature, and its collection, storage, and use are subject to legal and ethical guidelines. Technology has become increasingly integral to this collection, management, and dissemination of information. Within the professional staffing component of healthcare systems, there are also producers and consumers of information and services—e.g. clinicians could be considered to ‘consume’ the outputs provided by educators and researchers. Socio-technical principles are useful to influence or enable decisions ranging from routine administrative processes to obscure clinical diagnoses or decisions about structures or services. Although there are many success stories, there are many examples of not-so-­ successful implementation of new health information systems or technologies into workflows. A significant reason for this is a focus on the technology to the detriment of other factors. When information systems were first introduced into healthcare environments, the focus was on technology such as hardware requirements and capabilities, software design and systems development. There was little interest in understanding the organisational and cultural factors already embedded in the organisation (Deluca and Enmark 2000; Venkatesh et al. 2011). It was assumed that information technology would easily slot into health environments and be enthusiastically adopted by end-users, who could include clinicians, management and patients, clients, or consumers. This approach, today described as technocentric, may have worked relatively well for business processes, but not in clinical areas, with a number of consequences: systems not being used as technology designers had intended them to be used; systems being boycotted by health professionals; health professionals using only a limited set of health information system features and functions; and health professionals bypassing or ignoring health information systems features and functions in an effort to complete or conduct healthcare work (Borycki and Kushniruk 2010). Thus, digital health technologies that are developed to improve workflow do not necessarily do so (Jensen 2015; Westbrook et al. 2011). Socio-technical analyses show a technocentric approach as a significant factor in these less than successful systems implementations. Adaba and Kebebew (2018) noted that up to 90% of information system failures may be attributable to lack of consideration of social and organisational factors in design processes. Unintended consequences and workarounds occur where there has been insufficient understanding of the impact of socio-technical factors on health information systems or management. While technical issues are an integral element in the development of health information systems, it is necessary to move beyond these to explore organisational and cultural factors—the social sub-system—which interact with the technical system. Information technology systems in healthcare must be able to operate in diverse settings such as hospitals, community health centres, general practice surgeries, and

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other healthcare environments, and cater to complex and varied information management requirements. Planning an information system means that both information management requirements and the health environment need to be considered. New technologies can disrupt traditional work routines, flows, and relationships. The location of equipment, the need to consult with other health professionals and interact with technology, and the protocols involved may all impact on established work patterns. It follows that a lack of attention to how a new technology will integrate into existing workflows may influence the willingness of health professionals to use it. This may be the case even if health professionals see the benefits of the new technology. Clinical simulation of workflows has the capacity to reduce opportunity for error or harm before the full implementation of any digital health technologies into healthcare environments (Jensen 2014; Mather et al. 2017a). Clinical simulation are socio-technical methods used to capture how the implementation of new technology can influence established work patterns or create unintended consequences or workarounds. They attempt to uncover the potential for unsafe work practices and highlight deficiencies in effective and efficient deployment of the intended change to the physical and social environment. Clinical simulation can improve quality of care and patient safety by enabling evaluation of digital technologies in context in a controlled environment, prior to implementation in real-world settings. Clinical simulation allows systems designers to explore social environments, analysing user requirements and work practices. The strategy also facilitates stakeholder involvement in the planning and development process. Stakeholders, including anyone involved with seeking or retrieving health data, information, or knowledge, can be involved before, during, and after the clinical simulation. HIDDIN professionals should be involved in the planning and development of the simulation scenarios, observation of the clinical simulation exercises, and debriefing of participants. Additionally, HIDDIN leaders and executives may seek reports of outcomes of clinical simulations to assist with decision-making towards implementation of new products or services. Workflow studies conducted using ethnography, time and motion studies, and other methods also apply socio-technical principles to capture the complexity of workflows of end-users of technology implementation (Walter et  al. 2019). Westbrook et al. (2011) reported from their research into workflows that over time, use of computers to complete tasks increased. This factor is an important consideration when planning to implement any digital health technologies for end-users. Lead time, co-design, and educational preparation of end-users are necessary to ensure social factors which could enhance or reduce acceptance of the new technologies are also understood prior to implementation. Workflow studies have been utilised to investigate the feasibility of introducing digital health technologies, including health education using digital tablet technology into clinical workflows (Driscoll et al. 2019). A case in point is Baker et al.’ (2019) analysis of delivery of a digital tablet to a hospital patient at the point of care, for diabetes education and support. This example demonstrates the importance of considering the nexus of social and technical

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perspectives in workflow prior to implementation. They found that although current clinical workflows technically enabled the digital health intervention, the social context was such that healthcare providers were not able to spend sufficient time with each patient to instruct them on the technology platform or the content. They concluded that the provision of this support to patients in a general medical ward needed to occur independently from usual care activities. They argued that when there is a lack of alignment between the social and technical aspects of new workflows, consequences may include overload, resistance, or failure of acceptance of the technology (Mather and Cummings 2017). Workarounds can develop which could promote an unsafe environment or potentiate errors. This outcome can be costly in terms of finances, human and physical resources. Additionally, it can be much harder to re-launch a second attempt as a residual lack of acceptance from the initial implementation may remain (Mather 2012). Workarounds highlight the need for the social sub-system to be congruent with the technical sub-system. In this use case there were a number of socio-technical elements that needed to be considered (Mather et al. 2017a). At an individual level the digital and health literacy of the consumer and health professional need to be adequate. At an organisational level access to a digital tablet that has the appropriate software and permissions to connect with the wireless Internet of the healthcare environment needs to be available. At a systems and organisational level there is a need for appropriate governance to enable access and use of the tablet by the consumer and health professional. HIDDIN professionals are responsible for the seemingly easy, high quality, and safe access to the information required by end-users. If privacy, security, or confidentiality are breached or connectivity becomes slow or is unavailable, end-users will notice and as part of risk management processes will let the health service management know. It is during these interruptions to workflows, omissions or failure during the process of enabling end-user access, that end-users become at least minimally aware of the roles and responsibilities that HIDDIN professionals have or should have in their health service.

 elevance of Socio-technical Principles R to the HIDDIN Workforce The rapid growth in the use of digital health technologies has challenged the status quo within healthcare environments at an individual, organisational, and systems level (Huckvale et al. 2019). Using the lens of individual, organisational, and systems highlights opportunities for HIDDIN professionals to promote and apply socio-technical perspectives and augment their own knowledge. National governance structures are overarching components which impact on sub-systems, namely the organisations delivering digital health technologies and providing health services. Within organisations is another sub-system or level comprised of individuals. They are end-users, and are most impacted when socio-technical principles are inadequately considered. Many professionals within health systems may be aware

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that technology does not always fit smoothly into the environment for which it was intended, however, most may not be able to identify reasons for this quandary. HIDDIN professionals, either through formal training or experiential learning, are competent to address the socio-technical issues that contribute to (in)effective digital health systems. These professionals are able to use their specialised knowledge in promoting digital literacy, advocating for the application of socio-technical principles in the workforce, and contributing to research and knowledge development.

Promoting Digital Health Literacy Support for a health workforce that can confidently use digital health technologies to deliver health and care is a strategic priority of many governments (e.g. Kennedy and Yaldren 2017; Australian Digital Health Agency 2018). It is essential that consideration be given to the digital health literacy of administrative, clinical, technical, education, and research staff within healthcare environments. If users are inadequately prepared for or engaged in digital health technologies design, development or implementation has potential negative consequences at an individual, organisational, or systems level. However, the needs, motivations, and skills of the various groups within a health service can vary widely. At systems levels, HIDDIN professionals can advocate that all stakeholder perspectives are considered in decision-making, planning, and implementation of policies, protocols, and programs relating to information management systems and digital health. This work can include contributions to developing effective governance structures. Digital health literacy cannot be promoted if governance structures are not available to support stakeholders. Privacy and security issues become apparent when governance does not keep pace with technological change (Mather et  al. 2017b). Additionally, governance structures regarding effective, safe, and appropriate behaviour by health professionals are required to ensure that the public and workers are protected. Enabling the development of digital professionalism is an important aspect of professional identity formation that requires robust governance. Clear direction provided by appropriate governance is necessary for all stakeholders when applying socio-technical values and principles. Use of the socio-­ technical approach will contribute to reducing the potential for error or harm. At an organisational level, HIDDIN professionals can liaise with users to ensure unintended consequences and workarounds do not reduce quality and safety for patients (Jensen and Kushniruk 2016). These people can contribute to the development and implementation of usable health applications and systems. While not necessarily software developers themselves, many of them will certainly be involved in the design and/or procurement of systems. This places HIDDIN professionals in a position where they can promote socio-technical values and principles. Doing so during planning or development will assist with ensuring the success of implementation and acceptance of proposed systems. In addition, the concept of digital health literacy is complex as stakeholders develop their own discipline-­specific language

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and perspectives that may not be shared by others; HIDDIN professionals have a role in building mutual understanding throughout organisations, to achieve positive effects on healthcare delivery and outcomes. At an individual level, HIDDIN professionals can ensure users are adequately guided and supported in being educationally prepared and engaged in accessing and using digital health technologies. They can also promote digital health literacy work with consumers by health professionals. Without the upskilling of society in digital health literacy there will continue to be inequity among end-users, such as those in vulnerable groups (Azzopardi-Muscat and Sorensen 2019; Showell et al. 2017).

Advocating for Users of Technology in Healthcare At systems and organisational levels, HIDDIN professionals have the opportunity to encourage other potentially influential individuals and groups to respect the views of less dominant stakeholders. Thus, HIDDIN professionals can encourage management to adopt the principle that all intended users of any digital health technologies should be involved at all stages of digital health systems development. This advocacy can challenge narrower thinking about end-user involvement only at the beginning of processes to enable project design or development. Inclusion of all stakeholders over time will enable learning about tasks undertaken and the technical systems that supported them. Developing understanding from multiple perspectives is an integral element of a socio-technical approach. HIDDIN professionals can also advocate for different groups of users. As has previously been noted, the success of many digital health and information system initiatives may depend upon how well diverse perspectives are acknowledged, negotiated, and integrated. At the individual level, HIDDIN professionals who adopt a socio-technical perspective will be well equipped to challenge technocentric approaches, ensuring that user needs are considered and incorporated into systems design. A socio-technical design perspective could facilitate questions such as: How will the introduction of this system affect stakeholders? How will stakeholders view the introduction of this system? Who will be the main beneficiaries of this application/system? How will this application enable consumers to receive better healthcare? What will the cost be to achieve this aim?

Contributing to Research and Knowledge About Digital Health Mumford suggested that there should be ‘no theory without practice, no practice without research’ and notes that ‘socio-technical researchers have always tried to test and develop theory’ (2006, p.321). Knowledge of socio-technical principles, concepts, and theories is constantly evolving. HIDDIN professionals and researchers can contribute to this growing knowledge base through practice, academic

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activity, and professional research. Through practical application of socio-­technical principles, HIDDIN professionals can informally expand their own knowledge base while demonstrating to colleagues the value of the approach. At a more formal level, HIDDIN professionals can contribute to professional development either as participants to extend their own knowledge, or as leaders to extend the knowledge of other health professionals. HIDDIN professionals have many and varied roles during their careers, which can include research and teaching appointments in higher education institutions where dissemination of new knowledge through academic and professional publications is expected and encouraged. In addition, HIDDIN professionals can collaborate with academics both to contribute to the knowledge base and to disseminate research findings within the workplace and the community. In summary, HIDDIN professionals are in prime position to show leadership in promoting the humanistic values and principles encompassed within socio-­technical approaches, and so to inform health information systems design and implementation for the benefit of all users. Socio-technical approaches enable HIDDIN professionals to explore issues arising from the interaction between information and communication technologies, and the social, professional, and cultural contexts of healthcare. This approach  equips  HIDDIN professionals to ably  recognise and address socio-technical agendas and priorities at many levels in the digital transformation of healthcare systems.

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Chapter 4

Occupational Classifications in the Health Information Disciplines David T. Marc, Prerna Dua, Susan H. Fenton, Karima Lalani, and Kerryn Butler-Henderson

Abstract  If you ask people within the HIDDIN workforce how electronic health records support safe care delivery or how telehealth has shaped service delivery during the time of COVID, there is little dissension. Yet if you ask them what is the umbrella term for the various specialists whose functions relate to managing and governing health data, information, and knowledge, there is little agreement. This chapter explores the HIDDIN workforce as an occupational group. It examines how the workforce is represented in global and national occupation lists. It analyses the way that the workforce is grouped in global job listings. It weighs up role titles versus competencies as an approach to categorise types of work. The chapter concludes by reflecting on the effects of the COVID-19 pandemic and considering workforce development for the future of work. Keywords  Competencies · International Standard Classification of Occupations · Jobs · Roles

D. T. Marc College of St. Scholastica, Duluth, MN, USA e-mail: [email protected] P. Dua Louisiana Tech University, Ruston, LA, USA e-mail: [email protected] S. H. Fenton ∙ K. Lalani The University of Texas Health Science Center at Houston School of Biomedical Informatics, Houston, TX, USA e-mail: [email protected]; [email protected] K. Butler-Henderson (*) Digital Health Hub, College of STEM, RMIT University, Bundoora, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_4

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Introduction The HIDDIN workforce is a dynamically changing workforce, rendering it hard to define workforce boundaries. As countries have moved from paper-based medical records to electronic health records and healthcare-related apps, they have seen a change in operations for planning, management, clinical diagnosis and treatment, disease surveillance, research, and education. However, little attention has been paid to the classification of the workforce required to implement these operations successfully. The American Health Information Management Association is one of many HIDDIN associations asserting the need for professionals to update their skills to manage the transition to digital health (Gibson et al. 2015). Some types of HIDDIN jobs—health information management, for example—require healthcare licensure and specific knowledge and qualifications and are quite formally defined, while others seem barely able to be categorised or described as part of the healthcare industry. As digital health has evolved, overlaps, and synergies have occurred in job roles and occupation titles at the intersection between previously distinct fields of practice in healthcare, information science, and computer science. Workforce occupational classification systems can be of benefit in organising and ranking similar occupations in groups based on the defined tasks and duties required by certain jobs. These systems enable job placement, employment counselling, and career guidance; using uniform occupational language can effectively match job vacancies and workers (Miller et al. 1980). The classification of occupations helps government agencies and employers to collect and use data on labour market trends and perform comparisons between related occupations. Governments use occupational classifications in the collection and dissemination of statistics from a variety of sources, including population censuses, labour force surveys, household surveys, and employer surveys; these classifications enable occupations to be linked to socioeconomic status, lifestyle (sedentary, physical, or exposed to hazards), and achievement in life (Clougherty et al. 2010). Governments and companies can use classification systems for defining job requirements, identifying educational expectations, reporting industrial accidents, administering workers’ compensation, and managing employment-related migration.

How Occupations Are Classified Internationally To date, over 180 countries have adopted some form of an occupational classification system. Examples include the USA’ Standard Occupational Classification System (SOC) (US Bureau of Labor Statistics 2020), the Australian and New Zealand Standard Classification of Occupations (Australian Bureau of Statistics 2020), the South African Standard Classification of Occupations (Africa Check 2001), India’s National Classification of Occupation (India’s National Career Service 2015), the Classification of Occupations of Costa Rica (Sistema de

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Estadistica Nacional 2011), Japan’s Standard Occupational Classification System (Japan’s Ministry of Internal Affairs and Communications 2009), Germany’s Klassifikation der Berufe (FDZ 2013). In the global economy, a standard to collect and report occupations data has become indispensable. The International Standard Classification of Occupations (ISCO) was first proposed in 1921, created in 1958 by the International Labour Organisation, and has been revised three times since then. ISCO-08 is a four-level hierarchical system that classifies jobs into 10 major, 43 sub-major, 130 minor and 436 unit groups. A title, code number, and definition are associated with each ISCO-08 group. The definition specifies the scope of the group and summarises the main tasks and duties performed in associated occupations. ISCO is the basis for comparisons of occupational statistics between countries and a conceptual model for the development of national occupational classifications (ISCO 2016). To evaluate global labour trends, the mapping of ISCO-08 to national classifications is essential, although no known ontology specifies how each national classification maps to ISCO-08. One example is the US SOC system, last updated in 2018; the 2018 SOC does not currently include a direct map to ISCO-08; however, the 2010 SOC does, and a 2018 SOC to 2010 SOC crosswalk can be used to map the 2018 SOC to ISCO-08 indirectly. National classification systems that map to ISCO in some way or other are: Australia and New Zealand, Brazil, Bulgaria, Canada, Cape Verde, Costa Rica, Cuba, Czech Republic, Denmark, El Salvador, Germany, India, Indonesia, Israel, Norway, Panama, Paraguay, Philippines, Poland, Portugal, Saudi Arabia, Singapore, South Africa, Spain, Sweden, Switzerland, Thailand, the UK, the USA, Uruguay. Not all national classifications map to ISCO, however, or if a mapping exists, it is not a full crosswalk; the reason for this is that many national classifications offer more specific occupational categories based on their national workforce trends.

Occupational Classification of HIDDIN Work ISCO-08 has only three categories for HIDDIN occupations: Health Information Technicians (ISCO-08: 3252); Filing and Copying Clerks (ISCO-08: 4415); and Librarians and Related Information Professionals (ISCO-08: 2622). This is a very limited representation of HIDDIN-related occupations. Consequently, many national classifications also lack representation of HIDDIN-related occupations. In the USA in 2014, three peak bodies (the American Health Information Management Association, the American Medical Informatics Association, and the Health Information and Management Systems) jointly recommended changes to the Standard Occupational Classification System Policy Committee, to include additional categories for Health Information Technology occupations in the 2018 SOC update. This resulted in minor modifications—addition of a new occupational category for medical records specialists, modifications to the existing health information technologists to include medical registrars, and expanded career examples

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including health information analysts and health informatics specialists. However, these changes are not yet reflected in ISCO, and they still do not offer an exhaustive classification of HIDDIN occupations. The challenge for any occupational classification system is to be able to describe occupations that evolve quickly. All the HIDDIN disciplines are rapidly changing. For instance, the professional tasks, responsibilities, and roles in health information management-related professions have drastically changed in the past 5–10 years: Sandefer et al. (2015), Fenton et al. (2017), and Marc et al. (2019) have found that these professions in the USA anticipate growth in the areas of leadership, data and informatics, and decline in coding work, by 2025. Similar significant changes are occurring in the other HIDDIN disciplines. Continuous change in work focus makes it hard to fully describe and represent the HIDDIN disciplines in occupational classifications, nationally, or internationally.

Categories Derived from Global Job Listings Occupational trends may suggest categories in the HIDDIN disciplines globally. This is illustrated in an analysis of health informatics and information management recruitment in 64 countries on www.indeed.com during September 2018 (Marc et  al. 2019). Using competency areas from the American Health Information Management Association as a guide, 10 terms were applied in search strings to identify relevant job postings. This analysis provided a glimpse into four occupational clusters and differences across countries in the prevalence of job postings with these characteristics. Most of the jobs in cluster 1 related to health information technology; the terms used were data, engine, software, test, analyst, design, security, system, technology, and solution. The majority of jobs in cluster 2 related to health research and were more clinically focused. Most jobs in cluster 3 were related to health project management and leadership, and the most common terms included sale, market, research, client, business, and project management-related terms. In cluster 4, most of the jobs related to health compliance and included terms such as safety, regulatory, and maintenance. The analysis also showed a global trend in jobs in the areas of health consumer engagement, health informatics, health information governance, health data analysis, and clinical documentation improvement. The demand for these types of jobs was uneven. The role titles of health consumer engagement and clinical documentation improvement were found in the USA, but they did not often occur in other countries. The title health data analyst was found more frequently in the UK, Canada, Australia, and India than in other countries. Health data analysis jobs were in high demand globally; countries such as the USA, the UK, Canada, Australia, and India each advertised more than 250 positions. Health data analysis jobs were more prevalent in Australia, Canada, India, and the UK, whereas the USA had a high number of consumer engagement, information governance, and clinical documentation improvement jobs.

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The job categories derived from this analysis were in keeping with four high-­ level role types—executive, professional, operational/technical, and supervisory/ managerial—identified in a cooperative effort between the USA and the European Union to develop a comprehensive list of health information technology competencies (HITComp 2020).

Categorising Roles or Competencies Many of the HIDDIN disciplines work together in international organisations, such as the International Medical Informatics Association, the International Federation of Health Information Management Associations, the International Federation of Library Associations Health and Biosciences Libraries Section, and the Institute of Electrical and Electronic Engineering Medicine and Biology Society. Despite these international peak bodies, and the existence of ISCO, differences between countries remain in role titles and functional roles. Role titles are different in part because the needs of each country’s healthcare systems are different. In the USA, the healthcare industry is based on a system of third-party insurance; to date, this has largely meant that providers are paid based on services provided, also known as fee-for-service. The UK, Canada, Australia, New Zealand, and India all have public or nationalised healthcare systems, with many different financing systems. These differences perhaps create a focus less on specific role titles and more on the competencies needed, which may or may not be well-­represented by the ISCO or country classifications previously described. Given the diversity of health care systems across the globe, it is also to be expected that roles even within national healthcare settings will be varied. For example in the USA, more nationalised systems and larger integrated health delivery systems may have regional directors or otherwise may manage their health information and data functions centrally, relying on significant specialisation within their health information management department, while small critical access hospitals (less than 25 beds) may employ one or two people who perform all health information-related functions. This suggests a need to focus on the skills or competencies needed for a specific position and reduce emphasis on occupation classification, at least for the purposes of job placement (Markowitsch and Plaimauer 2009). In response, Austria, Germany, France, Sweden, and the USA have developed taxonomies and databases to document skills and competencies for jobs. Using verbs that describe skills and competencies to be mastered is a standard practice among course designers in competency-based higher education. These same verbs can be used to describe job functions to be performed in the workplace setting. Ideally, this approach helps to align workforce education with labour market demand. Technology and the skills needed to work with technology are changing at ever-­ increasing rates. The evolving emphasis on describing skills and competencies, as well as the continuous evolution of job classifications according to skills and competencies needed, are another challenge to the ISCO and national occupational

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classifications, and related resources. These struggle to keep up with fast-moving technology. Changes in occupational codes can take 4–5 years from proposal to adoption, meaning that the jobs, skills, and competencies proposed are likely to be outdated by the time they are published. As a result, discussions of the future of work in the digital economy are focused on foundational skills that cross industries, for example “digital building blocks, business enablers, and human skills” (Markow et al. 2018): Within digital building blocks are the skills of managing data, software development, computer programming, analysing data, and digital security and privacy. Business enablers include business processes, project management, digital design, and communicating data. The domain of human skills includes communication, critical thinking, collaboration, analytical skills, and creativity. Analysis of job postings shows high demand for these generic skills in the labour market and also finds that they have higher pay rates than more constrained job titles. An example is the concept of a new “analytics translator” function rather than a role, proposed to be needed across a wide variety of organisations (Henke et al. 2018). The rationale is that, to achieve real impact, organisations looking to hire data scientists so they can utilise advanced analytics and artificial intelligence to improve their decision making and processes also need to hire translators who do not have technical expertise in data science, but rather have domain-specific knowledge, general technical fluency, and project management skills. HIDDIN role titles may become less and less important as technological change continues to accelerate, whereas skills and competencies will increasingly come into focus. This affects the development of skills and competencies across the globe. For example a common skill might be “interact professionally at all levels of the organisation” or “create effective information visualisations from raw data;” such skills would not need to be role-specific, and a person who possessed them could be employed in multiple roles. The HIDDIN disciplines need to recognise this shift in the future of work and focus on developing skills and competencies that translate across jobs and organisations.

Conclusion The global digital health market forecast of a compound annual growth rate of 13.4% between 2017 and 2025 (Health Standards 2017) will continue to create opportunities for the specialist HIDDIN workforce. COVID-19 has highlighted not only the importance of information management and governance and how technology can support safe healthcare, but also how important this workforce is to health service management and delivery, at the level of international cooperation. An International Federation of Health Information Management Associations survey (Fernandes et al. 2020) identified the impact of COVID-19 on HIDDIN workforce functions. The rapid transition to electronic capture of information elevated the importance of these roles. Countries where information management uses

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electronic, digitised, or hybrid record systems, compared to those where the work is still largely paper based, reported higher rates of people in this workforce working remotely and being involved in establishing new systems for information capture and management. This global tragedy accelerated technology adoption, with many of these changes expected to remain or to start further transformation. For example expanded telehealth services are predicted to remain the new normal and to be the catalyst for telehealth for preventative health, consequently requiring new support functions and roles. What does this mean for occupations in the HIDDIN workforce? Evidence-­ informed and technology-supported health management and care will continue to reshape specialisations within this workforce. In coming years, many current roles in today’s workforce will no longer exist; whilst some will become redundant, others will evolve to meet future needs. The underlying need for this workforce will remain constant, and it ought to be recognised more formally among the health professions in occupation classifications. COVID-19 may be a driver for such change. Refinement of workforce categories in response to the evidence of global job categories and trends may assist governments and employers to meet future HIDDIN workforce needs in their respective countries. The required competencies continue to evolve rapidly, and the imperative rests on policymakers together with educators to ensure that there is a well-defined workforce able to adapt to technological change and to maintain its relevance.

References Africa Check. South African Standard Classification of Occupations (SASCO). 2001. https:// africacheck.org/wp-­content/uploads/2019/05/ghs-­2015-­occupation-­codes.pdf. Accessed 31 Jul 2020. Australian Bureau of Statistics. Australian and New Zealand Standard Classification of Occupations (ANZSCO). 2020. https://www.abs.gov.au/ANZSCO. Accessed 31 Jul 2020. Clougherty JE, Souza K, Cullen MR. Work and its role in shaping the social gradient in health. Ann N Y Acad Sci. 2010;1186:102–24. FDZ.  Klassifikation der Berufe (KldB). 2013. http://doku.iab.de/fdz/reporte/2013/MR_08-­13_ EN.pdf. Accessed 31 Jul 2020. Fenton SH, Low S, Abrams KJ, Butler-Henderson K. Health information management: changing with time. Yearbook Medical Inform. 2017;26(1):72–7. Fernandes L, Butler-Henderson K, MacDonald M. The impact of COVID-19 on the work life of HIM professionals: an IFHIMA survey. J AHIMA. 2020. https://journal.ahima.org/the-­impact-­ of-­covid-­19-­on-­the-­work-­life-­of-­him-­professionals-­an-­ifhima-­survey/. Accessed 28 Jul 2020. Gibson CJ, Abrams K, Crook G. Health information management workforce transformation: new roles, new skills and experiences in Canada. Perspect Health Inf Manag Int Issue. 2015. https:// library.ahima.org/doc?oid=301180#.YGQp9z87aUk. Accessed 28 Jul 2020. Health Standards. Digital health: current state & future growth 2017-2025. 2017. https://healthstandards.com/blog/2017/10/25/digital-­health-­trends-­2025/. Accessed 22 Jul 2020. Henke N, Levine J, McInerney P. You don’t have to be a data scientist to fill this must-have analytics role. Harv Bus Rev. 2018. https://hbr.org/2018/02/you-­dont-­have-­to-­be-­a-­data-­scientist-­to-­ fill-­this-­must-­have-­analytics-­role. Accessed 5 Feb 2020.

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HITComp. HITComp: Health Information Technology Competencies. 2020. http://hitcomp.org/. Accessed 29 Jun 2020. India’s National Career Service. India’s National Classification of Occupation (NCO). 2015. https://www.ncs.gov.in/Documents/National%20Classification%20of%20Occupations%20_ Vol%20I-­%202015.pdf. Accessed 31 Jul 2020. ISCO. ISCO-08 Structure, index correspondence with ISCO-88. 2016. https://www.ilo.org/public/ english/bureau/stat/isco/isco08/index.htm. Accessed 31 Jul 2020. Japan’s Ministry of Internal Affairs and Communications. Japan’s Standard Occupational Classification System. 2009. https://www.soumu.go.jp/english/dgpp_ss/seido/shokgyou/ co09-­2.htm. Accessed 31 Jul 2020. Marc D, Butler-Henderson K, Dua P, Lalani K, Fenton SH.  Global workforce trends in health informatics & information management. Stud Health Technol Inform. 2019;264:1273–7. Markow W, Hughes D, Bundy A. The new foundational skills of the digital economy. Developing the professionals of the future. Washington, DC: Burning Glass Technologies. 2018. Markowitsch J, Plaimauer C. Descriptors for competence: towards an international standard classification for skills and competences. J Eur Indus Train. 2009;33(8/9):817–37. Miller AR, Treiman DJ, Cain PS, Roos PA. Work, jobs, and occupations: a critical review of the Dictionary of Occupational Titles. Washington, DC: National Academy Press; 1980. Sandefer R, Marc D, Mancilla D, Hamada D. Survey predicts future HIM workforce shifts: HIM industry estimates the job roles, skills needed in the near future. J AHIMA. 2015;86(7):32–5. Sistema de Estadistica Nacional. 2011. http://sistemas.inec.cr/sitiosen/sitiosen/Archivos/ COCR_2011.pdf. Accessed on 31 Jul 2020. US Bureau of Labor Statistics: Standard Occupational Classification. 2020. https://www.bls.gov/ soc/. Accessed 29 Jun 2020.

Chapter 5

Competencies, Education, and Accreditation of the Health Information Workforce Ann Ritchie, Gemma Siemensma, Susan H. Fenton, and Kerryn Butler-Henderson

Abstract  This chapter looks at the identity of the HIDDIN (Health Informatics, Digital, Data, Information, kNowledge) workforce from the perspective of the competencies that are needed to do these jobs, and the accreditation of formal education and training programmes that confer recognised qualifications for these jobs. We examine definitions and a selection of research articles about how each of the groups has developed and differentiated themselves from others. We also compare the competency sets from a sample of professional associations and industry bodies, noting each group’s unique areas of responsibility and overlaps; it appears that information governance is a responsibility common to all groups. Alternative education and training pathways for initial entry and ongoing professional development for the HIDDIN occupations, such as employer-driven on-thejob training and modular certification and micro-credentialing approaches, are emerging as more agile responses to the immediate needs of the workplace, and are challenging traditional post-secondary education structures. This has implica-

A. Ritchie (*) Independent Consultant, Melbourne, VIC, Australia G. Siemensma Ballarat Health Services, Ballarat, VIC, Australia e-mail: [email protected] S. H. Fenton The University of Texas Health Science Center at Houston School of Biomedical Informatics, Houston, TX, USA e-mail: [email protected] K. Butler-Henderson Digital Health Hub, College of STEM, RMIT University, Bundoora, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_5

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tions for the identity of these groups as professions. The chapter concludes that a more systematic and coordinated approach to this aspect of strategic workforce development is needed. Keywords  Competencies · Education · Accreditation · Professionalism · Information governance

Introduction In the past few decades, healthcare managers and employers have witnessed a rise in demand for HIDDIN (Health Informatics, Digital, Data, Information, kNowledge) practitioners with increasingly specialised skill sets and more highly refined scopes of practice. This in turn has brought about a corresponding—although not always well articulated—need for more specialised training in related areas of expertise and competence. In some professions, training courses come with appropriate mechanisms for the individuals who undertake the training to be certified or credentialed, as well as mechanisms for accrediting the training programmes and training providers. Each of the occupational groups who together make up the HIDDIN workforce has recognised competencies and, in some cases, there are competency-based education frameworks against which post-secondary education and training programmes and providers are accredited. Accordingly, this chapter explores the educational structure for the HIDDIN disciplines, with examples where work has already been undertaken in this area.

Competency In essence, the term “competent” means having expertise, knowing how to do a particular task or activity, and being able to do it. Competencies have been described as: integrated sets of Knowledge, Skills and Attributes (KSAs) that are needed to perform tasks in a particular field or circumstance. Competencies are also said to be observable, measurable and able to be taught. Some competency frameworks have basic and advanced levels of practice, and performance measures by which these can be measured. (Ritchie 2020, p29).

When applied to HIDDIN work and the different occupational groups that make up this workforce, there are a number of competency frameworks that need to be examined. Table 5.1 provides an overview of the ways that competencies are used by different stakeholder groups (where stakeholder is defined as any group that has an interest in their development and use), outlines why they are used (their purpose), and how they are implemented and used. Stakeholders fall into six main groups,

Purpose—why competencies are used? Education and training (for qualifications, certification)

Stakeholder groups—who uses competencies? Universities and other training Professional associations/ organisations industry bodies Regulate and accredit Curriculum, education and training course providers; course design development, and delivery course accreditation Competency frameworks; Accreditation of Setting and Continuing Professional education and maintaining training providers Development (CPD); standards of administering practice, safety and and courses certification schemes; quality credentialing Education/research, Strategic workforce Education/ funding; translation into planning research; competency-based translation into competency-based competency frameworks curricula Teaching staff Continuing Professional Professional/ development Development (CPD) workplace/career programmes and audits development and lifelong learning Designing multi-­ disciplinary teams; team development

Human resource planning; Workplace frameworks Staff (workplace) development

National health workforce frameworks

Agency staff development

Recruitment; job design and position descriptions; team development; performance appraisal/ development

Recruitment; defining scopes of practice; mandatory staff training

Training site and work placement accreditation; credentialing

Health managers On-the-job training programmes

Oversee and regulate professions and organisations

Health workforce agencies Employers, e.g. (national) hospitals Recruitment; staff development systems

Table 5.1  Health information workforce competencies—their purposes and use by different stakeholder groups

CPD; staff development/ training; career planning; certification, audit/ reflections

Continuing Professional Development (CPD) and specialist certifications

Health practitioners (individuals)

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each having complementary areas of responsibility and roles: universities and other training organisations, professional associations and industry bodies, health workforce agencies, employers, managers, and health information practitioners themselves. The purposes for which competencies may be used are: education and training; standards, safety, and quality; strategic workforce planning; professional/ career/workplace development; and lifelong learning. In general terms, the work of all the specialist health information groups may be conceptualised as being “responsible for the development, maintenance, and governance of the systems used to manage health data, health information, and health knowledge” (Gilbert et  al. 2020, p39). Referring to the centrality of the role of information governance in their mapping of the competencies for the health information professions, The Global Health Workforce Council (2015, p3) state: “The emerging body of knowledge around Information Governance in the healthcare ecosystem anchors the entirety of the health information professions”. Figure 5.1 has been adapted from The Global Health Workforce Council (2015, p3) diagram to represent their shared responsibilities for information governance. Gartner (2020, np) define information governance as: The specification of decision rights and an accountability framework to ensure appropriate behaviour in the valuation, creation, storage, use, archiving and deletion of information. It includes the processes, roles and policies, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.

Health Information & Communications Technologists:

Health Information Managers:

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acquire, analyse, and protect digital and paper-based medical and health information vital to providing quality patient care and maintaining the daily operations management of health information and electronic health records; often serve in bridge roles, connecting clinical, operational, administrative, and financial functions; affect the quality of patient information and patient care at every point in the healthcare delivery cycle; ensure an organisation has the information available when and where it is needed while maintaining the highest standards of data integrity, confidentiality, and security.

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manage the technical infrastructure used to capture, manage, secure, share and use health information in a digital format; focus on health information systems design, implementation and operation, working with software and hardware used to process health data and ensure usability; facilitate the technology user’s experience and provide technical support for health information systems, such as electronic health records, laboratory information management systems, medical devices, mobile applications, and other systems used to capture and maintain health information and generate knowledge.

Information Governance Focuses on information as a strategic asset that requires high-level oversight.

Health Librarians:

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8 formal Information Governance principles: Accountability, Transparency, Integrity, Protection, Compliance, Availability, Retention, Disposition

focus on the services and systems that deliver research-derived data, information and knowledge to healthcare clinicians, managers, policymakers, educators and researchers; manage the research knowledge base published in all formats, as well as grey literature; advance the application of evidence-based practice through literature searching and evidence synthesis, health literacy and teaching, knowledge management and translation services; deliver services that are dedicated, secure, permanent and trustworthy sources of authoritative information, critical and fundamental to an organisation’s information governance structures.

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Health Informaticians:

work in the interdisciplinary field that studies and pursues the effective uses of health data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health; focus on how information technology is applied to the continuum of healthcare delivery in order to produce data, information and knowledge to support healthcare and public health practices; integrated discipline with specialty domains that include clinical and health sciences informatics, public health and nursing, research and population health and others.

Fig. 5.1  Responsibilities of the health information professions and information governance

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In essence and when applied to the health care sector, information governance is about ensuring the right decisions are made, i.e. decisions that are based on quality-­ assured information (evidence) derived from two major sources of data and knowledge: patient records and research publications. Each of the health information professions has a role in setting up, maintaining and managing the information systems and processes, and upholding the eight information governance principles: accountability, transparency, integrity, protection, compliance, availability, retention, disposition. Thus, they all have a common interest in getting the right information to the right person at the right time and place and in the right format. And each has a skill set, competencies, roles and responsibilities integral to the complex whole. The American Health Information Management Association (AHIMA) have defined the ten organisational competencies of information governance for health care: strategic alignment, information governance structure, data governance, enterprise information management, IT governance, analytics, privacy and security safeguards, regulatory and legal, awareness and adherence, and information governance performance (Fenton et al. 2017, p74). Fenton et al. (2017) differentiate between the three levels of governance characterising them as: data governance (process, methods, tools, techniques); IT governance (frameworks, best practices); and information governance (overarching regulations and policies). They state: “data governance is the most basic, rudimentary, level of information governance and if not undertaken properly, the results will substantially affect all other levels of information governance” (p73). They report on the results of a survey of HIMs conducted by AHIMA in 2015, which identified data analytics/mining, informatics, and information governance as the fastest areas of growth in their field. An illustrative example of organisational information governance is seen in the case of hospital librarians. Ritchie et al. (2020) in their study of the contribution of hospital libraries in meeting hospital accreditation standards, state that hospital librarians’ expertise and ability to access up-to-date health information and knowledge resources is “integral to an organisation’s governance framework… hospital libraries help to ensure that the work of a hospital’s employees—clinicians and other health care professionals, managers, administrators, educators, researchers, policy makers—is evidence-­ based and complies with safety and quality standards”. Understanding the distinctive skill sets (competencies) of the HIDDIN workforce, and recognising their common interest and varying responsibilities in the area of information governance, will help to clarify and make their roles more visible in the digital health care environment.

Competency Frameworks There are many competency frameworks used by the HIDDIN disciplines. A sample of these is outlined in Table 5.2. Even though these individual disciplines have evolved in relatively separate, specialist streams, there are roles and competencies

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that are complementary and at times, may have overlapping and/or blurred boundaries. Competency frameworks also vary in the details provided: some specify foundational skills through to advanced skills, while others provide descriptions to indicate the scope of a competency area. Table 5.2  Health information workforce competency frameworks Discipline, Name of organisation, and Information source Health Information Managers (HIM) Health Information Management Association of Australia https://www.himaa.org.au/files/Misc/ Docs/HIMAA_HIM_Competency_ Standards_Version_3_FNL_ June2017.pdf

Number and overview of domains/competencies/ quadrants Domains (9) • Generic professional skills • Health information and records management • Language of healthcare • Healthcare terminologies and classification • Research methods • Health services organisation and delivery • Health information law and ethics • eHealth • Health information services organisation and management Health Information Managers (HIM) Domains (6) • Data structure, content, and information governance American Health Information Management Association (AHIMA) • Information protection: access, use, disclosure, privacy, and security (2018) • Informatics, analytics, and data use https://www.ahima.org/ • Revenue cycle management him-­curricula/ • Health law and compliance • Organisational management and leadership Competency areas (8) Health Librarianship (HL) Australian Library and Information • The health environment • Reference and research services Association, Health Libraries • Resources Australia (2018) • Leadership and management https://read.alia.org.au/ • Digital, ehealth, and technology alia-­hla-­competencies • Health literacy and teaching • Health research • Professionalism Competencies (6) Health Librarianship (HL) Medical Library Association (2007) • Information services • Information management https://www.mlanet.org/page/ • Instruction and instructional design competencies • Leadership and management • Evidence-based practice and research • Health Information Professionalism Competencies (8) Health Informatics (HI) • Principles and strategy Public Health Informatics Institute • Standards and interoperability (2016) https://phii.org/resources/view/9462/ • Project management • Information systems applied-­public-­health-­informatics-­ • Communication competency-­model • Evaluation • Analysis, visualisation, and reporting (VAR) • Policy

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Table 5.2 (Continued) Discipline, Name of organisation, and Information source Health Informatics (HI) Digital Health Canada (2019) https://digitalhealthcanada.com/ wp-­content/uploads/2019/12/ Competency-­Requirements-­Exam-­ Version.pdf

Number and overview of domains/competencies/ quadrants Domains (6) • Information management • Technology ecosystem • Clinical and health sciences • Canadian health system • Healthcare transformation • Project management Quadrants (6) Health Information and • Operational interactions Communications Technologists • Health data interactions (HICT) and • Patient interactions Health Informatics (HI) • Administrative interactions HITCOMP (2015) • Clinical interactions http://hitcomp.org/competencies/ • Communication interactions Domains (29) Health Information and • Analytics and statistics Communications Technologists • Change management (HICT) and • Classification of disease, coding diagnoses, and Health Informatics (HI) and Health Information Managers (HIM) procedures • Clinical documentation improvement (CDI) Global Health Workforce Council • Data management and information governance (IFHIMA and AIMA) https://ifhimasitemedia.s3.us-­east-­2. • Data quality and information integrity • Ethics amazonaws.com/wp-­content/ • Financial management uploads/2018/01/20033722/ AHIMA-­GlobalCurricula_Final_6-­ • Health information access, disclosure, and exchange • Health information systems and application design and 30-­15.pdf planning • Health information systems and application development and deployment • Health information systems and application support • Health law, regulation, accreditation, and/or certification • Health record content and documentation • Human resource management • Information and information systems governance • Information protection—data privacy, confidentiality, and security • Information security strategy and management • Organisational management and leadership • Project management • Purchasing and contracting • Quality management • Research design and methods • Risk management • Standards for data content, health information exchange, and interoperability • Strategic planning • Training and development • Work design and process improvement • Healthcare delivery systems

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The analysis of the sample sets listed in Table 5.2 shows that although there are differences in each group’s primary areas of responsibility, there are similar skills, knowledge, and attributes which could be termed generic competency areas that run through all groups. These include Leadership and Management (variously detailed as financial management, human resource management, project management, change management); Professionalism (ethics, understanding health care environments and contexts, knowledge of legal and regulatory frameworks); Professional and Organisational Development (instruction and instructional design, professional development, training and development). Thus there is some basis for a superficial observation that the disciplines are more alike than different. Further analysis of the competencies related to each groups’ areas of focus, however, indicates that there are particular distinguishing features that align with their primary responsibilities, and serve to differentiate each of the specialist groups. Health Information Management has a key focus on health records and data and exhibits distinguishing features in three main areas: Health information and records management; Healthcare terminologies and classification; Data structure, content, and information governance. Health Informatics distinguishes itself in three areas focusing on patient information and how technology is applied: Computer science and system design (technology ecosystem); Clinical and health sciences; Healthcare transformation. Health Information and Communications Technologists distinguishes itself by focusing on the technical infrastructure of health: Health information systems and application; Implementation and operation of systems; Working with software and hardware used to process health data. Health Librarianship delivers the services and systems for research/evidence-­ based health care and distinguishes itself through its focus on three areas: Reference and research services; Information resources (in the sense of collection management); Evidence-based practice and research.

Competency-Based Education Competency-based education is defined as: an approach to designing academic programs with a focus on competencies (knowledge, skills and abilities) rather than time spent in a classroom. According to the Competency-­ Based Education Network (C-BEN): Competency-based education combines an intentional and transparent approach to curricular design with an academic model in which the time it takes to demonstrate competencies varies and the expectations about learning are held constant. Students acquire and demonstrate their knowledge and skills by engaging in learning exercises, activities and experiences that align with clearly defined programmatic outcomes. (Strategy Labs 2017, p5)

The structure and delivery of formal training varies greatly across the HIDDIN disciplines and between countries. For example, a review of global HIM training (IFHIMA 2021) identified that some countries have been offering training programmes for more than 80 years. The majority of formal training globally is

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typically through vocational providers and at an educational level below a bachelor or baccalaureate level. Whilst the number of countries offering formal university level training in HIM is increasing, including bachelor (majority) and master (minority) level, this level of required formal training is not recognised globally. No doctorate level training in HIM or clinical classification has been identified. Conversely, digital health and data analytics worldwide are considered relatively new on the educational scene and university programmes are gradually increasing in number and popularity. There is a paucity of research and evidence to support specialist HIDDIN educational development, compared to research into other areas of health workforce education. One main focus for health informatics and information management education is on the identification of competencies. The US HIM profession conducted a survey to try and identify the roles and skills that are needed for the future (Sandefer et  al. 2015), while the US health informatics profession used a more constrained consensus-based approach (Valenta et al. 2018). Interestingly, a survey of both educators and employers conducted in the USA around this time demonstrated a significant difference in the beliefs of these two groups regarding graduates’ professional and technical skills, leadership skills, and employability skills (Jackson et al. 2016). There seems to be more research about hybrid education, that is, providing clinical professionals with informatics skills—either interprofessionally (Gray et  al. 2015; Whittaker et  al. 2015), or in specific clinical disciplines: medicine (Vossen et al. 2020; Jidkov et al. 2019; AMIA 2011; Siribaddana et al. 2019), nursing (Ammenwerth and Hackl 2019), pharmacy (Martin et al. 2019), and the clinical research data management profession (Zozus et  al. 2017). Overall, determining the competencies needed to develop hybrid specialists is a work in progress. It is still not unusual to encounter persons working in HIDDIN profession roles who may have no formal training, or who may have done training that was not quality assured or evidence-based. Healthcare is now witnessing a training evolution beyond competencies to entrustable professional activities (EPAs). According to Cate (2005), EPAs: are part of essential professional work in a given context; must require adequate knowledge, skill, and attitude, generally acquired through training; must lead to recognised output of professional labour. EPAs should be: confined to qualified personnel; independently executable; executable within a time frame; observable and measurable in their process and their outcome, leading to a conclusion (“well done” or “not well done”). EPAs should reflect one or more of the competencies to be acquired. EPAs are a way to transform competencies into practice; EPAs do not describe the learner, but rather describe the work to be done; EPAs often require more than one competency, integrated together (Cate 2013). Finally, for an EPA, it is necessary to determine the needed levels of supervision, including (Cate 2013, p1176): “Observation but no execution, even with direct supervision; Execution with direct, proactive supervision; Execution with reactive supervision, i.e. on request and quickly available; Supervision at a distance and/or post hoc; Supervision provided by the trainee to more junior colleagues”. EPAs were initially developed for physicians, though we currently see EPAs for dental (Goodell et  al. 2019) and pharmacy (Marshall et al. 2020), along with interest from nursing (Al-Moteri 2020).

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Given the continuing integration of more and more information technology into day-to-day clinical practice, the question becomes whether or not the HIDDIN workforce needs to develop entrustable professional activities. For example, how can an organisation determine whether or not the recently developed clinical decision support algorithm has been correctly developed and is acceptable to be implemented for clinical decision-making? How can an organisation determine whether the various reports and dashboards are developed by those with the appropriate training and skill? Currently, the HIDDIN workforce training programmes, especially degree programmes, focus on competencies. While competencies are certainly an improvement over the previous topic-based approach to training the HIDDIN workforce, they may not be adequate in the increasingly digital world. EPAs were introduced to assure patients that they could “trust” their clinicians; so the need to use EPAs is not immediately apparent for the HIDDIN workforce. However, the HIDDIN workforce is increasingly involved in developing information, knowledge, and tools to assist clinicians and consumers to make shared decisions. It is vitally important that all decision-makers have trust in activities performed by the HIDDIN workforce. For example, “perform exploratory, inferential, predictive, and causal data analysis” is a competency outcome currently in use for an existing master’s degree in biomedical informatics. This could translate into an EPA if the component parts are considered. First, to perform these analyses, a trainee would need to understand each type of analysis, including the data needed for each. Second, a trainee would need to understand when it is appropriate to use each type of analysis. Third, a trainee would either need to know how to calculate the analyses manually or how to use various software packages or programs to conduct the analyses. The level of training of the trainee would determine whether or not the trainee could complete this, as would the strictness of the supervisor. Some supervisors might expect the most concise method, while others may not be as stringent, indicating that any method coming to the right conclusion is sufficient. The context, i.e., the type of data and the reason for which the analysis is being completed are important. Finally, if the EPA were to perform exploratory analysis only, the EPA might be simpler, while conducting predictive analyses would be more complex. The supervisor could demonstrate the analyses, progressively giving the trainee more responsibility, until it becomes the responsibility of the trainee to supervise others with less training.

Accreditation In education, accreditation is a standards-based process that is applied to programmes of study to signify that the provider and the study programme meet the requirements of a profession or industry body. Harrison (2017, p11) explains the role and critical importance of accreditation systems: Accreditation in a variety of guises is the norm in a wide range of regulated professions, including architecture, education, engineering, law, medicine and nursing. It serves to validate any academic program for which successful completion is a necessary precondition for entrance

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into the profession…. Typically, the accreditation is conducted with reference to standards dictated by the accrediting agency. This is often a body representing the profession itself.

This quality assurance is over and above the generic procedures for accrediting all public post-secondary education providers, so that their degrees are additionally sanctioned by the professions they serve. Although the health information professions are not listed as one of the regulated professions in Harrison’s definition, the same logic applies. To qualify to enter and work in their chosen fields, graduates should have baseline levels of knowledge, skills, and attributes, i.e., competencies, as determined by their respective professional associations or industry bodies. Accreditation systems that refer to competencies or standards of professional practice recognise both the education providers and their programmes of study. By attaining a recognised qualification, an individual gains professional status and is deemed entrustable to practice as an appropriately qualified and competent professional; thus accreditation standards provide a measure of quality assurance to employers and the public. An example of the operation of such a system is the Australian Health Practitioner Regulation Agency (AHPRA), which works with national boards of 15 regulated (licensed) clinical health professions; for AHPRA, a health profession accreditation standard means: A standard used to assess whether a program of study, and the education provider that provides the program of study, provide persons who complete the program with the knowledge, skills and professional attributes to practise the profession in Australia. (AHPRA 2020, np)

In the clinical health professions, accreditation of a clinician’s professional status serves as a guarantee of public safety and quality care. In contrast, the HIDDIN occupational groups that are the focus of this chapter do not belong to the regulated health professions, and their education and training may not be subject to the same rigorous accreditation systems. In Australia, AHPRA uses the terminology “self-­ regulating” professions to cover health professional groups such as the HIDDIN workforce. However, given the potential impact of the digital information and systems that they manage—on clinical decision-making and on the lives of patients, the efficiency and accessibility of care, and the policy framework that surrounds clinical care—there is a strong argument that the same rigour and principles should apply across both clinical and HIDDIN professions. “Academic program accreditation in higher education is both a process and, if a successful process, a mechanism for external quality and integrity validation for education programs” (Feldman et al. 2020, p237). In other words, accreditation is a pathway towards professionalisation of the HIDDIN workforce. Independent bodies are integral to the accreditation process, allowing review from those outside educational facilities. This helps make the system more robust, ensures multiple viewpoints are considered, and offers a higher level of governance and rigour. A multi-disciplinary survey of accrediting organisation members of the US-based Council for Higher Education Accreditation found that, among the 26 of the 85 recognised accrediting organisations (30.5%) that responded, their involvement in competency-based education was minimal, modest, and responsive in nature, partly because most degree programmes are not competency-based (Eaton 2016). The

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situation for accreditation and competency-based education in the HIDDIN professions is not dissimilar from that described in Eaton’s survey. Of the four disciplines highlighted in this chapter, HIMs appear to be the most regulated, with strong course accreditation reporting lines and frameworks and, together with some health informatics courses, they have demonstrated an intention to follow a competency-based model of education and accreditation. An example is the Health Information Management Association of Australia (HIMAA), the formal accrediting body for all HIM courses in Australia; HIMAA accreditation of HIM degree courses is linked directly to HIMs’ eligibility for full graduate membership of HIMAA (2020). Likewise, the Canadian College of Health Information Management (CCHIM) is the national accrediting body for HIM education in Canada. Accreditation with CCHIM confirms that the educational facility is committed to self-assessment and external peer review in meeting or exceeding the standards and to continuously find new ways to enhance the quality of education and training provided (CCHIM 2020). In the United States, the Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM) is an accrediting organisation which has independent authority in all actions pertaining to accreditation of educational programmes in the fields of HIM and HI (Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM 2020)). The International Medical Informatics Association (IMIA) is an international accreditation body that operates separately from universities and local national professional associations. The association’s competency-based model is confirmed by the second of IMIA’s aims for accreditation of biomedical and health informatics (BMHI) programmes: “To ensure that the level and quality of educational programs offered by academic institutions of various types meet the IMIA recommendations on BMHI competencies” (Jaspers et  al. 2017). It is noted that IMIA accreditation provides educational BMHI programmes “with information about whether their curriculum, courses, and student competencies upon graduation meet a global standard”. In contrast, Health Information and Communications Technologist and Health Librarianship degree courses are rare—rather, people may undertake training via majors or specialisations within a more generic degree, and be accredited by overarching, generalist industry and professional bodies not specific to health. For instance, the Australian Computer Society (ACS) accredits ICT degrees, and the American Library Association (ALA) accredits librarianship degrees (ACS 2016; ALA 2020). This brief overview of accreditation and competency-based education has shown this to be an area of weakness for the HIDDIN professions in terms of asserting a health profession identity, although perhaps also it is an opportunity to innovate high quality but more agile forms of education and training than universities typically offer.

Professionalisation For the health professions in general, and for the HIDDIN workforce in particular, digital health technologies have had disruptive and transformational effects. New and changing models of care are a feature of the clinical digital healthcare environment,

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and these, in turn, have stimulated demand for increasingly specialised health information professionals who manage health data, information, and knowledge, as well as the technological infrastructure that underpins the delivery of digital services. What is clear is that there is a complex interplay amongst the stakeholder groups who have varied interests, roles, and responsibilities affecting the delivery and outcomes of digital health services. This fluidity has ramifications for the education of the HIDDIN workforce who need to continually learn and adapt to their changing environments. Although they are not legislated and regulated, there are educational qualifications that confer eligibility for membership of the respective professional associations. But regulatory drivers that are equivalent to regulated professions do not exist—for educational qualifications, continuing professional development or accreditation of education frameworks. Nor are employers legally required to employ credentialed professionals. In addition, the emerging trend towards alternative education pathways that are separate from the traditional post-secondary systems of professional education, carries a risk of deprofessionalisation. We cannot yet be sure that selectively skills-based, modular or “micro” approaches will deliver the competencies required for an individual to develop and practice as a professional. Unfolding in front of our eyes, the uneven approach to train and certify members of the HIDDIN workforce is a social experiment in determining who can be trusted with responsibility and accountability for the digital transformation of health.

 oles and Responsibilities: Specialisation, R Convergence, Overlap This chapter’s focus on competencies of the HIDDIN workforce has revealed a shifting terrain with increasing specialisation within the groups, as well as a blurring of boundaries between the groups. Roles and titles have changed, and job descriptions and responsibilities are evolving to fit the requirements of the workplace. In some cases the roles and associated competencies are becoming increasingly specialised and more highly refined, while in other cases, there are areas of overlap. These observations have highlighted two trends which appear to be developing in parallel and independently of each other: Firstly, within particular occupational groups, there is demand for the practitioners to have increasingly specialised and more highly refined skillsets. Secondly, there is a counter trend towards convergence between the groups, demonstrated in the blurring of boundaries, and featuring shared or overlapping areas of competence.

Competencies and Information Governance Comparing the roles and competencies of the HIDDIN workforce groups has highlighted an important responsibility that is shared across all groups—the area of information governance. All the information professions have a common interest in

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getting the right information to the right person at the right time and place and in the right format. This is at the core of information governance, which is about accountability and responsibility for decision-making, and establishing accountability frameworks, systems, and policies, to ensure the right decisions are made (i.e. decisions that are based on high quality information and data) following the correct procedures. The implication of this observation regarding information governance as an area of shared responsibility is that there is a need for a more coordinated and planned approach to workforce development in such a critical area.

 ducation and Training: Traditional Academic Accredited E Model or Alternative Pathways? Lack of clarity about roles and responsibilities with regard to managing health data, information, and knowledge in the digital workplace can be confusing for employers who may not know who they need to hire to do a particular job, nor how to develop their workforce strategically. It is also confusing for individuals, who may be planning their job progression and career advancement but unclear about where the job opportunities lie or where they can train for these positions. Employers have tried various approaches to “growing their own” including product- or vendor-specific training (e.g. using particular Electronic Medical Records systems) and on-the-job workplace learning solutions. These are often product or process-oriented and not designed with broader professional competencies in mind. In its “Workforce and Education Roadmap”, the Australian Digital Health Agency (2020, p12) has stated: “Workplace-specific education will play a critical role in driving the new ways of working necessary to achieve the benefits of new technologies”. The Agency notes that transformational and situational leadership capabilities will also be required to support the identification and successful delivery of digital health programmes that will cause significant disruption at the enterprise level. As well as causing disruption in the workplace, lack of clarity about roles and responsibilities for the contemporary HIDDIN workforce is also a significant issue for educators. In general terms, the responses to this issue of “skills deficit” have been uncoordinated and disjointed. A gap in communication between professions, employers, and educators has led to a mismatch between content-based educational courses which focus on learning outcomes, and the competency-based needs of a rapidly developing digital health workplace. Incorporating digital health competencies into post-secondary education courses has been cumbersome, and formal accreditation of post-secondary education programmes has not kept pace with industry requirements for a skilled workforce. Alternative education pathways are being forged. To fill the training and education gaps, industry and professional association-led certification and micro-credentialing initiatives have developed as more immediate responses to the needs of the workplace. Many of the training systems that deliver

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certification and micro-credentialing are, in fact, based on the various professional groups’ competency sets and may be delivered by accredited training providers. Thus although the equivalent rigorous accreditation standards may not be applied to these alternative education pathways, they may have some credibility as standards-­ based, quality-assured systems. It remains to be seen, however, whether or not there is an ongoing trend in a direction that is outside the traditional academic model of education for the regulated clinical health professions.

Conclusion This chapter has sought to explore the identity of the HIDDIN workforce from the perspective of their underpinning competencies. It has proposed that understanding their distinctive competencies and recognising their common interest and varying responsibilities in the area of information governance, will help to clarify and make their roles more visible in the broader digital health care environment. Stakeholder groups who have an interest in the competencies of the HIDDIN workforce comprise: universities and training organisations, professional associations/industry bodies, health workforce agencies, employers, managers, and health information practitioners themselves. Competency-based education pathways are essential if graduates are to be “job-ready” with the requisite skills, knowledge, and attributes that enable them to perform in the workplace, and to maintain standards of professional practice, continually update their knowledge base and improve their skills. It is, therefore, imperative that education providers and employers work together to ensure that their programmes are based on changing workforce needs. Our analysis has found a paucity of competency-based tertiary education programmes for the HIDDIN professions, which may have led to the apparent mismatch between the content-based education and training programmes offered by education providers and the needs of employers for graduates who are a good fit for the available jobs, as well as programmes for upskilling current employees. Alternative education pathways that may be competency-based include certificates and microcredentials. These are emerging as a more agile response to the needs of the workplace and it remains to be seen if they will replace the more traditional model of academic education, or develop in parallel or in partnership with it. This chapter suggests that in addition to implications for education and training of the health information workforce, there are higher order issues at stake. If the health field is to address future challenges in digital health and the quality and safety of care, gaps in quality assurance and competency-based accreditation of education for the health information professions need to be addressed. A seriously coordinated and systematic approach to strategic workforce development for the health information professions is needed.

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Chapter 6

Professional and Industry Certifications for the Health Information Workforce Kathleen Gray

Abstract  One way to identify individuals in the health information workforce is by the professional and industry certifications they hold. A wide variety of relevant certifications is available outside formal post-secondary education systems. This chapter outlines the major features of these certifications, what aspects of HIDDIN work they highlight, and what body of knowledge they signify. Comparing and contrasting these certifications raises critical considerations for individuals, employers of HIDDIN workers, and certification providers themselves. Analysis suggests that the professional and industry certification business is long on competition and short on quality assurance. This situation undermines the professional standing of a workforce that substantially relies on this means of demonstrating competence to practice. Keywords  Certification · Credential · Examination · Fellowship · Qualification

Introduction How is it possible to determine which individuals in the health information workforce hold bona fide certification of ability to do work of specific types and levels of complexity? Evidence from the literature and from surveying this workforce points to problems of invisibility and undifferentiation in the way that health information

K. Gray (*) Centre for Digital Transformation of Health, University of Melbourne, Melbourne, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_6

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practitioners work (Gray et al. 2019). Looking at distinguishing features of the specialised Health Informatics, Digital, Data, Information and kNowledge (HIDDIN) workforce around the world, what visibly differentiates a professional from an amateur or an expert from a novice? A plethora of relevant certifications awarded by professional societies and industry associations are available for individuals to pursue, to identify themselves for this purpose. Earning credentials of this kind is an avenue to workforce differentiation and career progression that has low academic barriers to entry and uncomplicated methods of measuring achievement. Typically, gaining and maintaining this type of credential relies on small, staged quanta of learning, and entails less breadth and depth of learning and assessment than is expected in post-­secondary education systems. For this reason, these are sometimes called micro-credentials or microcertifications (Ellis et al. 2016; NCVER 2018). This chapter considers the certification of HIDDIN workers in relation to formal post-secondary education, and in the context of certification of practitioners in the related fields of information technology and health care. It reviews an international selection of pertinent certifications awarded by professional societies and industry associations and compares them from both supply and demand perspectives. The overall aim is to make certification options and practices more understandable to current and intending practitioners, employers, and organisations that offer credentialing of practitioners.

Why and How Individuals Are Certified? In any field of work, if we wish to determine whether an individual has a specialised skill or capability, we rely in part on their own claims, and in part on independent verification of their credentials by a third-party service that provides credible evidence of the person’s knowledge, experience, skill, or other eligibility. We attach greater or lesser importance to independent verification, depending on how serious we consider the implications of an individual’s competence or the consequences of their incompetence. Society’s trust in a professional or industry certification rests not only on a systematic process for certifying individuals and keeping a record of who is and is not certified, but more fundamentally too, on a systematic process for deciding how their competency is framed, that is what knowledge, skills, and attributes they need to demonstrate. The International Standards Organisation standard ISO/IEC 17024 (2012) sets out general requirements for good practice by “bodies offering certification of persons”, so that there can be public confidence in certification schemes and understanding of how they relate to each other. The standard also points out that “it is necessary to distinguish between situations where certification schemes for persons are justified and situations where other forms of qualification are more appropriate” and that “alternatives to certification can still be necessary in positions where public services, official or governmental operations are concerned”.

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Across the HIDDIN workforce, verification of competence to practice may come in the form of a specific qualification awarded by a post-secondary education and training provider, or not. Even if formal educational qualifications are available (not always the case), labour shortages open opportunities to move into this workforce without prior formal education in the field. Historically, many people have entered the HIDDIN workforce opportunistically, becoming skilled by learning on the job or self-taught through personal interest; included among them are some of the most senior and respected figures in HIDDIN communities of practice. Thus, in these communities, there is a persistent tension between academic and industry bona fides of competence (Eckman 2017). Professional and industry credentialing of individuals in the HIDDIN workforce mirrors a more general social trend to value industry-recognised certificates as a complement or alternative to academic qualifications. Rapidly rising demand for specialised skills, combined with the Internet’s facility to support learning interactions and transactions, are driving post-secondary institutions and other organisations to be entrepreneurial in offering credentialing services to individuals who are ambitious to signal their competence to do specific kinds of work (Kato et al. 2020). Among credentialing service providers, terminology, standards, and quality of service are uneven. Thus, there are many nuances to negotiate in HIDDIN workforce credentialing, as shown in an example from the USA: “The terms “certification” and “designation” are usually used interchangeably […]. A professional certification (e.g. PMP, CPHIMS) typically validates that an individual has attained the knowledge and skills necessary for competent practice in a particular profession. A professional designation (e.g. CRNBC, FRCPC) conveys a continuing competency as measured by the issuing national or international professional governing body and as managed by the individual on a regular basis” (Davis 2017). Workforce globalisation and transboundary job markets are a growing consideration for practitioners, employers, and credential providers. However key certification concepts do not translate directly from one country to another because of differences in national systems of education and of healthcare. Therefore, this chapter follows the OECD definition, in which the term “certification” is used to describe recognition by a professional or industry body. This is distinct from a certificate awarded by a post-­secondary education institution upon completion of an academic credit-bearing study program (Kato et al. 2020).

Comparisons with Health Care and Information Professions Where the stakes of individual performance or incompetence are high, society expects high accountability for the management and governance of certification. So, what can we learn from the way that related professions such as healthcare and information technology meet society’s expectations of practitioners? If the HIDDIN workforce aspired to be included among the health professions, the landscape of individuation certification would need to have sharp definition and tight boundaries. Serious attention is paid to the validity and the scope of health

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practitioner credentials in the interests of safety and quality in healthcare (for example ACSQHC 2015). Standards for registering or licensing individuals to practice are applied through an agency of government (for example, the Australian Health Practitioner Regulation Agency). Though there are many approaches to describing professional competence in those professions, the concept of entrustable professional activity has become a clear way to integrate the type and level of competence of the individual, with the work that needs to be done and the degree of oversight required to do that work legally (Ten Cate et al. 2015). Thus, although a registered doctor/ nurse/physiotherapist might undertake some form of certification in nutritional therapy/ultrasound/acupuncture as continuing professional development, that certification alone would not suffice for the individual to practice legally as a registered professional dietitian/medical radiation practitioner/Chinese medicine practitioner. The HIDDIN workforce has a clear affinity with the information professions, too, for example, De Almeida et  al. (2019) found that many health information management professionals are employed in roles such as computer and information research scientists, database administrators, information security analysts, statisticians, and computer occupations. How might HIDDIN certification processes and standards be akin to those professions? Here a different model of trust applies, with a balance very different to the health professions of how individuals acquire competence through learning on, near, or off the job. The peak organisations in the information professions have reached a consensus on certification in major bodies of knowledge (BoK), that is, the prescribed knowledge and skill that an individual must acquire to be certified as a practitioner in a particular area: The Skills Framework for the Information Age (sfia-­online.org) lists 40 BoKs that are the internationally accepted definitions of data science, development and operations (DevOps), digital transformation, information and cybersecurity, and software engineering, along with the organisations that curate certification in each field. By comparison, HIDDIN work rests on a patchwork of curriculum and competency standards that vary greatly around the world, undergo almost no regulation or quality assurance, and are not locatable in a shared framework. Some work in this direction can be seen in a handful of papers over the past two decades: Moore and Berner (2003) compared health informatics graduate program curricula against two sets of professional criteria. Gibson et al. (2015) examined convergence between health informatics and health information management. In 2017, separate papers by Newbold (2017) and by McCormick et  al. (2017) itemised certifications relevant to nursing informatics. Nevertheless, there is no universally agreed or observed body of knowledge, or process for demonstrating mastery, to give coherence to the level of competence that an individual has, to do HIDDIN work of defined scope and responsibility.

Certifications for the HIDDIN Workforce This chapter outlines the terrain of professional and industry certifications for individuals in the HIDDIN workforce. To locate and describe these certifications, firstly search terms were formulated based on the concepts and sources referred to in

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previous sections of this chapter. Then, searching of the peer-reviewed literature and grey literature 2000–2020 was completed at the end of 2018 (retrieving, for example, McCormick et  al. 2017; Zozus et  al. 2017), followed by additional Google searching to the end of 2019 (retrieving, for example, Tittel and Kyle 2019). In the first half of 2020, personal communications with professional and industry colleagues helped to refine lists and add details. Excluded were certifications where no information or documentation was openly accessible online, in English, from the primary provider source; a few peer-reviewed sources refer to certifications of this kind (e.g. Gundlapalli et al. 2015; Cummins et al. 2016; Lytle 2017). Also excluded were continuing professional development programs that do not directly award professional or industry certification—examples are the US National Library of Medicine Biomedical Informatics training program (https://www.nlm.nih.gov/bsd/ disted/nlm_bmi_training.html) and many massive open online courses (MOOCs) in the HIDDIN domain. Also excluded were certifications by commercial organisations that accredit individuals to work with their products; examples include 3M™ Health Information Systems Certifications (https://www.3m.com/3M/en_US/ health-­information-­systems-­us/resources/health-­care-­academy/certifications/); Intersystems’ HealthShare Health Connect HL7 Interface Specialist (https://www. intersystems.com/support-­learning/learning-­services/certification/healthshare-­ health-­connect-­hl7-­interface-­specialist/); and different types of Epic certification (Sok 2019). Lastly, excluded were not-for-academic-credit certificates offered by institutions that are primarily providers of formal post-secondary education and training; it is noted that growing numbers of universities around the world are engaging in this kind of activity. Over 50 certifications, offered by over 40 organisations, are summarised in Table 6.1, showing the primary information source, minimum descriptors of high-­ level knowledge and skill areas, and acronyms widely used to refer to these.

Table 6.1  Professional and industry certifications available to individuals in the HIDDIN workforce Credential name Credentialing organisation Information source Advanced Analytics for Health Care Strategists Society for Health Care Strategy & Market Development https://www.shsmd.org/education/ dgital-­badge-­certificate-­programs Advanced Health Informatics Certification AHIC; Certified Health Informatics Professional ACHIP American Medical Informatics Association AMIA https://www.amia.org/ advanced-­health-­informatics-­certification

Outline of knowledge and skills certified • New and emerging data sets • Changing landscape of inpatient and ambulatory world • Rapid, market-specific strategic decision making • Data visualisation and storytelling • Foundational knowledge • Enhancing health decision-making, processes and outcomes Health information systems Data governance, management and analytics Leadership, professionalism, strategy and transformation (continued)

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Table 6.1 (continued) Credential name Credentialing organisation Information source Associate/Member/Fellow Faculty of Clinical Informatics https://facultyofclinicalinformatics.org.uk/ how-­to-­apply

Outline of knowledge and skills certified • Experiential exposure to clinical informatics in the workplace • Contributions to advancement of clinical informatics • Leadership • Commitment/engagement Associate/Fellow of the Australasian Institute • Educational qualifications of Digital Health AFAIDH/FAIDH (formerly • Paid and honorary positions Member/Fellow of the Australasian College of • Community achievements • Research and development Health Informatics FACHI/MACHI) Australasian Institute of Digital Health AIDH https://digitalhealth.org.au/membership/ become-­a-­fellow-­of-­the-­institute/ • License to practice medicine Board-Certified Clinical Informatician BC • Other board certification American Board of Preventive Medicine • Practice experience ABPM and American Board of Pathology ABP on behalf of American Board of Medical • References • Graduate degree in informatics Specialties ABMS https://www.theabpm.org/become-­certified/ subspecialties/clinical-­informatics/; http:// www.abpath.org/index.php/to-­become-­ certified/requirements-­for-­certification?id=40 • Foundations of practice—professional Board-Certified Informatics Nursing practice Registered Nurse RN-BC American Nurses Credentialing Center ANCC • Foundations of practice—models and theories • Foundations of practice—rules, regulations, https://www.nursingworld.org/our-­ and requirements certifications/informatics-­nurse/ • System design life cycle—planning and analysis • System design life cycle—designing and building • System design life cycle—implementing and testing • System design life cycle—evaluating, maintaining, and supporting • Data management and health care technology—data standards • Data management and health care technology—data management • Data management and health care technology—data transformation • Data management and health care technology—hardware, software, and peripherals

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Table 6.1 (continued) Credential name Credentialing organisation Information source Certified Associate/Professional in Healthcare Information and Management Systems CAHIMS/CPHIMS Healthcare Information and Management Systems Society HIMSS http://www.himss.org/health-­it-­certification/ cahims; https://www.himss.org/sites/hde/files/ CPHIMS%20Handbook%202019.09%20 Final.pdf Certified Classification and Coding Specialist CCCS Canadian College of Health Information Management https://www.echima.ca/CCHIM/ classification-­coding-­certification

Certified Clinical Data Manager CCDM Society for Clinical Data Management https://scdm.org/get-­certified/ Certified Clinical Documentation Improvement Specialist CCDIS Canadian College of Health Information Management https://www.echima.ca/CCHIM/CDI

Certified Coding Associate CCA American Health Information Management Association AHIMA https://www.ahima.org/certification/CCA

Certified Coding Specialist/-Physician-based CCS/CCS-P American Health Information Management Association AHIMA https://www.ahima.org/certification/CCS; https://www.ahima.org/certification/ccsp

Outline of knowledge and skills certified • General—healthcare environment • General—technology environment • Systems—analysis • Systems—design • Systems—selection, implementation, support, and maintenance • Systems—testing and evaluation • Systems—privacy and security • Administration—leadership • Administration—management • Fundamentals • General medicine • Neoplasms • Interventions • Obstetrics and newborns • Iatrogenic disorders, trauma, related conditions • Case scenarios • Project management ... CRF design • Processing lab data … Database updates • SAE reconciliation … Application of randomisation schemes • Anatomy and physiology, clinical pathology, pharmacology, and medical terminology • Clinical coding skills • Documentation improvement • Leadership, communication, and education skills • CDI metrics and analytics • Clinical classification systems • Reimbursement methodologies • Health records and data content • Compliance • Information technologies • Confidentiality and privacy • Clinical documentation • Diagnosis coding • Procedure coding • Reporting requirements for provider-based services • Reporting requirements for inpatient services • Reporting requirements for outpatient services • Data quality management • Health information technology • Privacy, confidentiality, legal, and ethical issues • Compliance (continued)

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Table 6.1 (continued) Credential name Credentialing organisation Information source Certified Documentation Expert Outpatient CDEO American Association of Professional Coders AAPC https://www.aapc.com/certification/cdeo/

Outline of knowledge and skills certified • Purpose of CDI • Provider communication and compliance • Clinical conditions • Diagnosis coding • Documentation requirements • Payment models • Procedure coding • Quality measures • Clinical coding practice Certified Documentation Improvement • Leadership Practitioner CDIP • Record review and document clarification American Health Information Management • CDI metrics and statistics Association AHIMA • Research and education https://www.ahima.org/certification/cdip • Compliance • Business needs assessment Certified Health Data Analyst CHDA • Data acquisition and management American Health Information Management • Data analysis Association AHIMA • Data interpretation and reporting https://www.ahima.org/certification/chda • Data governance • Information and communication technology Certified Health Informatician Australasia • Health and biomedical science CHIA Australasian Institute of Digital Health AIDH • Information science http://www.healthinformaticscertification.com/ • Management science • Core principles and methods • Human and social context • Specialisations [to be developed] • Health IT Certified Health Informatics Systems • Health care regulations Professional CHISP • Computer science American Society of Health Informatics • Medical insurance billing Managers ASHIM http://www.ashim.com/health-­it-­certification/ • Information and data security • Medical terminology and anatomy • Operational principles in healthcare • Data mining, reports, and queries • Technology in quality of care • Biomedical sciences Certified in Health Information Management • Health care systems in Canada CHIM • Information systems and technology Canadian College of Health Information • Management Management CCHIM • Ethics and practice https://www.echima.ca/cchim/certification • Practicum

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Table 6.1 (continued) Credential name Credentialing organisation Information source Certified Health Information Manager/ Practitioner CHIM/CHIP Health Information Management Association of Australia HIMAA https://himaa.org.au/ professional-­credentialing/

Outline of knowledge and skills certified • Generic professional skills • Health information and records management • Language of medicine • Healthcare terminologies and classification • Research methods • Health services organisation and delivery • Health information law and ethics • eHealth • Health information services organisation and management • Other related qualifications and/or experience Certified Healthcare Chief Information Officer • Organisational vision and strategy • Technology management CHCIO • Change management College of Healthcare Information • Value assessment and management Management Executives CHIME • Service management https://chimecentral.org/wp-­content/ uploads/2020/01/CHCIO-­Brochure-­July-­2020. • Talent management • Relationship management pdf • Policies and solutions Certified Healthcare Information Security • Strategic planning Leader CHISL • Situations and responses College of Healthcare Information • Emerging issues, theory, and practice Management Executives CHIME • Risk management https://chimecentral.org/certification/chisl/ • Coordination of operations • Ethical, regulatory, and legal issues • Collaborative business continuity/disaster recovery • Ethical, legal, regulatory issues/environmental Certified in Healthcare Privacy and Security assessment CHPS • Program management and administration American Health Information Management • Information technology/physical and Association AHIMA technical safeguards https://www.ahima.org/certification/chps • Investigation, compliance, and enforcement • Professional values and capabilities Certified Healthcare Simulation Educator/ • Health care and simulation knowledge and Advanced CHSE/CSHE-A principles Society for Simulation in Healthcare https://www.ssih.org/Portals/48/Certification/ • Educational principles and activities CHSE_Docs/CHSE%20Handbook.pdf; https:// • Simulation resources and environments • Scholarship and teaching www.ssih.org/Portals/48/Certification/ CHSE-­A_Docs/CHSE-­A%20Handbook.pdf • Concepts in health care as applied to Certified Healthcare Simulation Operations simulation Specialist CHSOS • Simulation technology operations Society for Simulation in Healthcare https://www.ssih.org/Portals/48/Certification/ • Healthcare simulation practices/principles/ procedures CHSOS_Docs/CHSOS%20Handbook.pdf • Professional role: behaviour and capabilities • Concepts in instructional design as applied to simulation (continued)

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Table 6.1 (continued) Credential name Credentialing organisation Information source Certified Healthcare Technology Specialist CHTS & subspecialties: -TS, -PW, -IM, -CP, -IS, -TR American Health Information Management Association AHIMA http://www.ahima.org/~/media/AHIMA/Files/ Certification/CHTS%20Candidate%20Guide. ashx Certified Imaging Informatics Professional CIIP American Board of Imaging Informatics https://www.abii.org/Certification-­Overview. aspx

Certified Inpatient Coder CIC American Academy of Professional Coders AAPC https://www.aapc.com/certification/cic/

Certified Outpatient Coder COC Formerly CPC-H American Academy of Professional Coders AAPC https://www.aapc.com/certification/coc/

Certified Physician in BioMedical Informatics CPBMI Korean Society of Medical Informatics KOSMI http://www.cpbmi.or.kr/

Outline of knowledge and skills certified [discontinued]

• Procurement • Project management • Operations • Communications • Training and education • Image management • Information technology • Systems management • Clinical engineering • Medical imaging informatics • Medical record and healthcare documentation guidelines • Medical terminology, anatomy, and pathophysiology • Inpatient coding • Inpatient payment methodologies • Outpatient payment methodology • Regulatory and payer requirements • Compliance • Coding cases • Medical terminology • Anatomy • Coding guidelines • Payment methodologies • Compliance • ICD-10-CM • CPT® • HCPCS level II coding • Surgery and modifiers [Details unavailable in English]

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Table 6.1 (continued) Credential name Credentialing organisation Information source Certified Professional Coder (CPC) American Academy of Professional Coders AAPC https://www.aapc.com/certification/cpc/

Certified Professional in Electronic Health Records CPEHR Health IT Certification LLC http://www.healthitcertification.com/overview. html Certified Professional in Health Informatics CPHI American Health Information Management Association AHIMA https://www.ahima.org/education/ health-­informatics Certified Professional Health Specialisation AALIA (CP) Health Librarian or AALIA (CP) Health or ALIATec (CP) Health or ALIA Allied Field (CP) Health Australian Library and Information Association Health Libraries Australia Group ALIA HLA https://membership.alia.org.au/pdinfo/ specialisations/health-­specialisation

Outline of knowledge and skills certified • Surgical procedures performed on the integumentary system • Surgical procedures performed on the musculoskeletal system • Surgical procedures performed on the respiratory system, cardiovascular system, hemic and lymphatic systems, and mediastinum and diaphragm • Surgical procedures performed on the digestive system • Surgical procedures performed on the urinary system, male reproductive system, female reproductive system, and endocrine system • Surgical procedures performed on the nervous system • Evaluation and management • Place of services • Anesthesia • Radiology • Laboratory/pathology • Medicine • Medical terminology • Anatomy • ICD-10-CM/diagnosis • HCPCS Level II • Coding guidelines • Compliance and regulatory [transferred to HIMSS]

[discontinued]

• Health environment • Reference and research services • Resources • Leadership and management • Digital, ehealth, and technology • Health literacy and teaching • Health research • Professionalism (continued)

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Table 6.1 (continued) Credential name Credentialing organisation Information source Certified Professional in Information Exchange CPHIE Health IT Certification LLC http://www.healthitcertification.com/overview. html Certified Professional in Healthcare Information and Management Systems– Canada CPHIMS-CA Digital Health Canada https://digitalhealthcanada.com/wp-­content/ uploads/2019/12/Digital-­Health-­Canada-­ Competency-­Requirements-­Exam-­Version-­1. pdf Certified Professional in Healthcare Quality CPHQ National Association for Healthcare Quality— Certification Commission https://nahq.org/wp-­content/uploads/ attachments/CPHQ_Content_outline_-­_ effective_01-­2018.pdf

Outline of knowledge and skills certified [transferred to HIMSS]

• Information management • Technology ecosystem • Clinical and health services • Canadian health system • Healthcare transformation • Project management

• Organisational leadership—structure and integration • Organisational leadership—regulatory, accreditation, and external recognition • Organisational leadership—education, training, and communication • Health data analytics—design and data management • Health data analytics—measurement and analysis • Performance and process improvement— identifying opportunities for improvement • Performance and process improvement— implementation and evaluation • Patient safety—assessment and planning • Patient safety—implementation and evaluation Certified Professional Medical Auditor CPMA • Medical record standards and documentation guidelines American Association of Professional Coders • Coding and documentation compliance AAPC guidelines https://www.aapc.com/certification/cpma.aspx • Coding and reimbursement concepts • Scope and statistical sampling methodologies • Medical record auditing abstraction • Category risk analysis and communication [transferred to HIMSS] Certified Professional in Operating Rules Administration CPORA Health IT Certification LLC http://www.healthitcertification.com/overview. html

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Table 6.1 (continued) Credential name Credentialing organisation Information source Certified Risk Adjustment Coder CRC American Association of Professional Coders AAPC https://www.aapc.com/certification/crc/

Certified Specialist Health Interpreter CSHI National Accreditation Authority for Translators and Interpreters https://www.naati.com.au/become-­certified/ certification/ certified-­specialist-­health-­interpreter/ Certified Terminology Standards Specialist CTSS Canadian Health Information Management Association CHIMA https://www.echima.ca/college/certification/ ctss/; https://www.uvic.ca/hsd/hinf/graduate/ certificate/HTS-­certificate-­overview-­ nov12-­2019.pdf Digital Marketing Strategy in Healthcare Society for Health Care Strategy & Market Development SHSMD https://www.shsmd.org/education/ digital-­badge-­certificate-­programs

Outline of knowledge and skills certified • Compliance • Diagnosis coding • Documentation improvement • Pathophysiology/medical terminology/ anatomy • Purpose and use of risk adjustment models • Quality care • Risk adjustment models • Medical terminology • General medical knowledge • Knowledge of health systems • Ethics, culture, and the role of the interpreter • Advanced interactional management • Research and preparation • Health information standards • Controlled terminology standards • Health information exchange standards • Field project in health informatics

• Building the foundation of a successful contemporary digital marketing program in health care • Telling your story to drive results • Activating your message across the tradigital landscape • Preparing for what is coming next • Education Fellow of the American Medical Informatics • Certification Association FAMIA • Applied informatics experience American Medical Informatics Association • Peer recommendation AMIA • AMIA membership https://www.amia.org/famia • AMIA engagement • Future commitments Fellow of the Health Information Management • Qualifications • CV and references Association of Australia FHIMAA Health Information Management Association • Service to the organisation • Contribution to the profession of Australia HIMAA • Presentations made/articles published https://himaa.org.au/wp-­content/ • Level of knowledge and expertise uploads/2019/10/MEMB_HIMAA-­ MembershipApplicationForm_ FellowMember_7March2016.pdf (continued)

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Table 6.1 (continued) Credential name Credentialing organisation Information source Fellow of the International Academy of Health Sciences Informatics FIAHSI International Medical Informatics Association IMIA https://imia-­medinfo.org/wp/ iahsi-­nomination-­process-­for-­new-­fellows/ Foundation Certified Healthcare Executive CFCHE College of Healthcare Information Management Executives CHIME https://chimecentral.org/wp-­content/ uploads/2017/04/CFCHE-­Exam-­Blueprint.pdf HL7 Clinical Document Architecture Specialist CDA Health Level 7 HL7 http://www.hl7.org/documentcenter/public/ training/HL7%20CDA%20Certification%20 Study%20Guide.pdf HL7 Fast Healthcare Interoperability Resources FHIR R4 Proficiency Certificate Health Level 7 HL7 http://www.hl7.org/documentcenter/public/ training/HL7%20FHIR%20Proficiency%20 Study%20Guide.pdf

HL7 Version 2 Control Specialist V2 Health Level 7 HL7 http://www.hl7.org/documentcenter/public/ training/2.8%20Cert%20exam%20study%20 tip%20sheet_%20PDF%20Final.pdf

HL7 Version 3 Reference Information Model Specialist V3RIM Health Level 7 HL7 http://www.hl7.org/documentcenter/public/ training/RIM%20Version%203%20Study%20 Guide.pdf

Outline of knowledge and skills certified • Accomplishment • Recognition • Global engagement • CV • Publications • Health care vision and strategy • Technical proficiency • Change management • Value commitment • Hospital and healthcare expertise • Relationship building and collaboration • CDA overview • CDA document and CDA header • Body choice, section- attributes, participants, relationships, and narrative block entry acts • Entry relationships • Other topics—technical artifacts, document exchange, CDA context • FHIR fundamentals • Resource concepts • Exchange mechanisms (includes RESTful API) • Conformance and implementation guidance • Terminology • Representing health care concepts using FHIR resources • Safety and security • FHIR maintenance process • FHIR licensing and IP • Conceptual approach of HL7 • HL7 message elements, separators, and lengths 3. • Construction and processing of HL7 messages • Special HL7 protocols 5. • Acknowledgment messages • Message control segments • HL7 data types and their uses • Conformance using message profiles • Other issues • Introduction and overview • Foundation subject area diagram • Other subject area diagrams and state transition diagrams • Act, act-relationship, and participation core classes • Entity, role, and role-link core classes • Specialisations of core classes • Data types • Normative structural vocabulary

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Table 6.1 (continued) Credential name Credentialing organisation Information source HealthCare Information Security and Privacy Practitioner HCISPP Information Security Consortium (ISC)2 https://www.isc2.org/hcispp/default.aspx

Healthcare IT Technician Computing Technology Industry Association CompTIA https://www.comptia.org/blog/why-­comptia-­ healthcare-­it-­technician-­ Member/Senior Member/Distinguished Member Academy of Health Information Professionals AHIP https://www.mlanet.org/page/ first-­time-­applicant-­member,-senior,distinguished Practitioner/Senior Practitioner/Advanced Practitioner/Leading Practitioner Federation for Informatics Professionals in Health and Social Care FEDIP https://uploads-­ssl.webflow.com/5af421f1debb da36454953c8/5c5c442fb7e4f65e4e0e2775_ fedip-­standard.pdf Registered Health Information Administrator/ Technician RHIA/RHIT American Health Information Management Association AHIMA http://www.ahima.org/~/media/AHIMA/Files/ Certification/Revised%20Candidate%20 Guide%20November%202019.ashx Senior Member/Fellow Healthcare Information and Management Systems Society HIMSS https://www.himss.org/membership-­ participation/member-­advancement

Outline of knowledge and skills certified • Health care industry • Information governance in healthcare • Information technologies in healthcare • Regulatory and standards environment • Privacy and security in healthcare • Risk management and risk assessment • Third-party risk management [discontinued]

• Postgraduate degree that meets competency standards • Full-time professional work experience • Professional accomplishments, continuing education • Professional association participation • Professional competence [9 standards × 4 levels] • Context—patient care and wellbeing • Context—roles within health and care • Context—health and care terminology • Context—health informatics roles and interactions • Data content, structure, and information governance • Access, disclosure, privacy, and security • Data analytics and use • Revenue cycle management • Compliance • Leadership • Professional capability • Experience • Leadership • Service • Qualifications • Job description • References

Discussion The range of professional and industry bodies that provide certification services, and the variety of HIDDIN knowledge and skills that can be certified, attest to a complex specialised workforce, but also a workforce where scopes of practice and levels of expertise are not at all distinct. Points that differentiate these certifications for prospective applicants include: whether eligibility to apply hinges on a prior

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university degree or prior period of work experience, and how strict that requirement is; whether there is a written examination, and whether invigilated; whether a more advanced level of certification requires a portfolio of practical work and referees. Key points of difference among providers include: the fee charged for certification; the duration of certification and the recertification process; whether multiple nested or linked certifications are offered; whether the certifications are jurisdiction-­ specific or have international recognition. In terms of certification quality and value, most providers cannot readily be seen to address ISO standards for certification of individuals; for example, publicly available descriptions of the body of knowledge often lack details of scope and depth, are undated, and do not explain development processes or identify contributors. It is rare to find scholarly or industry reports with detailed analyses or evaluations of certification programs (such as Gadd et al. 2016). Some gaps in what is certified are evident here and can be compared with those noted elsewhere. The need for the HIDDIN workforce to be competent to work with genomic data to advance precision healthcare (noted in McCormick et al. 2017) is not yet well addressed. A focus on ethics can be found at the top level in several certifications in the inventory here, contrary to views that certification of HIDDIN work lacks this (such as Kluge et al. 2018). Artificial intelligence is a newly identified area of specialised skill where planning is in early stages, “to credential healthcare professionals and anyone else who seeks a greater understanding of the growing role and use of artificial intelligence, machine learning and deep learning in health care” (ABAIM 2020). Some inclusions in Table 6.1 are more marginal than others. Including certifications that are emergent (e.g. Advanced Health Informatics Certification) and defunct (e.g. those merged into HIMSS after 2017) makes the point that HIDDIN certifications come and go. Translator and quality manager certifications were deemed to have enough overlap with HIDDIN work to be included, illustrating the potential for scope creep in HIDDIN work. Indistinct language about digital badges as certifications was allowed in the case of a marketing certification, to note current interest in digital badging. A certification service run jointly by a professional society (CHIMA) and a university was included, to show a new form of convergence in the certification provider community. Some certifications and providers may have been missed due to a lack of international consensus and coherence in what is offered. There is scope for much further depth in the comparative analysis of certifications, synthesis of their underlying knowledge base, and visualisation of the entire terrain in a way that makes it more accessible to “outsiders”. There is also a great need for independent research into certification holders’ experiences, employers’ evaluation of certified HIDDIN workers, and the efficiency and effectiveness of the varied assessment systems and processes used to certify and recertify individuals.

Conclusions Analysis of professional and industry credentials provides real-world insights into the body of knowledge and standards of practice of the HIDDIN workforce. The HIDDIN certification landscape is constantly shifting, for two main reasons:

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Certification programs are a business activity, where organisations compete to persuade employers and prospective employees to prefer their product or brand. Also, while much HIDDIN work is done within a regional or national healthcare system and its policy and infrastructure, an emerging consequence of digital transformation is a global health services sector that needs a workforce with trans-border expertise. The certifications listed in this chapter go some way towards answering the questions: What sort of expertise do we need? What guarantees are there that an individual has this expertise? These are fundamental questions for assuring the safety and quality of HIDDIN work that underpins the healthcare sector broadly. However, a more detailed mapping of the certification terrain, and more rationalisation of its features, would help to position the HIDDIN workforce to be seen to have the trustable approaches to self-regulation and self-knowledge that we observe in healthcare and information professions. Rationalisation poses political challenges to the industry and professional associations that presently hold pieces of the puzzle of credentialing health information professionals—accreditation, certification, curriculum standards, competency systems. Undoubtedly improvements on the current situation would be appreciated by health workforce policymakers and employers; HIDDIN workforce education and training providers; and not least, current and prospective practitioners.

References ABAIM American Board of Artificial Intelligence in Medicine. Press release. 2020. https://www. aithority.com/machine-­learning/abaim-­aims-­to-­educate-­and-­certify-­healthcare-­professionals-­ in-­ai-­and-­related-­technologies/. Accessed 31 Mar 2021. ACSQHC Australian Commission on Safety and Quality in Health Care. Credentialing health practitioners and defining their scope of clinical practice: a guide for managers and practitioners. ACSQHC: Sydney; 2015. Cummins MR, Gundlapalli AV, Murray P, Park HA, Lehmann CU. Nursing informatics certification worldwide: history, pathway, roles, and motivation. IMIA Yearbook. 2016:264–71. Davis S. Seeking an advanced professional designation. Chapter 20. In: Handbook of continuing professional development for the health IT professional. Boca Raton: CRC Press; 2017. DeAlmeida DR, Houser SH, Wangia-Anderson V, Fenton SH, Hazelwood A, Barefield AC, Freeman JM, Jones LM, Bakuzonis K, Hamada DL. An exploratory study demonstrating the health information management profession as a STEM discipline. Perspect Health Inf Manag. 2019;16(Summer). Eckmann J. Earning a certificate to demonstrate competency. Chapter 8. In: Handbook of continuing professional development for the health IT professional. Boca Raton: CRC Press; 2017. Ellis LE, Nunn SG, Avella JT. Digital badges and micro-credentials: historical overview, motivational aspects, issues, and challenges. In: Foundation of digital badges and micro-credentials. London: Springer; 2016. p. 3–21. Gadd CS, Williamson JJ, Steen EB, Fridsma DB. Creating advanced health informatics certification. J Am Medical Inform Assoc. 2016;23(4):848–50. Gibson CJ, Dixon BE, Abrams K. Convergent evolution of health information management and health informatics: a perspective on the future of information professionals in health care. Appl Clin Inform. 2015;6(1):163. Gray K, Gilbert C, Butler-Henderson K, Day K, Pritchard S. Ghosts in the machine: identifying the digital health information workforce. In: Improving usability, safety and patient outcomes with health information technology. Stud Health Technol Inform. 2019;257:146–51.

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Gundlapalli AV, Greaves WW, Kesler D, Murray P, Safran C, Lehmann CU. Clinical informatics board specialty certification for physicians: a global view. In: eHealth-enabled health: Proceedings of the 15th World Congress on Health and Biomedical Informatics. Stud Health Technol Inform. 2015;216:501–5. ISO International Standards Organisation ISO/IEC 17024:2012. Conformity assessment  — General requirements for bodies operating certification of persons. 2012. https://www.iso.org/ obp/ui/#iso:std:iso-­iec:17024:ed-­2:v1:en. Accessed 31 Mar 2021. Kato S, Galan-Muros V, Weko T. The emergence of alternative credentials. OECD Directorate for Education and Skills. Working Paper 216. 2020. https://www.oecd.org/officialdocuments/pub licdisplaydocumentpdf/?cote=EDU/WKP(2020)4&docLanguage=En. Accessed 31 Mar 2021. Kluge EH, Lacroix P, Ruotsalainen P.  Ethics certification of health information professionals. Yearbook Medical Inform. 2018;27(1):37–40. Lytle KS. Differentiating yourself with a professional certification. Chapter 9. In: Handbook of continuing professional development for the health IT professional. Boca Raton: CRC Press; 2017. McCormick KA, Gugerty B, Sensmeier J. Comparison of professional informatics-related competencies and certifications. On-Line J Nurs Inform. 2017;21(1). Moore RA, Berner ES. Comparing health/medical informatics graduate program curricula against two sets of professional criteria. J Healthc Inf Manag JHIM. 2003;18(3):44–50. NCVER National Centre for Vocational Education Research VOCEDplus. Focus on micro-­ credentials. 2018. https://www.voced.edu.au/focus-­micro-­credentials. Accessed 31 Mar 2021. Newbold SK. What practicing nurses need to know about health information technology in order to practice today: continuing education and certification. In: Forecasting informatics competencies for nurses in the future of connected health. Amsterdam: IOS Press; 2017. p. 229. Sok S. Different types of Epic modules and highest demand certifications. GlobalHealthIT. 2019. https://www.globalhit.com/different-types-of-epic-modules-and-highest-demand-certifications/. Accessed 31 Mar 2021. Ten Cate O, Chen HC, Hoff RG, Peters H, Bok H, van der Schaaf M. Curriculum development for the workplace using entrustable professional activities (EPAs): AMEE guide no. 99. Medical Teacher. 2015;37(11):983–1002. Tittel E, Kyle M. 5 Best healthcare IT certifications 2019. Business News Daily. 2019. https:// www.businessnewsdaily.com/10788-­healthcare-­it-­certifications.html. Accessed 31 Mar 2021. Zozus MN, Lazarov A, Smith LR, Breen TE, Krikorian SL, Zbyszewski PS, Knoll SK, Jendrasek DA, Perrin DC, Zambas DN, Williams TB, Pieper CF. Analysis of professional competencies for the clinical research data management profession: implications for training and professional certification. J Am Medical Inform Assoc JAMIA. 2017;24(4):737–45.

Chapter 7

Professional Learning and Development for the Health Information Workforce Joseph Crawford and Kerryn Butler-Henderson

Abstract The Health Informatics, Digital, Data, Information, and kNowledge (HIDDIN) workforce is faced with remaining current and relevant in a landscape experiencing rapid change. This chapter provides a holistic learner, employer, and educator perspective to understand the principles of effective professional learning and development. Concepts of evidence-based practice, constructive alignment, and pedagogy are applied to develop a framework for a principles-based assessment of learning environments and activities. Such a framework is needed by learners, to evaluate available professional learning opportunities more critically. Likewise, employers need to evaluate currency and relevance of learning outcomes prior to investing time and resources into professional development programmes. Lastly, providers of professional development can benefit from self-review against a shared set of quality criteria. Keywords  Professional development · Professional learning · Constructive alignment · Professionalism · Educational quality

J. Crawford (*) Academic Division, University of Tasmania, Launceston, TAS, Australia e-mail: [email protected] K. Butler-Henderson Digital Health Hub, College of STEM, RMIT University, Bundoora, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_7

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Introduction What does it mean, for someone in the Health Informatics, Digital, Data, Information, and kNowledge (HIDDIN) workforce, to stay relevant and remain current in an everchanging landscape? The way individuals flourish within their personal and professional environments has changed radically in response to many macro-level changes—Web 2.0, the Global Financial Crisis, the dotcom bust, and recently the COVID-19 pandemic (e.g. Crawford et  al. 2020). Machine learning and artificial intelligence use was a key factor in responding to COVID-19 (Lalmuanawama et al. 2020). However, as health services everywhere confronted the need for rapid digitisation of information to meet new scientific decision-making, analysis and reporting demands, the pandemic highlighted workforce shortages and gaps that hampered technological transformation and analytics capability (Fernandes et al. 2020). Sudden shifts, such as the pandemic shock, are coupled with the continual and ongoing innovation facing every sector that seeks to be competitive and to improve on current practices. The impetus of constant social and economic change provides the foundation for myriad professional learning and development opportunities, provided by professional associations, educational institutions, commercial conference and workshop organisers, and digital and print media publishers. However, their professional development offerings may be based more on business than on science; in general, a lack of valid and reliable evidence-based practices plagues the existing professional development scene.  Professional competency, skill, and behavioural development often take place in diverse on-the-job and in-service locations without clear evidence for program development (Ramsaroop and Petersen 2020). For example, estimated global spending on training and development exceeded USD370 billion in 2019 (Atd Research 2019), and training and development was found to represent 42.1 work hours per employee in a 2019 industry survey (TrainingMag 2019). Despite the generally growing appetite for professional development, the efficacy of programmes has been challenged by practitioners (Jayaram et al. 2012; Loew and Wentworth 2013; O’Leonard and Loew 2012), academics (Avolio and Hannah 2008; Sung and Choi 2014), and commentators (Glaveski 2019; Pontefract 2019; Vedantam 2008). Many professional development offerings may not result in skill development or behavioural change; however, professional learning is a different story, as we explain shortly, and there are many examples of positive and effective professional learning programmes. The pandemic aside, the health sector has seen rapid adoption of technology to support more consumer-centred care and service delivery, and to enable better evidence-­informed service management and planning. The growth across areas such as artificial intelligence (AI), automation, the Internet of Things (IoT), and analytics is increasing demand for appropriately skilled specialists across the HIDDIN disciplines (Butler-Henderson 2020). Still, the health sector remains highly regulated with regards to biomedical and biopsychosocial scientific knowledge. Therefore, HIDDIN work has to be underpinned by evidence-based practices, professionalism, constructive alignment, and practice-based pedagogy. Many of these elements are commonplace in extensively developed programs (e.g. university qualifications), and have mixed uptake in shorter-form offerings (e.g. conferences, one-day professional workshops) (Percy et al. 2021). Each of these elements is an

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important link in what it means to remain current from the individual’s perspective and the employer’s perspective. This chapter unpacks professional learning and development within the HIDDIN workforce, and provides a framework to assess the current professional development environment for this workforce. What are the ongoing professional development needs for this workforce? Skill development, reading, networking, formal and informal education, gaining visibility, being social, and applying for awards are commonly listed strategies for delivering and supporting professional development (Rees et al. 2018). For practitioners, it is rarely about accessing a discrete professional development opportunity but, rather more often, finding effective professional learning that is efficient for their personal learning goals among the international sea of registered training organisations, private education institutes, peak bodies, and various self-help and professional development offerings. This is often challenged by insufficient digital and ehealth literacy among healthcare professionals (Mather and Cummings 2017), reducing the types of offerings accessible to the workers and learners. Keeping professionals’ skills current and relevant is most pertinent in environments where change is mediated by regular technological innovations (Lorenzi and Riley 2013); healthcare is a key example of the need for continuous professional learning (Manley et al. 2018). This is especially so because entry-to-practice clinical and public health degree programmes do not yet routinely prepare future clinicians to work within a digital health setting; this may be due to lagging degree accreditation standards, or due to a mismatch between theoretical knowledge domains in higher education and practical skills requirements of the healthcare environment (Gray et al. 2014). The same situation also applies to some of the entry-to-practice degrees that exist for the HIDDIN workforce. Thus, to remain current and relevant in a sector being reshaped by external pressures and internal innovations, the HIDDIN workforce requires high-quality opportunities to grow and develop capacity to manage and govern health data, information, and knowledge.

Professional Development and Professional Learning The evolution of this topic from professional development to professional learning requires explanation and subsequent application to the HIDDIN workforce. Professional development speaks to a defined cycle of individual improvement from identification of needs to learning, application, and reflection on learning (Stewart 2014). In comparison, professional learning speaks to broader concepts including content focus, active learning, coherence, duration, and collective participation (Desimone 2009). The aspirational shift is toward communities of practice to support learning, away from single time-point professional development opportunities (e.g. a one-off conference, or one-off workshop). These broader approaches to professional learning focus on informal learning, personal agency, influencers of learning, and structural influences on learning (Eraut 2012). The individual who engages in professional learning within a professional context likely begins by identifying as an employee, contractor, or consultant with a clear role description, but loosens

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their identity to participate as a ‘learner’; so throughout this chapter, we refer to the person engaging in professional learning or development as a learner. Many HIDDIN specialists hold a tertiary qualification, which has required them to cultivate generic graduate knowledge, skills, and behaviours, such as critical thinking, responsible collaboration, and formal communication. However, many of these people hold a clinical, business, or information technology or science qualification but have not undertaken training in a specific HIDDIN discipline (Butler-­Henderson and Gray 2018). Many report acquiring these skills through on-the-job training; but this can create a mindset that separates the functions of doing from the patterns of knowing (Stange 2010). So, a HIDDIN specialist may understand what to do, without comprehending why this is known to be the way to do it. On-the-job training also tends to create an organisational culture of professional tribalism that can support repeating of existing practice (Ebert et al. 2014), rather than creating a license for individuals to innovate and improve practices to improve healthcare outcomes. Human resources management (HRM) experts once considered that on-the-job training, with emphasis on single intervention just-in-time skill upgrades, was appropriate for low skill tasks—where the risk to completion of a task without in-­depth training is low—and to extend competence in previously developed skill areas (e.g. Acemoglu and Pischke 2003). However, there is some evidence that on-the-job training is ineffective even for these purposes (van der Klink and Streumer 2002), and that employees who have undergone on-the-job training can perpetuate mediocre practices or incorrect norms to their work team. So training and development theory have evolved significantly. The so-called soft HRM seeks to empower employees to continually learn and develop through tailored learning needs analyses of the individual and the organisation. Fulfilling the needs of the individual employee is an important outcome for the organisation (Marescaux et al. 2013). Soft HRM for the HIDDIN workforce, or the cultivation of a high-quality workforce through health sector work environments that are enriched for learning, is not well described.

Evidence-Based Strategies for Professional Learning The HIDDIN workforce has complex needs for specialised learning, development, and training (Sapci and Sapci 2020). The US Commission on Accreditation for Health Informatics and Information Management Education provides one comprehensive account of foundational knowledge and practice domains of the HIDDIN workforce, including: health information science and technology, human factors and sociotechnical systems, social and behavioural aspects of health, social behavioural and information science and technology applied to health, professionalism, interprofessional collaborative practice, and leadership (Valenta et al. 2018). These domains lend themselves to unique and varied opportunities for professional learning; despite this, there is often a gap between the specific situated needs of the learner, and the types of professional learning that are available. An integrated framework for evaluating professional learning opportunities will empower individuals in the HIDDIN workforce to transfer knowledge about learning

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strategies among their immediate co-workers (e.g. informal on-the-job training), and to also expand the knowledge of their wider networks about best practice learning approaches and empirically evaluated learning opportunities. Remaining current with reference to peer-reviewed literature about adult learning strategies (referred to as ‘evidence-based learning’) will complement and enhance learning on-the-job. Evidence-based learning is concerned with the relationship between evidence of learning and evidence of its application within practice settings, from the perspective of the learning sciences (Van der Hoof and Doyle 2018). This is quite distinct from professional learning about evidence-based practice, which is concerned with assuring that one learns about best practice in their profession, and is also a highly desirable feature of learning! Some examples of evidence-based professional learning strategies in health are outlined in Table 7.1, recognising the potential transferability of these practices within the HIDDIN learner context. There is a range of evidence regarding the efficacy of each learning strategy; for example, growth in post-workshop learners’ literature use and greater utilisation of evidence-based medicine in supporting inpatient clinical documentation was measured after one hands-on workshop (Sastre et al. 2011). The results of one-off events (e.g. one-day workshops, single classes) are questioned in the literature though (e.g. Mathisen and Bronnick 2009; Taliaferro and Harris 2014); there may be a decaying effect of training outcomes where training is not reinforced, although this is contested (Ashenfelter 1978; Bloom 1984; Gaudine and Saks 2004). The greatest challenge to overcome is the oversupply of single time-point events as an overarching Table 7.1  Evidence-based strategies for professional learning in health Professional learning strategies and definitions Case study analysis The presentation of dilemmas to respond to independently or collectively. Critically appraised topic A focused review of a single article to answer a specific clinical question, with potential for practical clinical reflection. Educational prescription Provision of clinical activities, lectures, and small group discussions for the practitioner to determine the clinical bottom-line. Hands-on workshops A practitioner-led workshop to support literature searching skills. Journal club Regular meetings among clinicians to share and discuss published journals within their discipline.

References Anderson (2004).

Capampangan et al. (2010); Ishmach (2004); Sadigh et al. (2012); Shine (2008). Ishmach (2004); Straus et al. (2018).

Sastre et al. (2011). Ahmadi et al. (2012); Cramer and Mahoney (2001); Milbrandt and Vincent (2004). Ilic et al. (2012).

Literature searching Constructing an answerable question and using literature searches to answer it. Topf and Hiremath (2015). Social media sharing Social media reviews of publications post-production, to support ease of information filtering for practitioners.

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strategy for professional learning. Such professional learning activities likely stem from good intentions, but do not tend to create sustainable long-term growth in the individual.  A competing challenge is to systematically measure and interpret the efficacy of other types of learning strategies (Crawford and Kelder 2019)—for example, determining when to evaluate learner and organisational outcomes, regardless of whether pre-post quantitative testing, managerial evaluations, self-­reflections, or participant interviews are the tool used for this. Truly effective training programmes may produce short-term declines in quantitative scoring as individuals reflect critically on their skills or, conversely, scores that are inflated as a result of the sense of community and excitement generated by a learning intervention. Evidence-based learning strategies for the HIDDIN workforce need to be accompanied by convincing evaluation of learning, and an ongoing suite of development opportunities that continually reinforce and build upon initial skills development. This may mean initially intensive programmes with smaller booster activities scaffolded across an annual training and development programme. Opportunities to critically reflect and share findings within a practical context are essential throughout such a cycle. Soft HRM reminds us that it remains important to assure that specific learning activities meet the needs both of the individual learner and of the organisation or organisations where they work (Gaudine and Saks 2004; Hadley et al. 2007).

Current Professional Development Practices While the need for professional development for digital health capabilities has been reported regularly in recent years in the clinical literature, there is a large gap in the literature with regard to HIDDIN specialist professional development needs. An examination of the literature since 2010 indicates some level of discussion in those disciplines where there are clearer qualification pathways, such as health librarianship and health information management, but less so in all the other HIDDIN discipline areas. This is a critical shortcoming that needs to be addressed as part of a conversation about strengthening professionalisation in this workforce. The literature over the past 10 years largely states the need for continuous professional development in these disciplines, and is not especially current (Abrams and Crook 2011; Blue 2011; Dimitropoulos et  al. 2019; Ritchie et  al. 2010). Where specific activities are referenced, they are conferences (Jenkins 2015), books (Shepheard 2011), and formal tertiary training (Ritchie et al. 2010). The literature is bereft of empirical evidence about the impact of professional development on this workforce. Interviews with people working in subdisciplines in the HIDDIN workforce identified a lack of incentives and time to incorporate professional learning into their roles, and little support from employers and clients, or recognition from them of the benefits of a workforce that participates in regular professional learning activities (Dimitropoulos et al. 2019). A 2018 Australian HIDDIN workforce census (Butler-Henderson and Gray 2018) identified that 53.8% (859/1597) of respondents undertook work-based learning (for example, workplace courses, workshops, seminars, journal clubs) during

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the previous 12 months, 34.3% (548/1597) undertook self-directed learning (reading information online, industry news, podcasts/vodcasts, or blogs), 31.4% (502/1597) participated in a professional activity (for example, events and journal clubs held by peak bodies, reading and contributing to professional journals), and 12.9% (206/1597) participated in formal education and training programmes (short courses, micro-credentials, or tertiary programmes). The majority of professional development activities undertaken by HIDDIN specialists are not aligned clearly with a competency framework, are unregulated and not accredited, and often are not aligned with a formal credentialing programme. As such, professional development for this workforce may be of a lower standard than required, does not always meet industry needs, and may not meet the professional development needs of the individuals. For example, 2020 saw marketing to this workforce of an explosion in short courses about telehealth in response to the COVID-19-driven transition to online services, but with little evidence available on what the professional development needs were or how the offering addressed any quality standards. When considering engaging with professional learning, it is critical to first understand the need it seeks to fulfil (Marescaux et al. 2013), and subsequently the degree to which the professional learning is likely to achieve that need. For example, one health information systems manager may need technical professional learning to support upskilling in a particular software being added to the hospital they are situated in. Another may require socio-technical development opportunities to enable their ability to influence laggards to engage and support new electronic health records systems usage. During the COVID-19 pandemic, this has been clearly evident with some medical practitioners requiring careful (but quick) onboarding and nudging to enable a move to telehealth and the associated record keeping required. Engaging in critical reflection and evaluation prior to entering the diverse range of learning environments and experiences can support a greater and more relevant skill set developed within the HIDDIN workforce.

Evaluating Before Choosing Professional Learning Options As an adult learner, it is common to be presented with a series of opportunities to engage in professional development or professional learning. To ascertain the most appropriate opportunities for the individual, it is useful to consider whether an education option aligns with the specific learner context. That is, will the offering support an individual learner’s ability to achieve and continuously improve? Table 7.2 presents a series of principles, along with brief questions that a learner can use to assess whether the professional learning activity will be useful for them to pursue. Table 7.2 speaks to the ongoing balancing act, between the needs of the workforce and the emerging best practice approaches to learning. These are not always perfectly weighted; however, careful consideration to a diverse range of stakeholders will maximise the likelihood that: the learner achieves the personal and professional goals that align to their needs; the educator assures the learner’s achievement;

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Table 7.2  Principles for evaluating options for future professional learning Principle definition Evidence-based The relationship between the learning and its basis in evidence, to foster future evidence use in learners.

Example questions • Does the learning support future evidence-­ based practices? • Is the learning based on valid and reliable evidence? • Are the outcomes of the learning measured valid and reliable? • Has a training needs analysis been conducted Strategic alignment prior to learning? The alignment between the individual, • Is there demonstrated connection between the organisation, sector, and the learnings. learning and the needs of the individual, organisation, and sector? • Are there opportunities to apply theoretical content to the practical context? • Are there clear and transparent intended Constructive alignment learning outcomes? The demonstrated relationship between intended learning outcomes, assessment, and • Is there a demonstrated link between learning outcomes and assessment? learning activities. • Do the learning activities support student success in the assessments, and attainment of the learning outcomes? • Is there a clear relationship with the type of Pedagogy The method(s) of teaching pedagogy applied and the context? • Are pedagogies explicitly applied to the learning environment? • Has the educator clearly considered the most appropriate pedagogy?

and the workforce collectively highlights continuous improvement as a result. For example, tertiary education should emphasise constructive alignment and sound pedagogy, but it may not align neatly with the strategies of the organisation preparing to fund their staff to undergo a learning opportunity. Likewise, professional association webinars may support a specific practice-based response to sector-­ identified needs, yet may lack sufficient time to build a rigorous, aligned, and pedagogically considered professional learning activity. The learning environment for professional learning requires consideration at every level from the designer to the end user or ‘learner’. Each stakeholder will have different needs, however, there is a need to consider explicit components of the learning from each stakeholder’s perspective. For example, the individual learner will likely be most concerned with the relationship between their needs and the learning activities, whereas an educator will likely be most concerned with the application of pedagogy and constructive alignment to support learning outcome achievement. These perspectives are not mutually exclusive and may require iterative approaches to incremental improvement beyond initial transformative curriculum development.

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Conclusion This chapter has applied a soft human resource management lens and a learning science lens to the professional development of a current and relevant HIDDIN workforce. The impetus within this research was to support a foundational understanding of the diverse stakeholder views within the professional learning landscape: educator, individual, organisation, and sector. While the educator may bring pedagogy and constructive alignment, the individual may seek rapid learning opportunities that fit within their personal and professional goals. The sector may bring immediate needs and challenges, but may lack the necessary educative expertise and time that the tertiary sector can spend developing postgraduate offerings. This chapter began with an exploration of the challenges to create high-quality valid and reliable learning environments to support learning globally. It considered diverse stakeholder perspectives and evidence-bases. It indicated the relative immaturity of professional learning and development, which is  an area of activity that requires maturation to support the emerging professionalism of the HIDDIN workforce. This may be in the form of sector and learning institutional collaboration to build systems and frameworks to support aligned and adaptable professional learning cultures. The chapter also outlined enormous opportunities to improve professional learning opportunities and to improve the contributions of this workforce to essential aspects of health service operations.

References Abrams K, Crook G. The Canadian Health Information Management Association: health information management in Canada. HIM-Interchange. 2011;1(1):17–21. Acemoglu D, Pischke JS.  Minimum wages and on-the-job training. Res Labor Econ. 2003;22:159–202. Ahmadi N, McKenzie ME, MacLean A, Brown CJ, Mastracci T, McLeod RS, Evidence-Based Reviews in Surgery Steering Group. Teaching evidence based medicine to surgery residents-­is journal club the best format? A systematic review of the literature. J Surgical Educ. 2012;69(1):91–100. Anderson JG. The role of ethics in information technology decisions: a case-based approach to biomedical informatics education. Int J Med Inform. 2004;73(2):145–50. Ashenfelter O.  Estimating the effect of training programs on earnings. Rev Econ Stat. 1978;63:47–57. Atd Research. 2019 statee of the industry. 2019. https://www.td.org/research-­reports/2019-­state-­ of-­the-­industry#gsc.tab=0. Accessed 31 Mar 2021. Avolio BJ, Hannah ST.  Developmental readiness: accelerating leader development. Consult Psychol J Pract Res. 2008;60(4):331. Bloom HS. Estimating the effect of job-training programs, using longitudinal data: Ashenfelter’s findings reconsidered. J Hum Resour. 1984;19(4):544–56. Blue B.  Health reform and the health information management profession. HIM-Interchange. 2011;1(1):3–6.

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Part III

Innovation

Chapter 8

Health Workforce Learning in Response to Artificial Intelligence Sandeep Reddy and Paul Cooper

Abstract  In the era of big data and digital technology, artificial intelligence (AI)’s potential and positive implications are becoming prominent. With evidence mounting that AI is performing as well as, or in certain instances better than, clinicians in diagnosis and therapy, it is inevitable the current, and future health workforce will be working with this technology. Yet there is limited or no education or training of healthcare professionals or health information professionals about AI and its appropriate use. We propose an outline for a formal training suite that will inform the health workforce about AI, its types, its application in healthcare and the implications. There are three principal components: computational, translational, and governance; each should be delivered using appropriate pedagogical approaches to enable progressive and productive learning. The current lack of AI training presents a risk when an unprepared health workforce begins using this technology in healthcare settings. This situation has direct implications for the specialist health information workforce. Keywords  Artificial intelligence · Machine learning · Data science · Deep learning · Training

Introduction We live in an era of big data, and the utility of big datasets for addressing health issues becomes ever more obvious (Puaschunder et al. 2020), but manual processing of large datasets is cumbersome. Fortunately, automated computation of such data via computational reasoning frameworks is one of the most disruptive recent S. Reddy (*) · P. Cooper School of Medicine, Deakin University, Melbourne, VIC, Australia e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_8

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medical services advancements and has drawn clinicians, analysts, and industry experts’ attention and efforts (Reddy 2018). Such computational reasoning frameworks mimic human knowledge acquisition by simulating reasoning and self-­ rectification (Sapci and Sapci 2020). Artificial Intelligence (AI) and distributed computing, which have accelerated the adoption of computer-based reasoning in medicine, have demonstrated the possibility of being more accurate than doctors at predicting or diagnosing some medical conditions, especially in radiology and dermatology (Reddy et  al. 2019). Contemporary AI’s capacity to advance learning acquisition autonomously separates it from previous generation mechanical or explicit rule-based formulations (Kulkarni et al. 2020). AI can now mirror human knowledge utilising self-learning strategies (Sapci and Sapci 2020). Machine Learning (ML), a subset of AI, permits information gain and forecasting without expressly created rules. Deep Learning (DL) is a subfield of ML utilising a structure of neural networks with feedback loops to obviate the need for express rules. Using DL techniques, computational reasoning frameworks can calculate how to group pictures and appoint names to words in a sentence (semantic naming). Medical imaging analysers, virtual assistants and facial recognition software are some other practical instances of DL. One consequence of DL’s approach is the need for enormous amounts of structured and unstructured data to train the system to achieve accurate predictive results; the need for the training data to be unbiased and of high quality adds to this approach’s complexity (Puaschunder et al. 2020). The requirement for digital health specialist expertise in processing extensive high-quality health datasets is therefore twofold: to directly aid clinicians through the combination and analysis of the datasets in new and insightful ways (e.g. to enable early intervention to mitigate disease complications, reduce medical errors and enable keener insights into disease progression and complications); and to provide datasets for training predictive computational reasoning systems that aid clinicians, especially those employing DL techniques. The algorithmic approach of AI and its ability to automate healthcare delivery aspects present unprecedented opportunities for stakeholders (Reddy et al. 2019). Aspects of AI, such as decentralised information curation and more economical processing of data can herald greater affordability and democratisation of healthcare, unlike other technologies. Also, as health data exponentially increases, more training data becomes available for AI, thus increasing clinicians’ ability to harness AI for achieving appropriate health outcomes. However, health data managers and clinicians must be properly equipped with the knowledge and skills to realise these opportunities to advance healthcare. As AI-enabled digital health advances, there will be a net positive benefit for healthcare services and patients, if this technology is used appropriately and safely (Reddy et al. 2019). If not, inequality, discrimination and medical risks may worsen. To support the appropriate use of AI, we need to organise the development of the skills, approaches and performance of the health workforce to become digitally competent and confident (Topol 2019). This means an organised approach to improve digital literacy levels, enable awareness of the competencies required, provide access to education and training, and support technology integration in clinical practice. This is not something any health service can ignore as there is a

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fundamental shift as to how digital technology is being adopted in healthcare; the COVID-19 pandemic showcased the utility of digital technology adoption in delivering healthcare and how digital systems can be learned and deployed in clinical environments in breath-taking speeds (Keesara et al. 2020). Currently, digital health applications include telemedicine, electronic health records and AI service information sharing and storage platforms, service delivery tools, and decision support, amongst other applications (Topol 2019; Coravos et al. 2019). With expanding telemedicine and AI integration into service delivery, the potential for greater use of remote health monitoring and utilisation of patient feedback in care models is imminent. Although more needs to be done to maximise the benefits of digital systems that include AI computational reasoning frameworks, current digital health knowledge and skills of many health professionals are limited; reasons for this may include unavailability of digital health applications or lack of integration of these applications in routine service delivery (Topol 2019; Alscher and Schmidt 2018), or aptitudinal and attitudinal readiness of the workforce (Department of Health and Ageing 2011). It is the role of clinicians to interpret medical conditions and explain them to the patients (Sapci and Sapci 2020; Wartman and Combs 2019) but the notion that clinicians can be abreast of all the relevant medical and patient information to provide optimal care is a fantasy in the era of overwhelming information sources. One useful path forward is to combine AI computational reasoning systems with human insights (Algorithmic Medicine) to offer combined benefits in processing and digesting huge data sources. It becomes harder and harder for clinicians to be ignorant of AI’s technical and functional aspects.

A Framework for Professional Learning As responsible managers and users of such technologies, the healthcare workforce and health information workforce need to be aware of AI’s possibilities in healthcare, and the best-combined capabilities of humans and AI. The workforce needs to understand the characteristics, advantages and limitations of machine learning algorithms, ethical implications, evaluation and audit mechanisms, benchmarking and development of AI models (Sapci and Sapci 2020; Topol 2019; Wartman and Combs 2019). Without such education, clinicians are likely not to adopt AI; where it is implemented, they may make mistakes or have an inability to interpret the algorithms or even be sidelined when deeper integration of AI in service delivery occurs. With such education, clinicians are likely to be confident and capable of seeing the AI as an augmentative process, and thus be more ready to integrate the assistive AI systems into workflows without compromising safety or quality. Three developments are setting the ground for a big shift to integrate AI in healthcare delivery (Topol 2019): the increasing proportion of the population having their genome sequenced, generation of health data that patients can control, and improvements in algorithms’ performance to analyse such data. To prepare the health workforce to more effectively and safely participate in the benefits offered by this shift, digital

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literacy, awareness of the requisite capability, access to education and training, and appropriate skills to utilise technology to improve patient outcomes need to be improved (Topol 2019; Bilimoria et al. 2019). For this to occur, there has to be a multi-level and multi-organisational effort, including investment from authorities and organisations in their workforce and training programs that incorporate the relevant syllabi and assessment structures. So, what should be involved in these training programs? We know that for safe and appropriate use of data-driven and smart technologies, we require extensive digitisation of patient records, standards for the design and development of these technologies, and evidence-based guidance in the technologies (Puaschunder et al. 2020; Topol 2019; Wyatt and Lin 2002). We also know that to use these technologies in combination with Algorithmic Medicine technologies, the workforce should have the knowledge and skills in data attribution, curation and governance, an understanding of the ethical aspects of the use of patient/healthcare data and the ability to scrutinise digital health technologies for their contextual utility. Further to this, we know for the benefits of AI technology to be harnessed within healthcare there has to be time and willingness amongst the health workforce to adopt the technology, knowledge of the technology amongst the workforce, patient-­ centred design of the technology and willingness amongst the organisation to fund the adoption of the technology. If we consider all these factors, together with considerations from existing health informatics educational frameworks, and existing AI training courses, an outline for an AI educational package for the health workforce can be designed. AI training courses generally offer computer science, data analytics and algorithm-based platforms in their curriculum to enable students to learn how to interpret AI models and formulate suitable AI strategies, particularly those requiring specialised knowledge; what has to be taken into account for the health workforce are the translational aspects of AI in healthcare, including predictive modelling, clinical AI applications and AI evaluation in healthcare for safety and performance (Reddy 2018). We detail each of these components of an educational package next. A course on AI targeted at a healthcare workforce should offer students a reasonable understanding of AI model development and deployment and, importantly, of how to ascertain benefits and deficiencies of various models and approaches. To do this, it should cover some aspects of the mathematics, programming and data science behind AI to at least some fundamental competency level. Mathematical concepts that might be considered fundamental include linear algebra, probability, multivariate calculus together with the applied use of these concepts like logical inference, Bayesian networks, Markov models and graph search that underpin the aspects of machine learning. Health data managers and clinicians who work with AI need to be familiar with these topics to understand how AI models are constructed and how they behave in test and real-world environments. Since these topics may be difficult to learn, we propose a pedagogical approach that is experiential, iterative and uses AI techniques to monitor and assist learners. Further to this, we recommend learning at least one of the major programming languages used to build AI models, e.g. Python, R, C++ and Java. Python is the most popular as it has extensive

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libraries, and a syntax closer to the English language than other programs and is easy to implement (Costa 2020). Learning a programming language also allows learners to interact with data by developing, testing, running, and iterating their code. Data science topics like databases, data query tools, exploratory data analysis, descriptive analysis, regression, classification, network and text analytics, and application programming interfaces are worthwhile to incorporate into the fundamentals. Data fuels AI (particularly DL approaches), and it makes AI relevant in the real world, so to have confidence in applying and managing AI models in healthcare requires some knowledge of the data science concepts of curating and manipulating large datasets in a healthcare context. Visual analysis tools can help in learning about data science. For AI technology to be adopted in clinical practice and healthcare environments, healthcare professionals need to know how and where AI can be applied. We propose that the healthcare and health information workforce should learn about the main forms of AI currently deployed in healthcare: Machine Learning Analytics (MLA), Computer Vision (CV) and Natural Language Processing (NLP) (Reddy 2021). These AI types are applied in various medical applications such as symptom checkers, real-time transcribers, medical imaging interpreters, clinical decision support systems, robotics, and data mining. MLA is being used in healthcare and by clinicians for diagnosis, prognosis and therapy (Reddy et al. 2019). However, the predominant use is in analysing health data to support healthcare professionals’ clinical decisions. These are best exemplified by the non-knowledge-based Clinical Decision Support Systems (CDSS) (Reddy 2018). These CDSS can draw upon complex clinical data and insights from sources like electronic health records and medical guidelines, to direct clinicians in making appropriate medical decisions. MLA also drives CV and NLP (Reddy 2021). CV utilises software to recognise and interpret images and videos, while NLP uses software to interpret human oral and written language. CV is being used for automated interpretation of multiple medical imaging modalities including X-rays, CT, MRI, histopathology and dermatological images, amongst other applications (Reddy 2021). NLP can be used in clinical environments to develop medical chatbots to screen and triage patients, extract clinical concepts from electronic health records and transcribe doctor–patient encounters, as well as for administrative purposes. For AI assistive approaches to be successful in clinical workflow settings, there needs to be a recognition that the human and the AI are part of a combined system; the fact that AI systems are cognitive assistive technologies means that potentially they are much more directly involved in expert guiding or assisting clinician workflows than previous generation healthcare IT systems. Education should emphasise the interactions between healthcare professional, the health information professional and the AI system, as part of learning about what makes implementations successful in clinical settings. For example, the experience of AI applications in radiology has shown that extra emphasis on the AI models’ quality assurance is required for methodical use in clinical practice (Vandewinckele et al. 2020). As the value of AI and its applications in healthcare are recognised by stakeholders, ethical and regulatory aspects and challenges of incorporating AI into existing

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clinical workflows and systems are coming to the fore (Reddy et al. 2019). There are still very few success stories of AI adoption in actual clinical environments and its operation on prospective data; so far, most of the achievements have been in controlled or simulated settings. Further, where AI has been utilised in healthcare administration type of applications, discriminatory effects particularly against marginalised or vulnerable populations have been found (Reddy et al. 2019). In addition to these concerns, AI applications’ autonomous or semi-autonomous nature can present regulatory challenges both at clinical approval and post-approval stages. At the approval stage, regulatory authorities have to consider the privacy of the data being used to develop the AI models and the safety of the AI applications when used in clinical settings. Post approval, there has to be an evaluation mechanism to monitor the efficacy and continued safety of approved AI applications. When errors occur because of approved AI applications, the liability issue needs to be taken into account (Reddy et al. 2020). These issues and their solutions have to be incorporated in the AI education suite, as well as aspects such as trust-building. Schneiderman (2020) has developed guidelines for reliable, safe and trustworthy human-centred AI systems in generic settings; and Cai et al. (2019) have reported that to gain confidence and trust clinicians desired upfront information about basic, global properties of the AI model, including known strengths and limitations, its subjective point-of-view and its overall design objective. Considering together all three components of AI training customised for a health-­ oriented workforce—fundamentals, translation, and governance—we recommend these be weighted in the proportions shown in Fig. 8.1. We further recommend that pedagogical approaches be differentiated depending on whether technical aspects or human factor aspects are the focus of learning; all these pedagogical approaches can also be supported by using assistive technologies (pedagogical agents) (Schroder and Adescope 2012). To support learning the technical elements inherent in the foundational and translational AI topics, we recommend experiential learning approaches such as the use of computer simulations. These have previously been shown to be useful in engineering education, such as the approach proposed by Botelho et al. (2016) which builds on the prior foundational work of Kolb’s (1984) experiential learning theory. We recommend this approach because the fundamentals component is essentially technical in nature, thus lends itself to computer simulation modules. Computer

25%

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50%

Translational

25%

Fundamentals

Trust Building Mechanisms Evaluation and Audit Mechanisms Regulation and Medico-legal Aspects Data Governance Systems Application in Medicine Application in Healthcare Delivery Change and Adoption Healthcare Policy and Management Al History, Definition and Categories Mathematical and data Science Components AI Model Development and Deployment

Fig. 8.1  Components of AI in health workforce education and training

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simulations would also be appropriate for the translational topics, where the three AI approaches of MLA, CV and DL could be explored and compared in an experimentally rich manner. For example, a variety of software models could be made available to the student to help them understand how a CV model can detect an image through repeat training, and to discover the limitations and benefits of each. The governance component of our framework and the human factor aspects of the translational and foundational components has inherently strong subjective elements that would be well supported by pedagogies that support frequent learner feedback cycles, such as evaluative feedback, self-regulation and attunement. Evaluative judgement enables learners to make decisions about the quality of work themselves, using practices such as: self-assessment, peer review, feedback as dialogue, rubrics for formative assessment and multiple exemplars with quality indicators (Tai et al. 2018). Feedback approaches for self-regulated learning (Nicol and Macfarlane-Dick 2006) include ‘scaffolded feedback’, that is, providing incremental hints until a correct answer is self-generated (Finn and Metcalfe 2010). Consideration of ethical and governance aspects of AI would be well suited to responsive attunement techniques where learners and teachers explore issues of importance in syndicate style activities or debates, such as where one group would develop and propose a situation of ethical complexity and another group would tease it out and respond to it.

Discussion As AI technology’s part in digital health becomes increasingly prominent in healthcare delivery, employers, regulatory and professional bodies, and the patient community will expect healthcare professional competency in governance, translation and fundamentals. AI’s ability to improve the diagnosis and prognosis of many medical conditions, standardise medical care, decrease the costs of delivering healthcare and reduce medical errors is already evident. AI has the ability to assist healthcare workflows even more when coupled with human involvement, i.e. human in the loop systems (Wang 2019); we already have seen instances where AI, coupled with humans, perform better than AI or humans alone (Lovett 2019). All AI uses in healthcare settings require the stakeholders to understand the technology they are working with, achieved through a structured orientation to the various aspects of AI and its healthcare application. Innovations in AI in healthcare thus have several direct implications for the specialised health information workforce that is the focus of this book. First, all practitioners in the specialised health information workforce will need to upskill in this field, so that they are prepared for inevitable changes to the roles and functions they presently perform. Following the training approach outlined in this chapter can meet the professional development needs of these practitioners. Additionally, some people in the specialised health information workforce will need to go beyond this training, to develop substantial expertise in AI in healthcare. This will be necessary if they are to remain relevant as advisors and consultants to clinicians, service

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managers, and policy-makers, about how those groups can work responsibly with computational, translational and governance aspects of AI in healthcare. Finally, a select group within the specialised health information workforce will need to become capable of designing and delivering the type of training described in this chapter, to ensure that professional learning and development is more widely available across the entire health workforce. As AI evolves, our framework will allow for new technology developments and their healthcare application to be included in curriculum design and delivery. Considering the current situation of limited or no health workforce education about AI and its functions and the imminent possibility of this workforce working with AI directly or indirectly, there is some urgency for educational institutions and healthcare organisations to collaborate to deliver AI-related education not just for the health workforce but also for the larger patient and health consumer community. This wider approach would strengthen awareness of governance needs and challenges in using AI appropriately in healthcare.

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Nicol DJ, Macfarlane-Dick D.  Formative assessment and self-regulated learning: a model and seven principles of good feedback practise. Stud Higher Educ. 2006;31(2):199–218. Puaschunder J, Mantl J, Plank B. Medicine of the future: the power of artificial intelligence (AI) and big data in healthcare. RAIS J Soc Sci. 2020;4:1–8. Reddy S. Use of artificial intelligence in healthcare delivery. eHealth – Making health care smarter. 2018. https://doi.org/10.5772/intechopen.74714. Reddy S.  Algorithmic medicine. In: Artificial intelligence: applications in healthcare delivery. London: Routledge; 2021. Reddy S, Fox J, Purohit MP.  Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019;112(1):22–8. Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(3):491–7. Sapci AH, Sapci HA. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020;6(1):e19285. Schneiderman B. Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI Systems. ACM Trans Interact Intell Syst. 2020;10(4):26. Schroder NL, Adescope OO. A case for the use of pedagogical agents in online learning environments. J Teach Learn Technol. 2012;1(2):43–7. Tai J, Ajjawi R, Boud D, Dawson P, Panadero E. Developing evaluative judgement: enabling students to make decisions about the quality of work. High Educ. 2018;76:467–81. Topol E. Preparing the healthcare workforce to deliver the digital future. The Topol Review. An independent report on behalf of the Secretary of State for Health and Social Care. 2019. https:// topol.hee.nhs.uk/. Accessed 31 Mar 2021. Vandewinckele L, Claessens M, Dinkla A, Brouwer C, Crijns W, Verellen D, van Elmpt W. Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance. Radiother Oncol. 2020;153:55–66. Wang G. Humans in the loop: the design of interactive AI systems. Human-Centred Artif Intell. 2019. https://hai.stanford.edu/blog/humans-­loop-­design-­interactive-­ai-­systems. Accessed 31 Mar 2021. Wartman SA, Combs CD.  Reimagining medical education in the age of AI.  AMA J Ethics. 2019;21:146–52. Wyatt JC, Liu JL.  Basic concepts in medical informatics. J Epidemiol Community Health. 2002;56(11):808–12.

Chapter 9

The Rise of the Consumer Health Information Specialist Rachel de Sain

Abstract  Healthcare organisations are looking to adopt connected technologies and innovations to empower healthcare consumers with quality information and services to become co-pilots of their own health and wellbeing. This chapter describes the emerging field of the consumer health information specialist (CHIS), their roles, pathways into the profession, places of work and principles of professional practice. It highlights the importance of health, digital and data literacies to ensure that all healthcare consumers can make informed choices for themselves and those they care for. As health technology adoption and adherence by consumers become more and more vital to effective healthcare outcomes, CHIS professionals are increasingly essential in the health workforce to advocate for, create, curate, distribute and analyse quality information and services for all healthcare consumers. Keywords  Consumers · Participatory medicine · Health literacy

Introduction Consumers want to play an active role in their health and care (Betts and Korenda 2018). This creates a demand for an environment in which health information, tools and services can be personalised for healthcare consumers’ own needs. Information should flow seamlessly amongst healthcare professionals and other parties (human or machine) whom the consumer chooses to involve in their care. Thus the consumer’s role shifts from passenger to co-pilot (Castle-Clarke and Imison 2016)

R. de Sain (*) Codesain, Sydney, NSW, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_9

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continuing to require quality information to guide their decision making while increasingly becoming creators of information themselves. The term consumer versus patient is debated by many across the health sector (Henderson and Petersen 2002). A healthcare consumer is engaged in preventive and wellness solutions, making decisions about current and future care needs, including being a patient. Patients are a subset of healthcare consumers where there is a consumer-clinician therapeutic relationship. A healthcare consumer is, therefore, an individual who uses the services of a healthcare provider, including patients receiving medical care or treatment. The health system is increasingly becoming digitised, enabling a greater breadth of information, including from consumers and being used by consumers. This digitsation of information offers transformative opportunities to rethink healthcare delivery models from research to treatment to future policy development (Allen 2020). To leverage the opportunities of this new digital age, health organisations are increasingly looking to professional practitioners who are consumer healthcare information specialists (CHIS) and the unique skills and expertise they bring to support strategic objectives (World Health Organization 2016). While a clinical audience expects information that is structured and standardised using consistent terminologies and classifications (Seebode et al. 2013), consumers have variable literacy, experiences, access to and needs for information, so they require different formats and styles of information to achieve their health outcomes (Pian et al. 2020). Information management includes policies, procedures, applications and systems to manage the information lifecycle (Boaden and Lockett 1991) but can overlook the activities required to plan, create and curate meaningful, actionable content to engage consumers in health care. Consumers require digital, data and health literacy skills to participate in the digitised health system (Australian Commission on Safety and Quality in Health Care 2013) and improve health outcomes (Hilfiker et al. 2020). This chapter explores the role of consumer health information specialist (CHIS) professionals in providing useful and usable information for health consumers in an increasingly digitised health system that relies on data, information, knowledge and wisdom for improving health outcomes. These CHIS professionals are an important part of the health information workforce.

The Work of Consumer Health Information Specialists CHIS work spans the domains of data, information and content management. Data management involves creating, obtaining, transforming, sharing, protecting, documenting and preserving data in a binary digital form that is efficient for processing. Information management is the collection, storage, curation, dissemination, archiving and destruction of documents, images, drawings and other sources of processed data. Content management is the set of processes and technologies to support the creation, collection, management and publishing of information in any form or medium. For the purposes of this chapter, these domains are referred to collectively

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as “information.” Working within these domains aligns with a cycle of activities that include planning, creating, curating, distributing and analysing information, with a centralised management, operations and governance function (Fig. 9.1). A planning stage defines the requirements for information creation. Planning activities may be triggered by a report, a one-off incident, a new clinical system, or a seasonal event. Factors that are analysed for planning purposes include what information sources already exist, the intended audience, governance and approval requirements, publication and distribution and desired outcomes measures. Since the creation of information may require additional expenditure, the commissioning organisation should mandate planning work (in the form of a business case, scoping document or project brief) and approval of a budgeted plan prior to any creation activity. This is particularly relevant if commissioning external services, for example from graphic designers, videographers or marketing agencies. Planning roles may include product manager, project manager, business analyst and technical architect; people in these roles often have specific experience and skills in project management, and organisations may prefer those with additional healthcare experience. Information creation may involve the acquisition or creation of new information or manipulation of existing content to meet new needs. The high-level information creation requirements developed during the planning phase will require validation GOVERNANCE

Planning

ADVOCACY

Curation ATL BTL

Distribution

MANAGEMENT

Fig. 9.1  The CHIS work cycle

OPERATIONS

Creation

Analysis

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with end users to understand their needs. Creators use service design principles and design thinking methodologies to co-design solutions with relevant user stakeholders. Creator roles require diverse skills, including copywriting, graphic design, videography and software development. Some organisations may not have permanent staff in these roles and may utilise third-party freelancers, contractors or agencies. Digital professionals are more likely to be working as freelancers within the gig economy (Bergvall-Kareborn and Howcroft 2014), pitching for work on a per-­ project basis rather than being permanently placed within an organisation. Increasingly organisations are looking to contract creatives with experience in the healthcare sector and a nuanced understanding of its protocols—such as clinical governance of what can be visually portrayed or said within a piece of consumer healthcare information and the impact of non-adherence to clinical quality and safety frameworks. Once information has been created, it needs to be curated. Information curation consists of above-the-line (ATL) activity and below-the-line (BTL) activity. Marketing and advertising terminology refers to ATL activity as mass media, untargeted communication (e.g. TV or radio) and BTL activity as online or social, often involving targeted communications to specific types of people (Furman 2017). In the context of CHIS, ATL curation activities impact how information is experienced by the end user, how it is seen or presented in a format. ATL curation activities determine the best way to collate multiple pieces of information to deliver a better experience for the healthcare consumer throughout their journey; this work recognises that these consumers access, use and reuse information differently at different stages to meet their perceived needs (Alzougool et  al. 2008). Social media has become the primary source of news and information for most people (Shearer and Matsa 2018). Consumers may access health information directly via a social network post, third-party website, navigating from a publisher’s website homepage or any number of pathways. Consequently, consumer healthcare information should be modular and responsive, so that it retains its credibility regardless of the access point. ATL curators may work closely with user research, insights and analysis team members and co-design directly with end user consumers to understand their audience’s behaviour. BTL curation work is the design, delivery and management of the metadata or descriptive data about the information to be found, shared, reported and managed efficiently. BTL curation roles include health informaticians, data scientists, information managers and health librarians. They are responsible for taxonomies, ontologies and vocabularies that enable information to be categorised, classified and appropriately indexed by various online search engines, websites and content services. BTL curators are responsible for the design, development and ongoing management of information architecture, content modelling, data schemas and overall information models. Many healthcare consumers seeking health information online use a search engine such as Google, Bing or Baidu to begin their search (Tan and Goonawardene 2017; Cocco et al. 2018). As search engine algorithms crawl the metadata to index the information within their catalogue, it is imperative that BTL curation is prioritised, ensuring that consumer healthcare information is easily accessible for all healthcare consumers.

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Information distribution requires understanding how the target consumer accesses and interacts with healthcare information that has been created and curated. Digital marketing experts estimated that most Americans some years ago were exposed to around 4000–10,000 ads each day (Simpson 2017), and this “noise” has increased vastly since then. It is challenging—but essential to distribution—to compete successfully against this noise, to ensure that healthcare consumers actually find and engage with the information that a commissioning organisation wishes to provide to them. CHIS distribution roles may exist within a marketing division, depending on the size of an organisation. They are tasked with understanding the best way to distribute the information to reach a target audience or to position the information capture service to elicit the most responses. These types of roles may include search strategists, social media managers, partnership managers. They may also require legal and commercial resources to negotiate distribution, licencing and partnership contracts for making the information available through third-party channels. The need to analyse the impact and whether information achieves its intended aims is part of the CHIS work cycle. The shift to digital interactions allows for significant insights into health consumer behaviour and an exciting opportunity to rethink the future of healthcare delivery by shifting from a “find and fix” model to a “predict and prevent” system that uses data-driven insights from consumer behaviour to identify and respond to potential health events. The analysis involves business intelligence, web and social media analytics, market research and reporting, as well as customer engagement and research. Analysis closes the loop when it is used in the planning phase of the next project; it guides the development of new requirements and defines what success metrics are used to measure the impact of the new or changed consumer health information. Management and operations functions are required throughout the work cycle to drive the day-to-day activities, support teamwork and deliver products and services effectively. These skills are transferrable from other industries and often provide an entry point into the field. Management experience in leading diverse teams and overseeing sophisticated, high-turnover online content applications or services is a highly sought-after skill as the volume of work increases for CHIS professionals. Sophisticated skills in assuring data privacy and security are increasingly important, too, as organisations capture and store data about the users of their consumer health information (Managed Healthcare Executive 2019). Information governance, a vital function of the entire health information workforce, is particularly important for CHIS professionals. Governance includes responsibility for developing the structures and policies that determine how an organisation manages the consumer healthcare information lifecycle. Information governance includes clinical, technical and content governance to ensure the quality of the information, the minimisation of harm from methods of distribution and use, and the engagement of users through adherence to design, usability and accessibility standards. Consumers are beginning to generate and contribute more of their own information either actively through blogs and application interactions or, sometimes passively, through data from wearables and interactions with health systems

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(Reinsel et al. 2018). Thus CHIS professionals have ever greater responsibility in that they not only provide the information that they create to consumers; also they are increasingly collecting, storing and managing consumer-generated health information and applications that interact with it. Governance will increasingly need to deal with the legal and ethical implications of how consumer-generated healthcare information is being requested, stored and used. Terms of use and governance policies will require regular updating to maintain transparency with healthcare consumers and meet evolving regulatory requirements. Consumer health advocates sometimes referred to as ePatients or professional patients, are not typically consumer health information specialists. Some may choose to learn the necessary skills to work in such roles, while others will consult closely with skilled CHIS professionals throughout the work cycle to represent patients’ and health consumers’ needs. CHIS advocacy work entails support for healthcare consumers to achieve the best overall health outcome for their particular circumstances, considering their specific situation, culture, beliefs and preferences and clinical health needs (Cervantes et al. 2020). CHIS professionals play a vital role to promote health equity and rights, facilitating, promoting and supporting health consumer advocacy, networking and leadership, through engagement, information dissemination and health literacy training.

Pathways into Consumer Health Information Work Creating and managing quality consumer health information and associated services is a team sport that requires a diverse set of skills and qualities, many of which are transferrable to/from other industries. The number of pathways is as broad as the number of roles associated with this workforce. It is rare within the health sector that specialists can come from such diverse backgrounds. This breadth of experience and knowledge is an asset for organisations tackling the emerging challenges coming from the digital transformation of the sector. Digitally enabled patients and carers driven by their own experiences may enter the health information workforce as advocates or content creators. They work to help and guide others, sharing their stories via support groups, blogs or dedicated personal websites, or increasingly through patient social networks and online communities like WEGO Health (https://www.wegohealth.com/) and HealthUnlocked (https://healthunlocked.com/). Public and private organisations are embracing the value of this ePatient work, with some creating specific roles for patient experience or outreach, although the majority of such activities are undertaken on a voluntary unpaid basis. Patients and carers may choose to become CHIS professionals by upskilling into the design, product management, strategy and general management roles. The role of library and information service managers appears to be a natural pathway into CHIS work, given their unique skillset. As health information moved online, many health librarians undertook the BTL curation required to make

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information searchable and discoverable—by consumers as well as providers of care—through health websites, online catalogues and search engines. Online health information has been a key facilitator for improving, maintaining and recovering health (Hill and Sofra 2018). Library and information service managers may enter the CHIS workforce as BTL curators, remain in those specialised roles as information content managers, and sometimes transition into associated analysis or governance roles. Since a significant component of CHIS work relies on digital, media and information and communication technology (ICT) professionals, many CHIS professionals begin their careers outside the health sector, which frequently does not have these skills in-house. Healthcare organisations recruit outside of their industry, as they begin to develop digital consumer health information services such as websites, interactive media or mobile apps. Often the first step for digital media and ICT professionals to enter the health sector is to be employed on government-funded projects, either directly or via professional services firms and digital/ICT agencies. Digital, media and ICT professionals may enter the CHIS workforce in any role matching their knowledge domains and skillsets. Clinicians from all health disciplines may choose to transition into CHIS roles to move away from face-to-face care delivery but continue to apply their skills and expertise to improve patients’ outcomes. Clinical professionals may be brought in as advisors or in a clinical governance role within a project, working alongside non-­ clinical team members to produce consumer healthcare information. Clinical professionals may enter CHIS work as clinical quality assurance advisors or to provide clinical governance of an organisation’s consumer information activities. They are detail-oriented and process-driven, skills that make an excellent base for the transition into CHIS planning, creation and curation roles—particularly in user research, user insights and service design. Others enter CHIS work via public service and peak bodies. Governments from local to state/regional to federal/national are significant funders of population health and hold overall responsibility for their citizens’ health and wellbeing. This provides a pathway into CHIS for public sector employees. At all levels of government, population health initiatives require a range of skilled public health professionals who may work on projects that include the development of consumer health information. As well, some government workers have extensive regulatory knowledge and experience as policy analysts about how consumers and their agents trying to procure health and care services must navigate bureaucracies. These public sector professionals may be sought after by healthcare organisations and peak bodies to help them to work more effectively with government agencies on related activities. They are likely to enter CHIS work in management, governance and planning roles. CHIS professionals may enter the field via post-secondary education programmes or via workplace training. Digital media tertiary qualifications with subjects in public health and social innovation are an excellent starting point for high school leavers wishing to gain a broad understanding of the whole health system whilst becoming qualified in digital media. Also, formal digital health education programmes focusing on clinical informatics, health information technology and

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healthcare law and management continue to emerge, and these may include aspects of CHIS work. Otherwise, generic degrees—media and communications, marketing, digital media, journalism, visual and graphic design, software engineering and information systems—can be education pathways for digital, media, business and ICT professionals who may find opportunities to develop those skills in a CHIS role. Relevant training targeting consumers may be available from non-governmental organisations and consumer health peak bodies. Some of the skills required to function effectively as a CHIS in a digital age require training in specific computer software, platforms or tools. There may be growing opportunities for traditional education providers in public health and in digital media to co-design specific training for future CHIS professionals.

Where Do Consumer Health Information Specialists Work? Healthcare consumers interact with every part of the health sector. Therefore, specialists who can design, develop, deliver and manage information to meet their changing needs may work in any part of the system. Healthcare consumers are also increasingly receiving information and making decisions about their health needs in non-clinical environments such as supermarkets, public transport, retail centres and while watching television programmes (Sarasohn-Kahn 2019). While we acknowledge the emergence of these “other” places of work in informing and supporting healthcare consumers, the health sector workplaces are discussed in this chapter. Healthcare providers from the local family doctor’s practice to large hospitals, multinational health insurers and device manufacturers are all increasingly looking to engage CHIS professionals as part of transforming service delivery models and empowering people to become partners in their health and wellbeing. The high volume of interactions these organisations have with healthcare consumers and the rapid pace of change, be it regulatory, clinical or technical, drive the need for CHIS professionals to work within these organisations. The primary funding sources for public health, government and public sector organisations, will require CHIS professionals to support population health initiatives. Different political structures and funding models for health and care delivery globally create varying organisational structures and responsibilities of CHIS professionals within them. Given the range of work undertaken, there is a need for CHIS professionals in all subsectors that relate to health data, information and knowledge management. These roles may be within a ministry or health department or as part of a specific government agency set up for consumer health needs, population health management or a national health information service—such as Healthdirect in Australia, 1177 in Sweden and NHS Choices in the UK. CHIS professionals are found working in media organisations. Traditional (newspaper, television and radio) and new (social, online and mobile) media organisations play an essential role in improving a community’s health and digital literacy. These organisations may work in partnership with others, particularly health

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organisations, peak bodies and government agencies, to promote population health initiatives and emergency health information distribution—such as activity to inform citizens about COVID-19. Many countries may also have a legal mandate that any news or content that may cause harm must provide appropriate information and links to support services (Skehan et al. 2020). Consumer health advocacy plays a vital role in future health reform and ensures an ongoing commitment to improving literacy, influencing policy, and improving the quality of health services for all (Wells 2016). Peak bodies, not-for-profit organisations (NFP) and non-governmental organisations (NGO) represent the interest of healthcare consumers as a whole (e.g. Australia’s Consumers Health Forum) or for a specific need such as disease type (e.g. a Cancer Foundation), accessibility (e.g. the Royal Society for the Blind), or socioeconomic factors (e.g. homelessness, rural and remote communities). Similar bodies may operate at a local, national or international level. Healthcare providers and industry organisations such as pharmaceutical companies and insurers are increasingly seeking to work with these groups and the CHIS professionals within them to ensure they provide information and services that meet the needs of the healthcare consumers they serve. The global healthcare industry reached a value of over $8,000 billion in 2018 (Wood 2019) and is segmented into pharmaceuticals, medical equipment, consumables, medical software and consumer health technology. The consumer health-tech market is a growing area of investment (Consumer Technology Association 2021). Technological advances such as mobile, virtual reality and autonomous vehicles are transforming the healthcare industry by addressing healthcare consumer needs in innovative ways, encouraging new models of engagement to improve prevention and management of various health conditions. CHIS professionals may work in any of these industry segments to support the creation of quality consumer healthcare information. As the health sector transforms into a participatory model, the information created by industry must evolve to be digestible for healthcare consumers and the traditional clinical and business audience. This will, in turn, drive more demand for quality CHIS professionals who understand how to create compelling, engaging content for a diverse audience, including the modern consumer. Professional services firms, sometimes known as the Big Five (Deloitte, KPMG, Accenture, EY and PWC), are often contracted by government and industry to support strategic business activities, to assess and then implement changes across the healthcare sector. Professional services firms have identified digital transformation as a growth area and have undertaken significant acquisitions of digital media and marketing agencies to provide additional skills to their portfolio of services (Gianatasio 2017). CHIS professionals may work directly as consultants at one of these firms or a subsidiary agency (e.g. Fjord, an interactive agency acquired by Accenture as part of its Accenture interactive services division). A growing “professional gig economy” of highly skilled independent professionals is flourishing and already changing the shape of the professional services industry (Source Global Research 2018). Independent CHIS consultants are a growing component of the workforce. They support health organisations in coping with short-term spikes in workload, meeting changing regulatory demands, exploring

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new business opportunities, and researching strategic opportunities. CHIS professionals working as self-employed contractors may work with multiple organisations at the same time or work on short-term projects.

Principles of Professional Practice There are no formally accepted principles of professional practice in place for CHIS professionals. However, accepted codes of professional practice may be applied to core functions within the speciality, namely those guiding clinical governance, information management, digital media and design. Analysis of existing professional practice principles, codes of ethics and definitions of quality from healthcare, digital media, and information management suggests that the following principles of professional practice may be appropriate for CHIS professionals. Sources included the US Institute of Medicine’s six domains of healthcare quality, the Health on the Net (HON) Code, digital media design principles, general principles of information ethics applied to health information and general principles of service design. 1. Consumer-centric—Provide information and services that are respectful, tailored and responsive to an individual’s preferences, needs and values. Minimise information entry by pre-populating fields with known information according to privacy rules. 2. Equitable—Ensure that information is accessible, engaging and non-threatening for audiences such as culturally and linguistically diverse groups, First Nation people, LGBTQIA2S+ communities, people with disabilities, other vulnerable or marginalised people. 3. Relevant—Understand the end-to-end consumer experience and how the information provided fits in a broader context of an activity. Present the information or request for information in a suitable, meaningful and contextual manner. 4. Trustable—Understand the responsibility of providing, capturing and managing consumer health information and ensure that it is valid, credible, reliable and safe. 5. Current—Information must be current, up to date and where appropriate, use modern innovative methods for presentation and capture that improve the consumer’s access to current information choices. 6. High quality—To facilitate trust and enhance the experience and long-term engagement, information must be professionally produced and consistent using common language and style guides. 7. Digestible—Accessible for all and provided so that consumers can digest the information and process it, as clear, standardised and structured information when shared across different platforms. 8. Actionable—Information should be engaging, practical and directional to support consumers to make informed decisions as to what steps to take next. 9. Beautiful—Information should adhere to design principles to ensure it is engaging and attractive and supports the consumer rather than distracting from the information or task.

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Conclusion Consumer health information specialists play a vital role in shaping the future of the health industry. Professionals who embrace the diversity of skills, experience and knowledge required to deliver the breadth of activities detailed within this chapter will position themselves as leaders and provide quality outcomes for the healthcare consumers, carers, providers and community whom they serve. Everyone is a healthcare consumer, whether actively seeking treatment or managing a condition as a patient, taking steps to remain healthy or merely existing in the modern digital age. The future healthcare consumer will be in the driving seat of their health outcomes. To interact with this new automated and personalised health landscape, healthcare consumers will require quality consumer health information available in any form they choose; tools to create, manage and share their health information; and the literacies to make informed decisions about the information they consume and the information they choose to share. These changes provide a transformative opportunity for new forms of work in the health sector to address the rising demand from an ageing population, an increase in non-communicable diseases, and complex care patients, alongside significant regulatory requirements. Improving healthcare can proceed by acknowledging the important role of CHIS professionals within the health information workforce.

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Chapter 10

The Globalisation of Health Information Work Kathleen Gray

Abstract  Globalisation in the health sector challenges conventions about the health workforce, such as where people in this workforce are trained and based geographically, how their work is conducted and regulated for safety and quality and consequently how health information flows. By reviewing scholarly literature on the COVID-19 pandemic, we can see some aspects of the globalisation of health information work, the people doing this work, the innovations they brought to this field of work and the challenges they faced. Three categories in this emergent workforce are described: distinct types of specialists, multidisciplinary coalitions of various kinds of workers, and non-professional Internet users. This review pinpoints some kinds of work that will tend to promote greater globalisation of the HIDDIN workforce and some types of health information challenges that globalisation cannot address without deliberate cooperation at the highest levels in the workforce. Keywords  COVID-19 · Global health · Internet · Public health · Infodemic

Introduction Globalisation refers to the economic interdependence of countries as a result of heightened international transactions of goods and services, free flow of capital, cross-border migration, and rapid diffusion of new technologies (International Monetary Fund 2002). It is reinforced and intensified by the rise of Internet capacity

K. Gray (*) Centre for Digital Transformation of Health, University of Melbourne, Melbourne, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_10

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and capabilities that support teleworking and the gig economy (Todoli-Signes 2017), and it affects the health sector. Governments that are signatories to the pertinent United Nations treaties accept that they have a legal obligation to ensure that their citizens have access to timely, acceptable, and affordable health care of appropriate quality and to provide for the underlying determinants of health (World Health Organization 2017). Still, even though we think of health services being the jurisdiction of local and national governments, a combination of market forces and biomedical and technological innovations enables some people to supply and consume health care that is beyond the jurisdiction of specific national governments (Ormond and Toyota 2018). Global economic forces influence cross-border supply, consumption abroad, and the presence or absence of trained personnel and of commercial interests (Smith and Hanefield 2018). Globalisation of this kind challenges conventions about the health workforce, such as where people in this workforce are trained and based geographically, how their work is conducted and regulated for safety and quality and consequently how health information flows. Some aspects of health care continue to rely on the physical proximity of the patient and the professional, but many other aspects are becoming more possible to accomplish through virtual care (Webster 2020). Similarly, some of the data, information, and knowledge technologies that provide the infrastructure for health care are developed and maintained in and for distinct localities, but digital health is recognised as a global industry (Conor 2020). What effect does the globalisation of health services have on that section of the health workforce that we call HIDDIN (Health Informatics, Digital, Data, Information and kNowledge management) and, conversely, what influence does the HIDDIN workforce have in the globalisation process? Changes in the world of work driven by the COVID-19 pandemic offer a chance to answer these questions. The pandemic accelerated remote working, disrupted traditional modes of supply and demand and distribution, and also amplified well-known problems such as the digital divide and antisocial uses of the Internet, generally. Information management and information technologies moved to the forefront across social and economic activities, including health care and health research; the ensuing changes to the nature of work—crowdsourcing; process automation; virtual workplaces and teams; the shift to online learning—have the potential to affect the HIDDIN workforce as much as other areas of the health workforce, or perhaps more (Barnes 2020; O’Leary 2020). The global response to the pandemic had a number of elements that were drivers for new forms of engagement in HIDDIN work. The spread of the virus outpaced localised disease management and triggered renewed attention to World Health Organisation programmes and functions (Tangcharoensathien et al. 2020). Health authorities lost control of public health analysis and reporting standards, as social media placed the power of communication in the hands of anyone who chose to comment publicly (Hou et al. 2020). Thus, the World Health Organisation declared that the pandemic was accompanied by an “infodemic:” “From selling fake coronavirus cures online to a cyberattack on hospitals’ critical information systems, criminals are exploiting the COVID-19 crisis, the United Nations has warned, as it also

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steps up its fight against a proliferation of false information about the virus.” (World Health Organization 2020). The idea of an infodemic originated during the SARS epidemic when it was explained thus: “A few facts, mixed with fear, speculation and rumour, amplified and relayed swiftly worldwide by modern information technologies, have affected national and international economies, politics and even security in ways that are utterly disproportionate with the root realities.” (Rothkopf 2003). As the COVID-19 infodemic unfolded in 2020, health information scientists were quick to grasp the information innovations the health sector needed. One area of innovation was managing new volumes of information about sample collection testing and reporting; about critical equipment and drugs in supply chains, and the movements of supply vehicles; about tracking hospital bed and ICU availability; about monitoring patients and people in quarantine (Lal, in Dwivedi et al. 2020). Another area was systems work generated by newly designed COVID-19 apps: managing privacy, crowdsourcing, donating data, tracking cases, and updating models (O’Leary 2020). Another was developing new cost-effective models, frameworks, policies, and applications for delivering healthcare in a post-COVID-19 world (Barnes 2020). Lastly, there was a need to investigate ”the centrality of information in the COVID-19 disaster; what constitutes information systems value and success in the context of the COVID-19 pandemic; technology’s role in the behavioural, temporal, societal, and organisational aspects of the pandemic; and the negative role of information systems in the COVID-19 pandemic” (Ågerfalk et al. 2020). The health information work opportunities that arose have been categorised into two dimensions—“fighting against a pandemic” and “adjusting to a new normal”— and six themes—“expanding digital surveillance,” “tackling the infodemic,” “orchestrating data ecosystems,” “adapting information behaviours,” “developing the digital workplace,” and “maintaining social distancing” (Pan and Zhang 2020). Categorising this globalised work is a start, but it is hard to know who has the expertise to match it. Certainly, no one in any workforce had prior experience of dealing with the combination of an infectious disease demanding such broad and deep decision-­making and a digital information ecosystem with so many sources and channels (O’Leary 2020). It is important to those interested in the evolution of the HIDDIN workforce to capture the reality of what the associated health information work entailed and to determine who was doing it.

A Rapid Review of the Literature This chapter summarises and synthesises globalisation trends in the type of HIDDIN work that the pandemic triggered, the people doing this work, the innovations they brought to this field of work and the challenges they faced. It does this through a rapid review of scholarly literature, with the rationale that “Health care decision makers often need to make decisions in limited timeframes and cannot await the completion of a full evidence review” (Polisena et al. 2015, p.1). Relevant articles were identified by a Google Scholar search in August 2020, “allintitle: COVID-19

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information.” Removing citation-only items and items with no English language full text yielded 400 papers. Papers were shortlisted first by inspecting titles for words that suggested activities or agents of information management/service/systems/technology. Included were items emphasising activities to do with the source/ use/analysis of information and suggesting related work practices (e.g. job*; labo*r; perform*; role*; responsib*; staff*; task*; work*; etc.). Included agents could be healthcare workers, technology workers, administrators, entrepreneurs, patients, and citizens. The next step was to screen the abstracts of included items for references to human actions or agency. Shortlisted items were analysed to find content that mentioned or implied globalisalisation; open coding was used to identify key themes such as inter-country similarities in work, transborder work, worldwide work. If such content was found, further content was sought about details of what the work constituted and details about where, how and by whom the work was done. Final details were extracted about how this reflected changed work practices and what major issues arose in doing the work. This reduced the number of papers to around 100. These were winnowed by: removing multiple papers by the same author (e.g. author SL Pan), including only one representative paper about a topic (e.g. the influence of Twitter), and selecting papers so as to give a perspective from different parts of the world. The resulting 41 (roughly 10% of the items initially retrieved) offer a sample of substantial papers from many different disciplinary forums to illustrate the globalisation of health information work in the first year of the COVID-19 pandemic.

The Globalised Health Information Workforce Table 10.1 summarises the types of people identified as doing COVID-19 related health information work and the work they did in the papers selected for review. It also summarises innovative elements of this work and challenging aspects of it. Table  10.1 arranges the workforce into three categories. Firstly, distinct types of workers were identified, for example, geographic information systems specialists: librarians; publishers. Some were very specific—proprietors of Facebook; pharmacists in low-income countries; plastic surgery society staff. Secondly, multidisciplinary coalitions of various kinds of workers appeared often. One example was community organisers collaborating with health workers, technology companies, and policy-makers; another example was workers in government agencies collaborating with others in non-government organisations and international news agencies. Finally, a non-professional type of worker emerged strongly from the literature—the health information consumer, the social media influencer, the anonymous Internet user in the crowd.

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Table 10.1  Globalised health information workers and their COVID-19 health information work The workers (Source) Distinct identities Artificial intelligence researchers (Su et al. 2020)

Computer scientists (Way et al. 2020)

GIS specialists employed by governments (Rezaei et al. 2020)

Government agency copywriters (Ojo et al. 2020)

Government officials (Baveja et al. 2020)

The work: Details Retrieving: Combine information extraction with state-of-­ the-art question answering and query focused multi-document summarisation techniques, selecting and highlighting evidence snippets from existing scientific literature in response to a query Translating: Online multi-lingual disease information for scientists and the public, using neural machine translation of English to and from French, Italian, German and Spanish Mapping: Use of geospatial data technology for systematic collection and representation of details and overview in each disease-affected country Writing: Making online information readable by the general public, using the SMOG index and US DHHS recommendations for reading levels Valuing: Producing and deploying a government-backed mobile app for citizen contact tracing through ubiquitous, real-time mobile technologies, with clear specifications of roles and responsibilities

Innovations

Challenges

A website for real-time interactions and open code for broader use and refinement

Breaking down a user query and rephrasing complex question sentences into several shorter and simpler queries that convey the same meaning remains a challenge for natural language processing.

Minimising the need for translators and interpreters

Automated translation performance can deteriorate without additional training. There is no simple way to do a comparative evaluation of online translation services. The data may not be transparently available to citizens. Mapping alone is insufficient for disease control.

Fast accurate tracking of disease location and spread

Specific attention to readability and comprehension of public health communications

Much public health information on many official websites are not understandable to people without a university education.

An explicit value proposition for citizens to participate voluntarily in information sharing

Official gathering of citizens’ personal information poses significant challenges in a free society.

(continued)

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Table 10.1 (continued) The workers (Source) Government policy-makers (De Coninck et al. 2020)

The work: Details Persuading: Ensuring the viability of conventional local news journalism to reach masses by providing incentives for local media outlets to publish reliable information Coordinating: Health Filtering clinical professional information quickly and society staff accurately to update (Kearsley and clinical guidelines and Duffy 2020) protocols information in global professional and scientific channels Guiding: Health sciences Work to identify and librarians (Naeem and Bhatti collate, also create tools and resources for 2020) differentiating fact from false information, and publish these online Disseminating: Journal editors (Song and Karako Rapid review and publication of scientific 2020) papers, using journals’ online advance publication platforms

Librarians (Ali and Gattti 2020)

Innovations Journalism infrastructure grants

Challenges Business models for “old” media are losing out in competition with global new media.

Professional organisations joining forces to produce a strong united platform with one message

There is a risk that professionals will experience alert fatigue, and important information may be lost in the “noise.”

There is an increase in New public training and education roles for stories on social media that at first may appear librarians credible but later prove false.

Dissemination of reliable information, including transparent methods of identifying cases, sharing data, unfettered communication, and peer-reviewed research, is hard to do quickly. Libraries should do Joint responsibility Communicating: more about sharing among public, Creating and academic, medical, and other useful disseminating information, e.g. information to the public specialist librarians histories from those and to researchers by who are recovering; monitoring emerging advice on lifestyle resources and tools from habits that can reduce publishers and agencies the risk of disease. (across South Asia) Papers published simultaneously in English and Chinese to communicate rapidly in the international scientific community

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Table 10.1 (continued) The workers (Source) Media platform proprietors (Wahgre and Seth 2020)

Media professionals (Andreu-Sanchez and Martín-­ Pascual 2020)

Pharmacists (Khatiwada et al. 2020)

The work: Details Managing: Influencing quality of information shared on social media platforms by supporting the fact checking and credible journalism; modifying how “explore” sections function or how search results are structured; algorithms for manipulating content to reduce users’ interactions with misinformation; new or modified policies about content and user behaviour Visualising: using actual or scientific photographs of the virus and avoiding aesthetically retouched illustrations from stock image suppliers Disseminating: providing information, including management approaches, psychological advice, home care and safety, and medical management of chronic comorbid illnesses, in addition to their usual role in providing drug information—both face-to-face in hospitals and central health sites and via teleconferencing and social media sites of drug information centres (in low-income countries)

Innovations New relationships being formed between governments, society, and platforms

Challenges Disinformation is overwhelming.

The first electron microscope images SARS-CoV-2, the coronavirus that causes Covid-19, were captured between January and March 2020. Upgrading of drug information centres to disease information centres

The original, real images are not interesting or informative for most audiences.

Centres are understaffed for this work, and some work (e.g. mental health) is not within professional scope.

(continued)

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Table 10.1 (continued) The workers (Source) Plastic surgery society staff (Al-Benna 2020)

Pseudo-experts encouraged/ employed by government agencies (Sukhankin 2020)

Public health agency staff (Li et al. 2020)

Social media company proprietors, specifically Facebook (Tewfik et al. 2020) Coalitions Artificial intelligence and information retrieval communities (Esteva et al. 2020)

Authors, editors, peer-reviewers, and journal publishers (Rahimi and Adabi 2020)

The work: Details Standardising: recommending information on websites of these groups in 67 countries, based on comparative review and analysis of content and reference to generic guidelines Misinforming: Content creation of falsified data, pseudo-­ science, and conspiracy theories for social media platforms and state-­ controlled satellite news media Delivering: using YouTube to deliver timely and accurate information in non-text formats reaching millions of viewers worldwide Sharing: Enabling individuals and organisations to publish information, while taking minimal responsibility for content accuracy or quality Retrieving: Specialised search tools, using semantic search engines over the scientific literature, developed by teams from 20+ organisations around the world in response to a global research challenge Disseminating: Emphasis on COVID-19 related work through open access agreement among 100 academic journals, societies, institutes, and companies

Innovations The first initiative to evaluate information provided by national plastic surgery society websites

Challenges Presently there is little integration and standardisation between websites, governing bodies and plastic surgery societies.

Amplifying malicious public messages

Propagation of fake news via global social media complicates access to reliable information.

Governments engaging with people who use video platforms to find health information

Over one-quarter of the most viewed YouTube videos contained misleading information.

Volume and popularity of health misinformation at new levels

There is no easy policy remedy for the preponderance of inaccurate information.

Combining advanced search technologies, a Wikipedia and PubMed pre-trained question answering system, and other tools for optimal retrieval

Systems that retrieve incorrect results to support frontline healthcare decisions could jeopardise trust and safety.

Research and data on COVID-19 publicly available rapidly and widely

Rushed peer review can lead to unethical publishing.

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Table 10.1 (continued) The workers (Source) Citizens, platform operators, and researchers (Rodriguez et al. 2020)

Community organisers, health workers, tech companies, and policymakers (Xie et al. 2020)

Developers and community leaders of knowledge organisation systems (KOS) (Zeng et al. 2020)

GIS developers and epidemiology researchers (Joao 2020)

The work: Details Overcoming misinformation: Promoting and sharing a great volume of scientifically-based messages to counter misinformation

Innovations Heightened imperative for scientists to increase their use of social media for dissemination

Challenges Misinformation has novelty value, leading active social media users to spread it, at the same time as there is long-term lack of investment by mainstream media in science journalism. Increased financial Initiatives that take Combining: resources are needed advantage of Coupling high-tech community organisers for rapid, well-­ online and low-tech coordinated who understand the offline social needs of their residents implementation. connectivity services to know the local deliver trustworthy resources available and information to older adults, family caregivers, can move quickly and healthcare providers The knowledge and Decentralised Exchanging: standardised search and skill to deal with Adapting classification information overload retrieval systems, systems, taxonomies, and resolve semantic interactive maps and controlled glossaries, charts, repositories and conflicts are unevenly thesauri, subject distributed. headings, ontologies, and databases other types of KOS to integrate diverse information sources and targets as a base to support decision-making using semantic technology, data mining and machine learning approaches, graphs, and integration of often Internet based systems The tool’s output is Rapid creation of a Visualising: vulnerable to source Dashboard for worldwide global public tool to errors in data input. pandemic visualisation, report and monitor outbreaks as they drawing upon a data unfold, with gathering strategy, a transparent data commercial GIS tool, sources and an open online platform (continued)

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Table 10.1 (continued) The workers (Source) Government agencies, media, NGOs, international news sources, and opinion leaders (Fu et al. 2020)

The work: Details Disclosing: Making information public, using public channels to release Information about cases, medical knowledge, containment policies and compliance

Communicating: Exercising professional duty to bring evidence-­ based knowledge to the general public, using new channels such as webinars or online discussion groups Governing: Health Hospital clinical data information governance over data managers and supplied to proprietary research ethics health databases to board members (Robinson 2020) reduce risks associated with the assembly and analysis of large health data sets Categorising: Media professionals and Teaching information medical librarians literacy and health (Ashrafi-Rizi and literacy to citizens so that Kazempour 2020) they differentiate credible information and understand appropriate behaviour in times of crisis Health care professionals, epidemiologists, and infectious disease experts (Ma et al. 2020)

Mobile app proprietors and users (Vanoni et al. 2020)

Volunteering: Using volunteered geographic information collected across many different locations, collated by the developers of a city map mobile phone app, which gathers large amounts of open-source data generated by transport authorities, local transit authorities and individual users, supporting epidemiological analysis

Innovations New areas for public policy and administration have arisen due to the scientific and social nature of the disease

A collective “infodemic patrol” to engage media outlets, scientific agencies, governments

International clinical dataset as the foundation for COVID-19 research

Challenges Patient privacy and public interest must be balanced and transnational coordination of information disclosure is constrained by anti-globalism geopolitics. Misinformation is still widespread.

Disregard data governance can undermine research credibility.

To find the best behavioural model for dealing with crises, it is necessary to have knowledge and awareness about information production and dissemination infrastructure and familiarity with information types. It is a complex matter A novel way to to determine public evaluate the interest uses of effectiveness of government restrictions personal mobile phone on personal movement data. A typology of pandemic information types: valid, comforting, perplexing, mis-, dis-, shocking, contradictory, doubtful (untrusted), progressive, postponed, confidential

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Table 10.1 (continued) The workers (Source) Public health agency scientists and technologists (Yu et al. 2020)

Researchers and citizens (Aizawa et al. 2020)

Researchers, pharmacists, and allied health professionals (Razonable et al. 2020)

Scientific authors and publishers (Homolak et al. 2020)

Teachers and parents (Iivari et al. 2020)

The work: Details Curating: Scientific information about genomics and other precision health tools, through Web portal access into results of database extraction Aggregating: Multi-lingual information aggregation, through a research collaboration that combines crowdsourcing, crawling, machine translation, and a topic classifier Appraising: Rapid literature reviewing teams with membership, from epidemiology, infection prevention and control, diagnostics, therapeutics, clinical care, and public health with results published at an authoritative medical information website Publishing: Adopting open science principles and a mindful approach to data access when publishing research findings and data sets Integrating: Incorporating information management and digitalisation topics during the sudden, unexpected digital transformation of children’s basic education (Finland and India)

Innovations Information filtering on an important aspect of the disease

Challenges Continuous updating of the database is labour intensive, and translational impact is not guaranteed.

The complexity of the Potential to enrich cross-language sharing data processing slows of quality information the performance of the system.

Medical information Multidisciplinary partnership among data has increased rapidly and exponentially. analysts and clinical experts

Submission-to-­ publication time for most journals reduced tenfold.

It is possible that we are sacrificing the quality of journal content in exchange for speed and reach.

Making visible how significant the field of information management is in supporting and understanding digitalisation, and that through design and digital technology, we can make the world better

Established technology, practices, skills, attitudes, and cultural factors may be barriers to digital transformation.

(continued)

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Table 10.1 (continued) The workers The work: (Source) Details Amorphous workforce Correcting: Community champions among Dispelling misinformation and false marginalised claims about vaccination; groups building collaborations (Schiavo 2020) among clinicians, policy-makers, community leaders, families, academia, and organisations from the public, for profit and non-profit sectors Consumer health Consuming: information users Choosing information wisely by using health (Phillips 2020) library and information service guidelines and tools Releasing: Crowdsources Providing alternative (Khan Pathan public access to 2020) information about infections, active cases, deaths, critical conditions, infected areas, hospitals, and questioning official information, via crowdsourced contributors to electronic media Searching: Health Global internet searching information-­ using Google search seekers engine (Parshakov et al. 2020) Online opinion leaders (OOL) (Yin et al. 2020)

Innovations

Challenges

Improving health and media literacy as well as civic literacy

A great deal of vaccine-related misinformation is spread via social media.

Public participation in the creation of the “vaccine against misinformation”

Misinformation is widespread and readily transmitted.

New interest in critiquing official versions of events

Even some of the richest and the most developed countries’ governments may not be releasing all information to the public.

Search intensity depends not only on a nation’s disease dynamics, but also on cultural characteristics of that nation Original message Propagating: communicators Promoting the understanding of OOL development of public opinion on social media behaviour, so as to platforms, by forwarding/ design effective communication reposting messages strategies accessible to large numbers of followers

Governmental communication strategies may not address cultural characteristics effectively. Mechanisms are not in place for persuading OOL to shape public understanding of official messages quickly and appropriately.

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Table 10.1 (continued) The workers (Source) Prominent commentators (Matthews 2020)

The work: Details Misinforming: Social media and mainstream media posts, via global online platforms, building up scientifically unfounded criticisms of the national response in a particular country (e.g. in Japan) Diffusing: Social media Super-spreading of large influencers amounts of information (Alqurashi et al. over social media 2020) platforms such as Twitter, Instagram, YouTube, Reddit, and Gab. Sharing: Social media Tweeting and retweeting users, esp of disease information in Twitter (Singh et al. 2020) conversations from 217 countries

Innovations Renewing debate about the part played by culture and associated behaviours in reasons for differences among countries

Challenges Media attention to cultural differences may mask failures in governance and government responses, and may reinforce stereotypes about cultural behaviours.

A global view of Arabic language information dissemination

A focus on the language of content does not necessarily tell us about reliability of information.

Possibility to predict global disease outbreak and spread by geotagging conversations

Misinformation is being circulated around the world in more than 30 languages.

HIDDIN Workforce Implications The force of globalisation, as illustrated by the pandemic, puts pressure on our current understanding of the health information workforce; the health information work and workers described during the pandemic are less conventional and more inclusive than we might expect. This review found that the infodemic that accompanied the pandemic created many vital and stimulating globalised roles and responsibilities for health information workers. However, role titles do not readily convey that specialised health information work is being carried out in those roles, and workers taking on the roles do not always align with existing professions’ definitions of who “belongs” in this workforce. From this review, it seems too that many HIDDIN roles remain invisible; particular types of workers not mentioned in the literature here are IT infrastructure managers, data quality managers and informaticians. Descriptions of work of this kind within national boundaries certainly were published, but details of how global work was being done by people in these roles lacked the scholarly profiling that one might expect. The review highlighted two key features of the globalisation of the HIDDIN workforce. One feature is the emphasis on participation in complex, changing interprofessional workgroups to achieve impact at scale. This is not a field where sole practitioners can succeed, except as consultants to workgroup sponsors and

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convenors. The other feature is the ascendency of influential efforts by online communities of laypeople, doing roles across a spectrum of health information work. This underlines the importance of formally trained professionals being ready and willing to advise and support this active, worldwide part of the health information workforce. Familiar terms to describe HIDDIN work appeared in a range of variants: retrieval, curation, integration, and sharing. There were also less conventional ways of describing the work: personalisation, evasion, framing, orchestration, even war. It is dismaying to note that a professional specialisation appears to be emerging to perform work that is agnostic about the scientific quality and benefit of health information content, but skilled at synthesising and distributing (dis)information to serve geopolitical purposes. This review pinpointed some kinds of work that will inevitably tend to promote greater globalisation of the HIDDIN workforce. Examples include large-scale international data sharing using standards and communication protocols; near real-time scientific information dissemination through open research platforms and new scientific publishing models; rapid multichannel public health information sharing and validation using a considered mix of formal and informal Internet outlets; curation and analysis and representation of data about Internet searching and social media sharing topics. The lesson of the pandemic is that the professionals conventionally identified with the HIDDIN workforce are meeting some—but not all—of the needs of a globalised health sector. Globalisation brings challenges that HIDDIN workforce professions have not resolved; resolving them is a major a test that may determine whether these professions have a future. The challenges include imperfect and uneven health data collection; multi-lingual scientific information dissemination; incompatible jurisdictional laws and regulations regarding individual data privacy; inconsistent information behaviours of citizens under social and economic stress; commercialisation constraints on the flow of information, e.g. about drug development. Deliberate efforts to overcome these challenges are required at very senior levels in the professions concerned before a formally qualified specialised HIDDIN workforce can be recognised as a serious influence on the way the world works with health information. Limitations of the rapid review method, considering only the period up to the end of July 2020, mean that it will need expanding and updating to develop a fuller picture. As well, the generic health information search terms may have obscured insights into the globalised workplace for specialised data management and knowledge management workers that the pandemic has created. Another limitation is that studying the context of the pandemic is by no means an exhaustive way to explore the topic of health information workforce globalisation. Further research might reveal transborder work trends driven by a particular multinational corporation’s dominance in the health information systems market, or by widespread consumer demand for personal health information technologies such as an app or a wearable, or by an international agency sponsoring a major investment in health information technology infrastructure. Indeed, one or more drivers of this kind may gain

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momentum from humanity’s post-pandemic realisations about our shared health care needs. If so, there are likely to be further worldwide repercussions for HIDDIN workforce skills, supply, demand, and logistics.

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Part IV

Impact

Chapter 11

Leadership Roles in the Specialist Digital Health Workforce Tiffany I. Leung, Karen H. Wang, Terika McCall, and Frits van Merode

Abstract  Leadership roles in the specialist digital health workforce are incredibly heterogeneous. No one single career pathway suits all aspiring leaders and as society and healthcare become increasingly digitised, the opportunities for new careers grow. Content and methodological expertise form solid foundations for leaders, combined with clear organisational strategy and vision, and the ability to foster work environments for diverse and highly skilled teams to collaborate on innovative projects and programs. This chapter covers foundational principles and important normative considerations for leaders in digital health and provides selected examples. Keywords  Leadership · Diversity · Inclusion · Professional development

Introduction Health Informatics, Digital, Data, Information and kNowledge (HIDDIN) careers and professional training pathways are highly diverse, leading to a variety of specialised leadership roles. Leadership roles can encompass several scopes which are not mutually exclusive: clinical or non-clinical responsibilities; research across

T. I. Leung (*) · F. van Merode Care and Public Health Research Institute/Maastricht University Medical Center+, Maastricht, The Netherlands e-mail: [email protected]; [email protected] K. H. Wang · T. McCall Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_11

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basic, translational, and applied health and services or implementation sciences; local, regional, national, and international governmental or non-governmental organisations, which may involve interagency or intersectoral collaborations; entrepreneurial pursuits; and more. This chapter covers foundational principles and important normative considerations for leaders across this workforce. Such principles may be applicable across various career pathways. Leadership profiles and project examples in this chapter are by no means a complete catalogue of possibilities. To achieve a leadership role, training or hands-on experience is foundational in developing the identity and scope of work for a digital health specialist. Training, for clinicians and non-clinicians, may involve degree-seeking masters-level programs, doctoral programs, or accredited and non-accredited postdoctoral training programs (e.g., Accreditation Council for Graduate Medical Education accredited clinical informatics fellowships for physicians, or National Library of Medicine and other fellowship programs, in the United States of America [USA]). Training program titles in relevant disciplines (e.g., library science, computer science, management science, etc.) may or may not identify health informatics or health analytics explicitly. Formal training programs may not be required; future leaders may instead perform on-the-job work with or without pursuing online learning, acquiring certifications, or working as visiting scholars in academia or interns in industry. However, progressing to a HIDDIN workforce leadership role with a specialised degree or certification is becoming the norm as more customised training programs become available. Leaders must build on a foundation of expertise, developing a set of advanced skills and competencies that advance their organisation’s strategic vision and priorities. In specialist digital health work, common and universal leadership skills draw from multiple disciplines, including organisational management, social sciences and organisational behaviour, organisational professionalism and ethics, and even compassion research and training. Due to the diversity of work and training pathways towards specialist roles in digital health, leaders are vital agents towards achieving the organisation’s goals.

Leadership Traits and Skills In general, effective leaders enable organisational growth, learning, and resilience. Leaders are responsible for engendering organisational professionalism (Egener et al. 2017), which can translate into ensuring a physically and psychologically safe organisation and work environment with a sustainable work culture. This may further include enacting organisational policies that promote diversity, equity, and inclusion in the organisation’s workforce to achieve the organisation’s mission. The important roles of community and patient partnerships as well as ethical operations and business practice also should be prioritised; this chapter explores these further through a health equity lens on leadership.

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Regarding operations and business practices, a well-known paradigm in this field is the learning health system, in which information technologies, standards, and policies are designed in support of healthcare infrastructure that facilitates evidence-­ based and practice-based healthcare delivery, public health activities, biomedical research, quality improvement, and other population and social services (Friedman et  al. 2010). It describes the idea of a virtuous cycle of data-driven practice and evidence-based medicine advancing healthcare (Chambers et al. 2016; Leung and van Merode 2018). To build a learning health system, or lead an organisation that will engage in partnership with others, a leader may need to be especially adept at recognising their unit’s needs in terms of operating structures and design. A leadership role engages in at least one of two functions, strategy making and providing an organising function for their unit or organisation. Strategy making is explored in subsequent sections of this chapter. The organising function, in which the leader is responsible for best matching the design and structure of the unit to achieve its goals within its host organisation, is vital to achieving the unit’s goals. For example, organisational design and structures implemented or enforced by leaders may uniquely impact clinical care settings, where logistical and management control can be essential in supporting multiple specialised units or departments providing patient care services. Several units may need their own specialisation with their own logistics devices to meet the needs of different patient populations. However, this can lead to fragmentation and even siloes in healthcare services. At the same time, these units may not be large enough to operate autonomously, for example, because of the use of infrastructure and technology that is connected with other units (e.g., clinical departments or specialties, or functional departments or services such as laboratory or imaging services). Consequently, this requires logistical controls that have a high degree of integration and are capable of taking the specific requirements of each unit into account. In other words, a leader takes into account not only the entire organisation, but also the resources that it can provide for the differentiated parts to function well and achieve their aims within the whole system. Thus, a leader may need to consider organisational development in system design, with their effort dedicated to the right combination of integration and differentiation. From this perspective, integration is defined as the joint effort of organisational subsystems to affect organisational tasks (Lawrence and Lorsch 1967); and differentiation of organisational units concerns the segmentation of the organisation into subsystems. They perform some of the organisational tasks, but do so in a way that specifically aligns with the task environment (Lawrence and Lorsch 1967). The necessary level of integration is determined by the perceived need for joint decision-making. The degree of differentiation is strongly related to the need for specialisation of subsystems and the specificity of their task environments. Box 11.1 gives an example of when a leader might be called on to balance these organisational structures within a hospital system; this is challenging because organising methods are often very different (van der Ham A et al. 2020). Full integration of healthcare and patient logistics will only be achieved

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through an effective shared real-time decision-making culture combined with good real-time data provision and physical and digital infrastructures. Box 11.1 MijnIBDCoach: Building a Cross-Disciplinary and Sector Platform Gastroenterologists, in partnership with both academic and non-academic hospitals and a private company, Sananet, developed the MijnIBDCoach, an information technology platform in support of patients with inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis (de Jong et al. 2017a). Patients with IBD often are knowledgeable in managing their disease with the right guidance and information. The MijnIBDCoach makes it possible for patients to contribute and transmit data on a daily basis to their clinical care team. As a smartphone or web-based app, instant feedback can be provided to the patient as needed to facilitate self-management. At the same time, a nurse monitors the health status of the patients via a dashboard. If necessary, the patient may be advised to come to the Gastroenterology (GI) clinic and such a visit can be arranged with a very short access time. For example, in a traditional clinical workflow, patients with Crohn’s disease have at least one scheduled annual check-up, which is also quite common for other chronic conditions. Diagnostics and surveys of patients’ symptoms may be performed, with variable value for the annual visit, as the disease may be quiescent and minimal meaningful information is exchanged between the patient and care team. Additionally, the annual visit appointment can be made weeks or sometimes months in advance, which can contribute to access delays overall for the GI clinics. Furthermore, if disease activity increases and the patient becomes increasingly symptomatic, an intervening visit to the GI clinic would be needed. However, with the development and implementation of MijnIBDCoach, the number of mandatory yet low-value annual visits decreases considerably. The platform allows for collection of patient-reported outcomes and other data from the patient that allow for coordination and completion of on-demand visits on the same or next day, promptly addressing disease instability or changes, without compromising patient care quality (van den Heuvel et  al. 2017). Consequently, with digital health leadership, the clinical workflow shifts largely away from routine outpatient clinic appointments to remote monitoring, patient self-management and engagement, and care on demand, with a statistically significant reduction in utilisation of outpatient clinic appointments and a reduction in access time (de Jong et al. 2017b).

Leaders should have a working and updated knowledge of current ethical standards in the field. Especially in digital health, policy changes may significantly impact operations with regard to all data activities and infrastructure (Séroussi et al. 2020). In particular, privacy, confidentiality, and informed consent issues are

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commonly encountered; however, lack of transparency and explicability of clinical information systems, particularly artificial intelligence and machine learning applications, is ethically problematic (Hübner et al. 2020). Furthermore, the primary and secondary uses and applications of those data are subject to ethical principles, such as the use of information technologies “for good and not evil” (Goodman 2020). Leaders also have a responsibility to foster an organisational culture conducive for workers to achieve the unit’s goals. This may include reducing hierarchy and power distance between employees and their supervisors, and between employees and their coworkers, in order to promote collaboration and reduce unnecessary siloing and competition in the workplace. Enabling interprofessional teamwork may be especially pronounced in digital health work because it involves knowledge work, such as information management, information exchange, and knowledge sharing. Barriers to knowledge sharing and exchange can stymie advancement of the organisation or even the field overall (Lifshitz-Assaf 2018). Another aspect of leadership in work culture is to foster an ethical work climate, in which institutionalised normative systems best match the types of work and communication among workers (Victor and Cullen 1988). Why and how to foster an ethical work climate with special attention to principles of well-being, diversity, equity, and inclusion are discussed next.

Digital Health Leadership for Workforce Well-Being Digital health leaders are central in influencing those practicing and pursuing HIDDIN careers, as well as shaping the work experiences of those who are end-­ users of information technologies and digital health tools that are designed or deployed by their organisation. Such influences can be especially vital in sustaining the productivity of diverse HIDDIN professionals. Consequently, promoting workplace well-being warrants special attention to the design of a healthy and sustainable workplace for the digital health experts needed to facilitate the design, deployment, and evaluation of technologies that are equitable, accessible, and as diverse as the populations that they serve. Despite the rapidly expanding body of literature describing the impact of poorly designed, deployed, or evaluated technologies on clinician burnout—for example, electronic health records—there remains a stark lack of focus on well-being or burnout in the HIDDIN workforce, but we can draw inferences from available data. In market research surveying nurses’ experiences using electronic health records (EHR) in 2014, only 15% of the more than 14,000 registered nurses across 40 U.S. states believed that their information technology (IT) department was knowledgeable and receptive to suggestions on improving electronic documentation. Among IT professionals surveyed in the United Kingdom in 2013, two-thirds of IT administrators considered their jobs stressful and 63% of IT staff felt as stressed or more stressed than friends or colleagues, with management the most commonly identified source of stress (35%). In academia, generally, graduate and postgraduate

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studies are also increasingly recognised as periods of high stress and risk for poor well-being; in one survey of over 6000 graduate students from six continents, more than one-third of respondents sought professional help for depression or anxiety that they attributed to their doctoral studies (Woolston 2019). An example of leadership on digital health specialist work-life concerns and workload sustainability comes from the USA, where addressing the well-being of physician trainees is an accreditation requirement for graduate medical education programs, including clinical informatics subspecialty fellowships. Leaders should be aware of, even if not engaging specifically in, organisational structures and culture that positively influence employees’ well-being and engagement at work. Work engagement is seen as an antidote for burnout and can be promoted through increasing job resources, such as social supports at work, work support (e.g. supportive supervision, team efficiency, access to mentorship and sponsorship, etc.), increased work control such as worker influence over schedule and work planning or pace, and availability of performance feedback (Demerouti et  al. 2001). Among clinicians, work engagement is associated with better work ability (Mache et al. 2013), fewer medical errors (Prins et al. 2009), and increased patient-safety-related behaviours and attitudes (Daugherty Biddison et  al. 2016), even though better patient care experiences may not be a result (Scheepers et al. 2017). Promoting a culture of psychological safety, in which workers have a perception that there are no negative consequences of taking interpersonal risks, is foundational in enabling idea sharing and interactions (Edmondson and Lei 2014) and important for a productive organisation. However, these constructs have not been systematically studied in digital health workers, highlighting a gap in knowledge about leadership practices to support this workforce, including enhancing their professional development and mitigating attrition and turnover. System-, unit-, and individual level contributors to HIDDIN workers’ well-being can also perpetuate workplace inequities and low workforce diversity. To avoid consequences that can be costly to the mission and goals of the unit or organisation, digital health leaders can better prepare to recognise and respond to HIDDIN workers’ needs.

Leadership for Diversity, Equity, and Inclusion Promoting diversity, equity, and inclusion (DEI) and incorporating DEI principles into everyday leadership benefits the HIDDIN workforce and broader society. Leaders in digital health should understand and have competencies in identifying the organisational structures and systems that foster racism, discrimination, and bias, so that they may take corrective actions in order to cultivate the next generation to be a more diverse workforce and to develop informatics tools and methods that do not facilitate inequities (Metzl and Hansen 2014). The aim in embracing such principles is to develop the tools necessary to counteract structural forces, thereby achieving health equity and justice. Leaders can drive healthcare system changes when they carry an awareness of both the structural determinants of health and also best practices in addressing how health inequities negatively affect the health of a

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population. For example, an equity-sensitive leader would have an understanding of data quality issues, such as data missingness on race or ethnicity or how such data are collected regarding the population their organisation serves, to ensure that services are equitable. Communicating this type of need and knowledge may be necessary to garner executive sponsorship for institutional resources to pursue this aim. From a system perspective, the learning health system concept does not explicitly include social determinants of health as a component of personalised care (Nwaru et al. 2017). As healthcare systems strive towards the idea of a learning health system, a digital health leader with an understanding of local and regional health inequities could be well-positioned to recognise and incorporate such considerations explicitly into service evolution. Providing healthcare and related services to populations occurs within a complex adaptive system, in which each agent is interdependent and interacts with others, never acting in isolation; changes in one part of the system may influence changes in other parts of the system, intended or not (Waldrop 1993; Plsek and Greenhalgh 2001; Jaaron and Backhouse 2017). Ultimately, a leader in this environment is positioned to foster the needed creativity, learning and adaptability necessary to achieve organisational goals; the present Knowledge Era or Digital Age needs leaders who can address adaptability challenges—rather than technical challenges characteristic of the Industrial Age (Uhl-­Bien et al. 2007). Organisational leadership should design a just, equitable, diverse, and inclusive environment, as part of a larger effort towards organisational professionalism (Leung et al. 2019) and promoting a healthy workplace. Prior studies demonstrate that diversity of workforce fosters innovation and creativity (Phillips et al. 2006). Informational diversity results when people in groups can bring different perspectives, opinions, and information, social diversity in terms of their lived experiences based on societal hierarchies created based on racism, sexism, xenophobia, and other dimensions; such diversity offers unique considerations in group work (Phillips 2014). However, no single occupational category captures specialists in digital health; this is needed in order to provide data for leadership action to lift diversity in the workforce. This complicates further efforts to measure and ensure DEI in leadership. One study found that between 2017 and 2019, men held leadership positions in 74.7% (71 of 95) of USA academic biomedical informatics programs and 83.3% (35 of 42) of clinical informatics fellowship programs (Griffin et al. 2021). There were no women clinical informatics fellowship program directors in 2018, even though the first such programs had been accredited 4 years earlier (Longhurst et al. 2016). Recently, professional societies are beginning to lead by example, investing in structures to increase DEI.  For example, the American Medical Informatics Association (AMIA) developed targeted programs for racial and ethnic minority undergraduate college and university students and women in informatics; deployed a salary survey to begin understanding career and pay gaps; formed a Women in AMIA committee; and convened a Diversity, Equity, and Inclusion task force. A 2017 International Journal of Biomedicine and Healthcare special issue profiled renowned women informaticians towards this aim (Nyänken and Whitehouse 2017). Digital health leaders can promote DEI through updating institutional policies and practices—for example, hiring practices with an awareness of implicit biases; equitable parental or partner leave policies—or sponsoring DEI task forces or

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committees to advise on such important considerations. Additionally, sponsoring and mentoring potential leaders is important to sustain diversity in the career pipeline, especially towards influential leadership roles. Leaders as sponsors may publicly support and advocate for protégés in support of their career advancement, recommending them for promotion, connecting them with other leaders, promoting their visibility, and essentially pulling them up the ladder with them (Hewlett 2011; Travis et al. 2013). Gender disparities remain problematic, as do racial, ethnic, and cultural disparities, in perpetuating inequitable access to career advancement opportunities (Hewlett 2011). Digital health leadership is challenged further to tackle the effects of systemic racism as a key structural determinant of health and its role in creating health disparities, which re-emerged into mainstream dialogue in 2020. This was a result of glaring disparities in COVID-19 morbidity and mortality in Black, Latin, and First Nations populations as compared to White populations in the USA and other countries (Selden and Berdahl 2020; Chowkwanyun and Reed 2020). Also, it arose from the Black Lives Matter campaign fuelled by the violent death of countless Black people at the hands of police or other community members, and the wide acknowledgment of overt and covert racism faced by Black, indigenous, and people of colour (BIPOC) populations daily over generations, to the detriment of their well-being. Previously unacknowledged bias in clinical medicine, such as predictive algorithms and machine learning (Rajkomar et al. 2018; Vyas et al. 2020), highlight the necessity of ethical, equitable applications of digital health. Structural racism embedded in information systems applications has been described in population access to social services (Eubanks 2018), arrest and incarceration rates (O’Neil 2016), access to and use of digital services like search engines, and even access to broadband internet (Noble 2018). In the last case, lack of access to broadband internet can widen the digital divide for populations who are unable to, for example, access telemedicine services during the COVID-19 pandemic (Rodriguez et  al. 2020). Furthermore, data privacy and security as well as digital literacy, or the capabilities that one must develop to live, learn, and work in a digital society (Cooke 2018), remain vital ethical considerations in addressing health inequities (Rivera-­ Romero et al. 2020). The importance of structural and social determinants of health and their effects on the well-being of individuals and their communities is increasingly recognised (Daniel et al. 2018; Byhoff et al. 2020). Digital health leadership can sharpen the focus on precision medicine initiatives and data collection efforts to capture social and behavioural determinants of health; both seek to leverage the large volumes of data and develop new data streams to improve individual and population health (Institute of Medicine 2014, 2015). Leaders need to raise awareness in the health sector and wider society that digital health data are critical in contributing to efforts to measure and facilitate individuals’ and communities’ abilities to intervene on these determinants of health (Veinot et al. 2019). However, considering the volume and sharing of such data, applications of information and computer technologies need to be ethical if they are to offer pathways towards health equity rather than

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further driving health disparities among patient populations. Thus leaders need to ensure patient engagement through community-based participatory informatics research, patient advisory boards, and citizen science. Leadership in designing innovative healthcare service delivery options may be one way to help overcome some structural biases in healthcare systems. With high-­level knowledge of health policy as well as access to voluminous data on vulnerable populations with poor healthcare access, digital health leaders have the opportunity to achieve a mission of social service. Box 11.2 describes a case of data-driven leadership and innovation, for a traditionally underserved population. In the USA, Medicare insurance coverage is available to all Americans 65 years of age and older and those with a disability; at the State level, Medicaid insurance coverage for low-­income individuals is possible, but may vary in terms and services offered due to differences between States’ health policies. Many people eligible for either or both insurances (dual eligibility) frequently have higher than average rates of chronic diseases, including mental health disorders, and may live in “health care deserts,” where patients have trouble accessing a physician even if they are insured (Porter et al. 2017). Box 11.2 Oak Street Health: Launching Community Healthcare Service Networks Oak Street Health is a network of primary health centres in the USA, where a novel care model guides the design of both medical and non-medical services for elderly populations in low-income neighbourhoods. Due to their focus on the vulnerable elderly, they are also entering the social domain (Porter et al. 2017). Integrated health promotion services are offered proactively, whether elders are healthy or have medical conditions that require more management. Healthcare is delivered by a team (doctor, nurse, and care manager) seeing the sickest patients every 3 weeks and those who are healthy, every 3 months. Team nurses also visit patients in the hospital as needed, however prevention plays a prominent role in this care model, especially since the Oak Street Health business model is globally capitated and therefore allocates financial resources towards outpatient services and away from emergency rooms and hospitals. Leaders of this model report a 40% reduction in hospital admissions. Throughout the patient’s care continuum, their health data are collected and integrated, as are their patient experience scores. The strength of the model lies in managing the overall coherence of both the patient process and the care system, through integrated data management and funding based on bundled payments. The model is unique in that patient flow to and from the hospital is moderated by primary care networks, and not by the hospital as frequently occurs elsewhere in the USA. Leadership of this innovative model of integrated primary care, constantly driven by digital data applications that directly benefit the community, serves to manage and improve the value-cost ratio of care.

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Archetypal Leadership Roles One quite extensive list of specialist digital health jobs and leadership roles has been published previously (Mack 2016), and the American Health Information Management Association publishes an interactive career map as well (https:// my.ahima.org/careermap), however these still underrepresent the breadth of digital health leadership. The diversity of the workforce and the evolution of the domain lend to many possible leadership roles within or bridging different sectors, as well as involving work on a local, regional, national, or international scale. Here, we briefly describe a range of existing roles in and across academic, executive and government sectors, as a way to convey the leadership possibilities. Academic leadership roles characteristically encompass directing education and research programs, some funded by governmental or institutional resources, some by philanthropic grants and/or industry funders, some by fees from individuals. These leaders are well-positioned to advocate for DEI, to advance science and practice in the field, and to influence the next generations in the workforce. These roles come with expectations that they will provide thought leadership and engage with integrity in defining education and research standards, overseeing accreditation and certification of individuals and organisations. This leadership is often accomplished through service on boards and committees of health agencies and professional societies. Recognisable executive roles in health services include chief information or informatics officers (CIO), chief technology officers (CTO), and chief digital officers (CDO) all of whom may have varying but overlapping scopes in strategy development and implementation across an enterprise. A health services chief medical informatics officer (CMIO) may be responsible for system-wide information technology applications across clinical settings, including compliance, research, and training. For example, during the COVID-19 pandemic, this role may have overseen rapid implementation of telehealth and remote monitoring capabilities and workflows, as well as rapid development of analytics and decision support tools reflecting best available guidance on the diagnosis and treatment of infected patients. An emerging leadership role in health systems, the chief clinical informatics officer (CCIO) may be similarly responsible for leveraging health information systems, for example, reducing medical errors, promoting evidence-based care, and optimising the effectiveness of electronic documentation while minimising the administrative burden (Sengstack et  al. 2016; Kannry et  al. 2016). Kannry et  al. (2016) offer a concise overview of additional executive roles that draw upon specific clinical disciplines, including chief nursing informatics officer (CNIO), chief pharmacy informatics officer (CPIO), chief dental informatics officer (CDIO), as well as the CMIO role. Another new executive leadership role is the chief research informatics officer (CRIO); the scope of this role most often includes developing and managing infrastructures, such as data warehouses, managing clinical research services, such as those needed for clinical trials, data request services, and research data governance

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(Sanchez-Pinto et  al. 2017). All these executives may have a significant role in advancing the applications of new information technologies in practice. For example, one such advancement could be establishing an innovation shop or lab, to create a collaboration space for developers, engineers, clinicians and patients, with the capability to disconnect and experiment while developing care processes but at the same time bridge administration and control of technology—a concept called bimodal IT (Mesaglio and Mingay 2014) or two-speed IT (Avedillo et  al. 2016; Bossert et al. 2015). Government leadership roles may overlap with academic and executive leadership roles at times, and people may move in between such roles. During the COVID-19 pandemic, prominent governmental digital health leadership drew from public health institutes and research centres tasked with population health surveillance, including healthcare service utilisation, to guide rapid development of public health policies. Even in non-pandemic periods, leadership of infectious disease surveillance and response, as well as chronic disease surveillance, remains vital to guide policy and budgetary decisions towards population-level health promotion. Formulating and implementing information technology strategies in publicly funded health care services also requires skilled leaders; examples include electronic health record modernisation in primary care or hospitals, health care information systems for military personnel, and community disability and aged care IT systems. In addition to governmental leadership roles, leaders of nongovernmental or non-profit organisations also have an important role in convening multiple stakeholders across disciplines and sectors around core values of digital health. Highly visible health executive leadership roles necessarily exist across all sectors of academia, government and non-governmental agencies, and in the private sector, from small start-ups to large multinational corporations, including public and private health service providers and their suppliers throughout the digital health ecosystem. Leaders in these sectors must be prepared to work together and to work across sectors.

Conclusion There is no one single pathway for an aspiring digital health leader, and more than likely, such a leader will be positioned in more than one discipline or sector. Also, leaders are likely to face increased demand for a diverse, inclusive, and well-­ supported HIDDIN workforce. One final consideration in taking on a leadership role or following a leader is to avoid the romanticisation of leadership, or a tendency to view an organisation’s successes (or failures) as attributable to a leader (Meindl et al. 1985; Mayo 2017). This chapter has offered several principles that leaders can enact to facilitate positive change in their immediate workplace, in the HIDDIN workforce, and in society.

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Chapter 12

The Specialist Digital Health Workforce Impact on Access and Equity Anna G. Shillabeer, Lawrence Sambrooks, and Aydan C. Shillabeer

Abstract  HIDDIN (Health Informatics, Digital, Data, Information and kNowledge) work aims to provide ubiquitous healthcare for all: Global healthcare is now datadriven. Health information and support services are accessible 24/7. Wearable devices and smartphones connect people with their health data and enable monitoring by a clinical team for patients at home. Given these tools and technologies and the vast potential they provide for patient access and equity, this chapter asks why so many people still do not have access to a quality healthcare system and why many who do report inequity in treatment and care. Several cases highlight issues with the design and availability of these tools for marginalised and minority groups and lower socio-demographic groups. The chapter argues for a revision to the required skill set for the HIDDIN workforce and a need to bring these professionals out of the silos that they have traditionally worked in and into the communities they aim to serve. Keywords  Marginalised groups · Minority groups · Access · Equity · Socio-­ economic status

Introduction Human populations enjoy longer and higher quality lives than at any time in history. The application of evidence-based practices into health in the 1900s facilitated a doubling of a human life span by the end of the twentieth century. However, by the

A. G. Shillabeer (*) · L. Sambrooks · A. C. Shillabeer College of Sciences and Engineering, University of Tasmania, Hobart, TAS, Australia e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_12

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beginning of the twenty-first century we had arguably reached the limit of human capacity for such radical progress to be sustained by the work of individuals unaided by new technology. The ability for a healthcare practitioner to adapt, incorporate new knowledge from the rapidly growing big data stores and meet the global drivers for ubiquitous, cross-cultural health care through experimentation and observation in real time has been surpassed. In the twenty-first century reliance on empiricism has faded and digital data is the fundamental tool in healthcare innovation and experimentation. A strong drive to embed technology into clinical practice to continue to realise better diagnostic, treatment and prognostic outcomes has expanded the healthcare toolbox and patient expectations beyond the imagination of previous centuries. Data-driven digital health facilitates rapid responses, decision making, improved and personalised patient care and, with considered implementation, can ensure greater access and equity to high-quality healthcare globally. For these goals to be achieved, we need an enhanced workforce, with cutting-edge technology skills, the ability to build human-centric systems and a global framework for implementation. The potential for digital health to achieve the stated goals and address community drivers has been evidenced since the beginning of the twenty-first century. In 2005 the World Health Organization (WHO) released resolution WHA58.28 and encouraged all member countries to develop infrastructures and strategies for leveraging the potential of computing technologies to provide ‘equitable, affordable and universal access’ to quality healthcare (WHO 2020a). This guideline was followed up in 2013 with resolution WHA66.24 which urged members to develop strategies and frameworks to ensure interoperability and a digital health platform underpinned by ‘policies and legislative mechanisms linked to an overall national e-health strategy’ (WHO 2020a). Two years later, in 2015, the 2030 Agenda was adopted by the UN and aimed to ‘bridge the digital divide and to develop knowledge societies’ (WHO 2020a). Throughout these resolutions there has been a call for closer collaboration and cooperation between the many stakeholders to achieve the defined goals. In the WHO Thirteenth General Program of Work, 2019–2023 it was stated that the aim was to ensure universal health coverage for a billion currently un-­ serviced people around the world (WHO 2020a). Achieving this would require digital health initiatives to be targeted towards those who currently experience barriers in accessing the current health system structures. It would also require expertise that is not currently available within the domain of traditional clinical health workers or the HIDDIN (Health Informatics, Digital, Data, Information and kNowledge) workforce. As a final guiding statement the WHO Global Strategy on Digital Health defines digital health as ‘the field of knowledge and practice associated with the development and use of digital technologies to improve health’ and address the social determinants of health (WHO 2019, 2020a, b). The aims should be met through the use of new technologies including artificial intelligence, the Internet of Things, big data analytics and robotics (WHO 2019, 2020a, b). These skills are not commonly found in HIDDIN education degrees (Shillabeer and Anderson 2018). The WHO strategy also outlines four strategic objectives with short-, medium- and long-term goals to provide a standardised global platform from which to effect the

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implementation of digital health systems and provide a sustainable minimum standard of healthcare for all. Although strategies, frameworks and capabilities are being built, we have not yet reached sufficient global workforce capacity to implement such a broad scale healthcare revolution. The changing healthcare environment has expanded the expectations of the communities it serves. Current healthcare systems are being driven to deliver personalised health care and preventative health management for complex populations, ensure rapid upscaling and deployment to address emerging health events, and meet local and global community drivers within myriad social, political and financial environments. The WHO has provided a set of overarching principles to guide the development of digital health platforms and systems, but the question that remains is why are we still experiencing global barriers to access and equity in healthcare around the world? This chapter explores some of the challenges that impact access and equity and discusses how the HIDDIN workforce can help to break down the barriers and progress towards a state of universal healthcare for all.

Digital Health as a Facilitator of Access and Equity Digital health innovation and data dependent technologies present significant benefits for patients. In Australia, a single CSIRO telehealth application was able to provide a 53% reduction in hospital admissions and a 40% reduction in mortality, this is by no means a lone case (Hospital and Healthcare 2020). The demand for a skilled workforce to sustain such benefits is well documented, but the provision of appropriate competencies and capacity has lagged. Primary stakeholders must work together while being mindful of the diverse skill sets and drivers. The HIDDIN workforce often must concentrate on systems to provide back-end data and information to support health services management. Healthcare providers require front-­ end solutions that provide instant access to accurate patient information, reduce their administrative burden and help develop new treatments and medicines (Australian Digital Health Agency 2017; Deloitte Centre for Health Solutions 2013). Digital health must be a solution for all members of a community, not only those who are technology literate (Azzopardi-Muscat and Sorensen 2019). Meanwhile, members of the community are demanding systems that are user friendly, give them control, ensure that their information is kept confidential and implement digital tools that are safe and secure (Australian Digital Health Agency 2017). The definition of ‘user friendly’ varies amongst community members, hence developing a digital health solution for a whole community is an almost intractable exercise for HIDDIN workers or healthcare providers without sufficient in-depth understanding of the many sub-communities they are trying to support and serve. This chapter posits that there is an ongoing shortfall in HIDDIN professionals who are suitably trained to facilitate ubiquitous data-driven health care, and this is contributing to persistent inequity in access to healthcare across the globe, especially for disadvantaged populations (Frenk et al. 2010; Kiyumi et al. 2016; Drehobl et al.

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2012; Shadmi et al. 2020). We outline the potential impact of the HIDDIN workforce towards ensuring healthcare is available to all and present cases to demonstrate the positive impact that these professionals can have if they are visible and able to engage at the grassroots level. The (WHO 2011) advocates that all people should have equal opportunity to receive good healthcare and be able to realise their full health potential, without disadvantage. Unfortunately, many social, geographic and political constraints exist to achieving this (WHO 2019; Shadmi et al. 2020; Azzopardi-Muscat and Sorensen 2019; Deloitte Centre for Health Solutions 2013); these constraints form the social determinants of health (SDOH), which are frequently used to measure the potential outcomes of health initiatives and underpin health inequity and disparate access (WHO 2019). Digital health and the workforce that develops and manages it have a significant role to play in reducing this inequity but the real value of this work is yet to be felt. Health in the general population can be very much enabled by digital tools and technologies, wearable and implantable devices, smartphone apps and digital trackers to name but a very few; their monitoring and feedback functions may trigger health-seeking behaviours, provide digital access to the health system, reduce disparity and increase access and equity (Australasian Telehealth Society 2017). Although the HIDDIN workforce undoubtedly contributes to the design, development and testing of these technologies, this workforce does not generally interact with the population that uses them or directly influence the behaviours that produce health benefits. To reduce the impact of the SDOH, and present opportunities for the HIDDIN workforce to emerge from the shadows, a new professional model is needed, that takes a collaborative approach beyond traditional research and clinical workforce settings to overcome social disparity. Digital health personal devices, and clinical tools and technologies, often unfortunately exacerbate the potential to increase disparity and inequity for those who are already marginalised. Low socio-demographic or minority cultural groups either may not be able to afford wearable devices or may not have interfaces and information in their native language. Unaffordability excludes such groups from enjoying the potential benefits of information that informs decisions to seek access to healthcare services (Brewer et al. 2020; Azzopardi-Muscat and Sorensen 2019). Given the increasing reliance on and pervasiveness of digital health brought about by the COVID-19 pandemic, it is important to take a broader approach to the design and development of digital interventions. SDOH impede equity already and may be compounded by perceived, or actual, racism and culturally inappropriate digital designs that marginalise those who stand to benefit most (Hardeman et al. 2016). Access and equity in healthcare services are a community problem and are felt hardest by those in marginalised communities (Brewer et al. 2018; Deloitte Centre for Health Solutions 2013). The most significant gains in access and equity can be achieved by addressing the needs of such communities. Digital health should seek to contribute to achieving the WHO Millennium Goals and reducing the SDOH burden. Given the complexities of the populations and environments where digital health is deployed, a visible and capable HIDDIN workforce can have an important

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impact on community health. Several case studies demonstrate the potential impact of focussing on marginalised communities with the lowest levels of access and equity within the health system. These cases support the position that the digital health developers and champions need to move out of conventional traditional research and clinical settings for their full potential to be realised.

Digital Health Work in US Communities The American Health System is frequently cited as a health system that fails to serve marginalised communities; these communities may mobilise and create innovative solutions—and research opportunities for digital health workers. One such example is the African-American community in Minnesota which historically experienced a higher incidence of CardioVascular Disease (CVD) than white Americans and almost twice the mortality rate (Minnesota Department of Health 2017). Traditional measures to reduce the problem and create awareness had a low impact, attributed to health services’ lack of understanding of community beliefs and social structures leading to a strong community perception of systematic racism and a feeling that the health system tolerated rather than served them (Hardeman et al. 2016). Community improvement initiatives including health were primarily driven through church groups, rather than through health provider organisations or clinicians who were not considered trusted, inclusive entities (Brewer et al. 2018, 2020). Engagement with these marginalised and minority groups by digital health researchers and developers is not common practice and thus perpetuates, and often exacerbates, non-communicable and chronic disease problems in African-American communities (Brewer et  al. 2020). Previous research had identified that African-­ Americans had a similar rate of uptake and ownership of mobile devices as the broader community (around 80%) but were more likely to use smartphones and similar devices to access health information (Ray et al. 2017; Anderson 2015; Pew Research Center 2019). Given the barriers to access described above, reliance on an ‘out of clinic’ health system is not surprising, but it does call for high-quality information and digital health tools to be available in a form that is easily accessible and applicable to these minority groups. The ‘Fostering African-American Improvement in Total Health’ (FAITH!) project was initiated from within the Minnesota community to reduce the CVD burden and facilitate better use of mobile technology as part of a broader community health initiative. The FAITH! premise was to translate community, church-based interventions into a mobile app, so as to have access to information that could be trusted, was relevant to the community and could be used to build and supplement traditional social support mechanisms (Brewer et al. 2020). The project used an approach that included conventional clinical, research and behavioural scientists, some of whom were HIDDIN practitioners, and a range of community partners and church champions, from inception to conclusion. By engaging with community and church leaders, the digital health team developed a bespoke design that incorporated

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clinical and health education considerations, with messaging framed by biblical and spiritual references and culturally appropriate images and infographics (Brewer et al. 2019a, b). Trial users drawn from the community had a participation rate of 100% and retention rate of 98% and gave the app a high rating for acceptability and satisfaction (Brewer et al. 2018); more importantly, the overall impact on participants’ blood pressure, diet and activity was positive (Brewer et al. 2019b). Through subsequent government funding to support clinical integration of the app, the project was also able to influence the target group to access traditional community health centres and services (Brewer et al. 2018). There was a positive impact on equity within and beyond the target population because the general population was able to engage with the app during consultations at participating health centres (Brewer et al. 2020). The multi-level impact was attributed in part to the visibility of HIDDIN practitioners in the community consultations and data gathering processes (Brewer et al. 2018). Marginalisation and systematic racism are not the only barriers to accessing equitable health care. There are also significant barriers among older people with comorbidities, or who are geographically isolated (Fortuna et al. 2018). Reduced access is an issue in particular locations with significant rural and remote regions where populations have lower socio-demographic characteristics, higher than average age profiles, and often a higher health burden but with low clinical capacity to deliver global best practice in healthcare (Fortuna et al. 2018; Australasian Telehealth Society 2017). An application of digital health in these scenarios is supporting self-­ management of chronic mental health conditions. As in the previous case study, development of effective and appropriate digital interventions is best facilitated through a collaborative process, for example the ‘Peer and Technology Supported Self-Management Training’ (PeerTECH) app (Fortuna et al. 2018). This app targets people who have geographic barriers to access, are over 60 years old and have comorbidities which impact on their ability to manage their mental health issues (Fortuna et  al. 2019). This cohort was  recognised as having lower than average engagement with the health system due to a lack of resources (Fortuna et al. 2019). Peer support was already known to help such groups, so this project focussed on training certified peer specialists to provide outreach support and increase access to mental health services (Brewer et  al. 2020). The app took a highly innovative approach, shifting from a medical to a biopsychosocial standpoint. It transformed a traditional mental health management app and moved away from a ‘highly medicalized self-management approach’ and towards a tool focussed on recovery, social engagement and self-advocacy (Brewer et al. 2020). As with the FAITH! project, there was broad community engagement that sought input from the certified support peers, clinical scientists and the patients themselves. The results could not be achieved at arm’s length, and these participants did not have high engagement with the health system so opportunities to collaborate with the usual HIDDIN workforce domain was low. Thus digital health specialists worked within the community to ensure that the app was appropriate for the target user group and that sustained use and acceptance was high. The result was a targeted app that facilitated training for both the patients and their health support peers to enable mental health and

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comorbidity monitoring and management in their home locations, equitably compared with a clinical setting. The project achieved statistically significant improvements in personal psychiatric and medical management (Brewer et al. 2020). This US community-based development approach translated internationally, and led to a similar approach in Uttar Pradesh in India (Deloitte Centre for Health Solutions 2013), where again an app was developed through consultation with women, church groups, local government representatives and community health workers. That app was developed as a guide to enable community health workers and health mentors to counsel women on prenatal and postnatal care. Where previous health initiatives had not addressed high maternal and infant mortality rates, this digital health intervention reduced maternal deaths by 16.4% and infant deaths by 5.2% over 10 years (Deloitte Centre for Health Solutions 2013).

Digital Health Work in Australian Communities Whilst Australia arguably has one of the best health systems in the world, there are still issues with access and equity (Australasian Telehealth Society 2017), and examples where digital health work does not yet serve the community equitably. Australia needs to appropriately expand healthcare services and support to manage an aging population, growing chronic and non-communicable disease rates and the complexities of indigenous health (Australian Government 2011; Australasian Telehealth Society 2017). All of this is underpinned by a need to grow the capacity and capability of the under resourced HIDDIN workforce and develop a strategic and coordinated approach to developing and implementing innovative digital health solutions (Australian Digital Health Agency 2017; Australasian Telehealth Society 2017). The 2017 National Digital Health Strategy (Australian Digital Health Agency 2017) had a focus on the provision of a digital health platform that would enable models of care for supporting the health of rural and remote populations, babies and young children, and the elderly—all very dependent upon non-clinical carers outside of the national health system—and Australia’s indigenous communities were also a focus as a hard to reach population. But whilst access to information is integral in this digital health strategy, unfortunately there is no defined focus on addressing equity as the following examples show. The implementation of a government supported personally controlled health record, My Health Record, (Australian Digital Health Agency 2017; Deloitte Centre for Health Solutions 2013) failed to address how My Health Record would help those who had not engaged with the health system and hence would have a minimal health record. The initiative lacked effective consideration of language and cultural barriers for indigenous people, and a lack of remote area transport impeding access to the clinical health system (Department of Health and Aging 2012). The strategy also intended to reduce medication errors and give prescribing doctors national access to patients’ medication records (Australian Digital Health Agency 2017; Deloitte Centre for Health Solutions 2013)—but people must be able to

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actually  access the system to realise those benefits. Insult was added to inequity when the government changed My Health Record from an ‘opt in’ to an ‘opt out’ model. The strategy document frequently mentioned ‘access’ as a benefit, but this referred to access to health information when the underlying issue of access to health care per se was unresolved. ‘Care at home’ remains a test case for HIDDIN work. 16% of the Australian population live with chronic conditions and many experience issues with poor information flows through their care teams or when being transferred between providers (Australian Bureau of Statistics 2016; Campbell et  al. 2017). Information access would seem to be relevant in new telehealth and ‘health in the home’ initiatives— but many initiatives began unplanned broad scale testing only due to the onset of the COVID-19 pandemic in 2020. Two earlier cases where telehealth implementations have been successful—monitoring chronic health conditions for those in aged care facilities and telehealth facilitated out-patient appointments in three remote towns (Department of Health 2015; Celler et  al. 2016)—show the potential of well-­ informed design and development to improve appointment compliance, reduce hospital admissions and save significant health care system costs. However, evaluation of those cases did not look for, nor find, improvement in access and equity—a missed opportunity to build the evidence-base about the impact of HIDDIN work. In another example, tele-psychiatry has been used as an essential part of Australia’s healthcare system to reach geographically distanced communities (O’Connor et al. 2016). This technology became even more essential during COVID-19 to manage the impacts of ‘lockdown’ measures, which exacerbated already critical levels of mental health problems in rural and remote areas (Meadows et al. 2015). However, effectiveness was impacted by changes in legislation; the initial COVID-19 support package provided by the government mandated that there would be no ‘out of pocket’ expenses for digital consultations (Shadmi et  al. 2020; Hunt 2020), but ­revisions to legislation led to barriers in accessing cost-free mental health support for all and created inequities for many (Shadmi et al. 2020). Further changes enabling digital consultations at no cost were supported only for those who were defined as ‘vulnerable’, not for all who needed mental health counselling at that time. Many people who contracted and succumbed to the virus were from designated ‘non-­ vulnerable’ groups, and many who had minor symptoms and survived were from groups considered highly vulnerable. The legislative implications of telehealth access, not founded on the evidence-base in the HIDDIN disciplines, served to introduce inequity where it did not previously exist. Australia experienced a further broadening of the digital divide as a direct consequence of the lockdowns. Those in marginalised and lower socio-demographic groups experienced reduced or no access to digital health tools and technologies due to prohibitions on access to public technology infrastructure. During lockdown, people who could not afford home Internet also could not leave home to access free wi-fi in public spaces such as libraries (Shadmi et al. 2020). This reduced access to social digital health support structures, and in some cases, prevented access to any healthcare services for those in remote areas that were already underserviced (O’Connor et al. 2016; Ward and Agostino 2020). This case, showing that access to

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the healthcare system during a public health emergency is directly influenced by access to digital technologies in the community, reinforces that building and maintaining a strong grassroots digital health presence is needed to overcome access and equity issues in the future. Similar to the US case studies, for digital health to address access and equity issues in the Australian context, multi-disciplinary teams that integrate peer and community support stakeholders and extended carer networks need to be deployed to reach beyond the mainstream healthcare system (Australasian Telehealth Society 2017; Deloitte Centre for Health Solutions 2013). The economic, geographical, cultural and linguistic needs of the Australian population must become a more fundamental concern in digital health strategy.

Digital Health Work in Responses to COVID-19 The COVID-19 pandemic has been the ultimate case study for the potential of digital health. A range of implementations emerged around the world, and there is a stark contrast in COVID outcomes between countries that have engaged with digital health as part of their management strategy, and those who have not. Countries implementing a two-tier health system response, with one of those tiers based in digital health, have achieved the most positive outcomes. Countries such as Belgium had a first response process utilising digital health systems. Any patient exhibiting symptoms was required to digitally  contact their GP who would then triage, and determine if the patient needed to present at a clinic or hospital, or could stay at home (Shadmi et al. 2020). Those who were only mildly symptomatic were advised to remain at home and were provided with a self-management plan that was supplemented with online or phone follow-up until they were considered safe (Shadmi et al. 2020). This freed up clinical care places for those who needed them most. The process also ensured that resources could focus on a rapid response to those who required clinical care, thus enabling access for more patients. Whilst the response in Belgium was underpinned by digital mechanisms, there was still inequity for those not fluent in the national languages (Shadmi et al. 2020). As in most other countries, the first response was for the majority of the population; minorities and marginalised populations were secondary, resulting in higher incidence of COVID-19 in those populations (Shadmi et al. 2020). For them, inability to understand the public health messaging meant greater community and familial transmission, and reduced access to early assessment and treatment. Other countries such as Israel implemented a secondary response mechanism to enable home isolation with digital monitoring, for non-critical cases. These cases were initially triaged in a conventional clinical setting (Shadmi et al. 2020). By implementing an isolation and recovery at home plan, Israel was able to minimise the impact of COVID-19. Although Israel has a comparatively low number of hospital beds per capita (2.2/1000 vs 3.6/100 OECD average), and therefore a reduced capacity to treat cases, it constrained the spread of the virus to relatively low levels (Shadmi et al. 2020; OECD 2020) and is now investigating further tailored measures to protect more vulnerable

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subpopulations, including those with minimal connectedness to digital platforms (Shadmi et al. 2020). The Israeli Ministry of Health stated that for measures to have a positive impact they must be tailored for the socio-cultural and linguistic needs of each subpopulation; strategies must also include religious and other community leaders and champions at all stages of development and dissemination (Waitzberg et al. 2020).  Although there are many good examples, this discussion must be tempered by examples where digital health and the HIDDIN workforce were not mobilised. In America, the historical minority and marginalised communities, such as the African-­ American population discussed earlier, experienced even greater disparity and reduced access to healthcare during 2020. Historic inequity issues were exacerbated by a lack of financial, housing and food stability; limited budget for healthcare and Internet connectivity; and reduced access to personal and community technology and community social structures—creating barriers to systems that would otherwise provide connectivity to health services and support (Benfer and Wiley 2020). The AfricanAmerican population in Chicago recorded over 50% of all cases in the state of Illinois, and the Navaho Nation had more per capita cases than in any other area of the country (Capatides 2020). This almost complete disconnection of some groups of people from any form of healthcare support unquestionably contributed to America having appalling COVID-19 outcomes, with similar stories in other countries, including Brazil and Guatemala (Shadmi et al. 2020). While the inequity could be blamed on the low socioeconomic status of the population, there are positive examples from countries with similar socio-demographics such as Armenia. In Armenia, hot lines were set up to provide a first-line response and to provide an outbound call service to directly connect with the population and provide information (WHO 2020b). This was strengthened by public health information announcements on radio and television; mobile phone tracking for contact tracing, and social support mechanisms leveraged to care for the vulnerable and disadvantaged in the community. Armenia did not escape the impact of the pandemic, but it did not see the devastation experienced by the aforementioned countries. Whilst not the cutting edge of digital health technology, this case does highlight the potential for even minimal digital connectedness to have a positive impact on population health access and equity. Digital connectedness is a self-evident crucial factor for any digital health initiative, and even if it is only used to mobilise messaging and education through social media, it can improve wellbeing and save lives in times when no information, or misinformation, can be catastrophic.

Bringing the HIDDIN Workforce Out of the Clinic A HIDDIN workforce is central to narrowing the digital health information divide, but the cases presented in this chapter suggest that it is not sufficient to simply have a HIDDIN workforce. For that workforce to adequately address community needs and overcome access and equity barriers, they must be in the community, directly working with the marginalised and disadvantaged groups. Providing systems that

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are culturally, socially and linguistically appropriate is a critical factor in acceptance and adherence; without an understanding of these factors, the HIDDIN workforce cannot possibly meet the requirements of all stakeholders, and their digital health developments will have less impact. The common message from the cases in this chapter is that there is a need for community, patient and clinical engagement in the design and development of digital health solutions most especially where those solutions have an overarching goal to increase access and equity. The call to use digital health to increase access to healthcare suggests that targeted users do not currently engage enough with healthcare practitioners or health-seeking activities. If we accept this, we must also accept that the needs of such potential users cannot inform development that almost exclusively occurs within research and clinical environments governed by national authorities—but this is precisely where the HIDDIN workforce is employed. Digital health is more than a clinical or health system application and must be contextualised if greater access and equity to healthcare is to be achieved. The social structures and engagement mechanisms must both be understood and be a driving force in digital health initiatives. The workforce must actively and visibly engage otherwise they can never truly realise the true value of their work, even if all stakeholders know they exist. It is incongruous to provide applications to facilitate greater access for those who already access the systems and structures designed to support their health care needs. Relying on a standardised one-size-fits-all digital health development approach is ineffectual, as is relying on practising health professionals to speak authentically for marginalised and disadvantaged communities and cultural groups within their practice catchment. What this calls for is a broadening of the digital health profession skill set and a redefinition of the workplace boundaries for the HIDDIN workforce. Given the need to change how this workforce functions if access and equity are to be achieved, there needs to be a redefinition of learning and training pathways and outcomes. A broader, more competency-based, vocational education platform would address the education needs of a workforce that requires both lateral pathways and graduate entry; but we note a number of identified issues with equitable and appropriate access to such education opportunities that present barriers for aspiring HIDDIN workers from under-represented social groups (Shillabeer and Anderson 2018). Some Universities and Colleges now are providing short courses that incorporate micro-credentialing to ensure that the HIDDIN worker can engage with technology, clinicians, terminologies, design and development processes; and also can be mindful of human psychology and behaviour change concepts (Hospital and Healthcare 2020; Shillabeer and Anderson 2018). An example from RMIT in Australia aims to develop graduates who will be ‘design thinkers and leaders in health care and social services with the capacity to reimagine and design prototypes of new digitally enabled healthcare services that incorporate good healthcare design principles’ (Hospital and Healthcare 2020). Such graduates could have impact at the grassroots level, but only if they work through community engagement in non-traditional settings. Course descriptions suggest that graduates will lead digital health innovations and influence ‘transformations within their organisation’ (Hospital and Healthcare

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2020), even though the greatest demand and potential for impact is outside of structured organisations. The Australian Digital Health Strategy among its priorities states the need for support for healthcare workers to understand the tools and technologies available. Upskilling is proposed to be provided to all Australian healthcare professionals by 2022 (Australasian Telehealth Society 2017; Australian Institute of Health and Welfare 2006). However, as discussed in this chapter, without specialist training to develop bespoke digital solutions for marginalised and remote communities, equity issues will remain and access to digital health initiatives will be limited. This presents a significant global problem for indigenous and minority populations. Many of these populations have high levels of non-communicable diseases, significant SDOHs; chronic conditions, low literacy, and cultural barriers to engaging with formal health systems (Brewer et al. 2020; Australasian Telehealth Society 2017; Shillabeer 2015; Australian Institute of Health and Welfare 2006; Australian Government 2011; Azzopardi-Muscat and Sorensen 2019). Without a concentrated effort to connect with, understand and include these people, the HIDDIN workforce will have no impact on access and equity. Whilst governments in many countries have detailed strategies to grow their national digital health capacity, this is not yet adequately supported by education for development and management of digital health solutions to address access and equity. The cases presented in this chapter call for a rethink in how the HIDDIN workforce conceptualises digital health solutions. This work needs to revitalise the principles of altruism, benevolence and personal care upon which healthcare and health research are founded. Patients are people, not diagnoses, and must be considered at the individual and holistic level if health disparities within our communities are to be addressed. This requires a connection with people in their own community, not in unfamiliar and often scary clinical and organisational silos. This can only be possible when our HIDDIN workforce ventures out. Achieving this dissolution of boundaries will facilitate the provision of a healthcare system that is accessible for all. It will also provide actual, rather than alleged, health equity and not cater only for those who already enjoy the myriad benefits of good health and good healthcare. This is a focus area where the HIDDIN workforce needs to be active and seen—the right people need to be in the right place. As suggested through the case studies presented in this chapter, if digital health is to increase access and equity, we need knowledge and capacity at the grassroots. Measures implemented around the world in response to COVID-19 have demonstrated that any digital health implementation is better than nothing. However, a generalised approach does not work, and in fact often broadens the access and equity gap. One size simply cannot fit all, and in fact may fit very few. Digital health solutions that aim to increase access and equity need to be developed as part of multi-faceted teams including clinicians, digital health professionals, community leaders and the intended users themselves. Community engagement, cultural and religious beliefs, and innovative implementation science are key mobilisers of outcomes that can reduce or eliminate health disparity. This can take time and requires a much broader skillset and mindset than is usual in the HIDDIN workforce.

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Digital health initiatives developed and implemented by a well-skilled and widely visible HIDDIN workforce have the potential to enhance the quality of life of every person on the planet. Longevity is all for naught if it is a life of pain and a constant battle with unmanaged chronic disease.

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Chapter 13

The Impact on Safety and Quality of Care of the Specialist Digital Health Workforce Angela Ryan, Brendan Loo Gee, Susan H. Fenton, and Meredith Makeham

Abstract  Digital health technologies play a critical role in the safe, effective delivery of care across health systems. The benefits for patients can be significant; however, evidence suggests that poorly designed and implemented technologies can have unintended consequences, with downstream impacts on patient safety and the quality of care provided. Key to the success of these digital health technologies is the specialist digital health workforce, who possess the requisite skills and expertise to manage and govern the safe use of digital health tools and technologies. This chapter explores the specialist digital health workforce and its impact on healthcare safety and quality practices. Following analysis of a set of case studies from US, Australian and other health systems, we highlight the paucity of attention in this important area, and the need for healthcare organisations to acknowledge and promote the specialist skills required to use digital health safely. These cases also highlight the need for further research and evaluation in this area. Keywords  Patient safety · Healthcare quality · Impact · Policy · Risk

A. Ryan (*) Australasian Institute of Digital Health, Sydney, NSW, Australia B. L. Gee College of Health and Medicine, Australian National University, Canberra, ACT, Australia e-mail: [email protected] S. H. Fenton The University of Texas Health Science Center at Houston School of Biomedical Informatics, Houston, TX, USA e-mail: [email protected] M. Makeham Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_13

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Introduction In complex systems, things happen every day that have never happened before. (Braithwaite 2018).

Digital health technologies play a critical role in the delivery of healthcare across the health system. The digital health strategies of Australia and other nations highlight that the benefits for patients, and the health system more broadly, range from avoided hospital admissions, reductions in adverse medication events and unnecessary duplicate testing, better-informed treatment decisions, and better coordination of care for people with chronic and complex conditions (e.g. Australian Digital Health Agency 2018). However, evidence shows that poorly designed or implemented systems can harm patients and even lead to death (Shortliffe 2010; Ammenwerth et  al. 2008; Ash et  al. 2004; Coiera et  al. 2006). The scale of the problem has grown as digital health has become more ubiquitous. Commonly reported issues in digital health implementations include poorly designed user interfaces that can disrupt clinical workflows, and insufficient training of staff to use health information technology systems during routine care (Kim et al. 2017). For example, a coroner’s report found the lack of hospital staff training on the clinical information system was a contributing factor in the death of a 54-year-old patient mistakenly given high doses of opioids (Daly 2018). The complex adaptive nature of health systems, and the need to acknowledge the impacts of health IT adoption in this context was recognised in a report by the US Institute of Medicine (IOM 2001), following an increasing body of evidence relating to patient injury and death associated with digital health. The report argued that the discipline of safety science needed to be “…better integrated into a health IT-enabled world”, and that…. “…safety is the product of the larger sociotechnical system and emerges from the interaction between different parts of this larger system” (IOM 2001). While the authors noted that this was not a new concept, they argued that effort was required across the health system to support this approach. One of the critical factors emerging in this report and the literature has been the under-preparedness of the health workforce to utilise digital health technologies, and the need for training and education programs to ensure a confidently skilled and capable health workforce. The literature also emphasises the critical role of the health informatician in leading the charge, as the specialist who can acknowledge and manage the risks inherent in building and implementing systems into complex adaptive healthcare environments (Kilbridge and Classen 2008; Schneider et  al. 2014). This report built on other landmark IOM reports: To Err is Human (IOM 2000) prompted calls to make healthcare safer, estimating that 44,000–98,000 lives are lost in US hospitals every year due to iatrogenesis [in Australia, adverse events in hospitals are estimated to range from 2.9% to 16.6% of admissions, with at least half considered to be preventable (Duckett et  al. 2018)]. Crossing the Quality Chasm (IOM 2001) argued for a health system redesign to improve the quality of care. The stimulus to augment the provision of healthcare with

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widespread implementation of electronic health records (EHRs), followed the introduction of the US Health Information Technology for Economic and Clinical Health Act (HIPAA Journal 2018), resulting in 96% of all nonfederal acute care hospitals possessing certified health IT systems (ONC HIT 2017). In Australia too, governments have prioritised digital health to improve service delivery and health outcomes, as have many healthcare providers, stimulating entrepreneurs and developers to invest in new tools and innovative ways to use data to provide health services (Australian Digital Health Agency 2018). This coalescence of efforts is contributing to the evidence base to support the role of digital technologies in modern clinical practice (Makeham and Ryan 2019). This is also true globally, where digital health is expanding. Much of this activity did not contemplate the environment into which the technologies were deployed, nor the preparedness of the workforce to utilise them (IOM 2001). Already these technologies are changing profoundly the way healthcare is delivered, having additional impacts on traditional approaches to health occupations, tasks, and leadership functions. A confident and capable health workforce is required to realise the benefits of digital health technologies (Australian Digital Health Agency 2020), that is, health workers who are schooled in the nuances of the digital environment and who can enter the workplace with a degree of digital literacy, regardless of their specific healthcare role. For those who are specialists in digital health, such as informaticians, confidence and capability rely on an in-depth understanding of the component pieces of digital health, such as legal and regulatory requirements, data governance, data quality, and the nexus with patient safety (Ryan 2019). A digitally capable health workforce is still emerging as an area of focus in the health, education, and training sectors; globally, the emphasis is on enhancing digital health technologies rather than improving the capabilities of the workforce to use them effectively and safely. Nevertheless, key works such as Topol (2019) and the Australian Digital Health Agency (2020) highlight the importance of investing in the workforce—fostering a culture of continuous learning, robust governance, leadership, innovation, and the enablers of change (Wachter 2016), and considering the differences across health professional roles and contexts, especially in particular settings where digital inclusion is low (IOM 2001). Despite the significant investment in digital health technologies, we could find no explicit literature that evaluated the impact of the specialist digital health workforce on patient safety and quality. So, this chapter uses case studies selected to examine workforce education strategies and programs, involving the expertise of digital health specialists, intended to have a positive impact on digital health literacy and patient safety. These give a cross-section of perspectives on the potential for digital health impacts on patient safety and healthcare quality: responding to a pandemic; building a cohesive workforce; managing risk; increasing transparency; operating a virtual clinic; and sharing knowledge globally. Table 13.1 provides a summary of the case studies.

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Table 13.1  Case studies of workforce development for safety and quality of digital healthcare Author (Year) Education program or strategy Annis et al. (2020) Virtual health rotation at the University of Minnesota

Australian Digital Health Agency (2020) Workforce and Education Roadmap

Bellringer et al. (2017) In-house specialist training for senior therapists and advanced clinical practitioners.

Global Digital Health Partnership (2018) Learning Networks (Networkbased Learning Health Systems)

Greenhalgh et al. (2020) Article disseminated via BMJ on the 25 March 2020. OpenNotes (2010) Sharing of clinician notes.

Goal of program or strategy How was the case study implemented? To improve the use of remote monitoring solutions. − Using existing medical resources from multiple local programs to rapidly establish a virtual health rotation. − Collaboration among clinicians, academia, and industry as being instrumental to its success. − Limitations include inefficient manual processes, and limited customisation capabilities. To inform policy for education and training for digital health specialists, and to aid in the development of curricula and resources for the health workforce. − Led by the Australian Digital Health Agency, and supported by leading health informaticians, the Roadmap was co-developed with all Australian jurisdictions, including the Commonwealth, and States and Territories, university and education providers, clinical and consumer peaks, researchers and industry, and healthcare providers and consumers. − The Roadmap defined a set of digital role profiles that articulate the digital health capability requirements for the health workforce in Australia. To provide staff with skills to use digital health technologies. − The Brighton and Sussex University Hospital Trust in the United Kingdom established new roles for senior therapists and advanced clinical practitioners who received specialist training in-house, in the area of digital technologies, imaging, and acute injury management. To provide a policy for education and training for digital health specialists. − The Global Digital Health Partnership (GDHP) was established by the Australian government, in concert with international governments and territories, government agencies, and the WHO. − The specialist digital health workforce from participating countries is organised across five different workstreams, with a number of white papers developed and published in collaboration. To improve use of telehealth. − Tool was developed from previous research findings and official guidance. − Doctors responded to a straw poll with advice. To provide patients with skill to use digital health. − The OpenNotes system was designed by experts at the Beth Israel Deaconess Medical Center, a teaching hospital of Harvard Medical School in Boston, and located in the Division of General Medicine. − The study involved 105 primary care doctors and 20,000 of their patients.

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Table 13.1 (continued) Author (Year) Education program or strategy Queensland Digital Academy (CSDS 2019) Certification, basic computer training, leadership and governance training, university partnerships, grand rounds, and just-in-time training.

Goal of program or strategy How was the case study implemented? To provide staff with digital health specialists skills, and training opportunities to build confidence in using digital health technologies. − The QDA is led by a specialist digital health workforce and operates within the Clinical Skills Development Service on the Herston Campus of Metro North Hospital and Health Service, in Brisbane. − Learning opportunities are maximised through a centrally coordinated hub and spoke model. To improve use of telemedicine. Reeves et al. (2020) − Implementation of the telemedicine system was led by the Online self-guided learning Chief Medical Information Officer and Associate Chief videos on virtual patient care in Medical Officer. conjunction with “boots-on-the− Training material was broadcast and communicated on the ground” resources provided telemedicine platform which provided scripted triaging, onboarding assistance to electronic check-in, standard ordering and documentation, clinical areas enabling rapid secure messaging and real-time data analytics, and specific deployment of telemedicine metrics to key organisational leaders in real time. visits. To provide staff with skills to use EHRs in routine care to Singh et al. (2013) improve patient safety. A set of Safety Assurance − Guides published by experts who have published widely on Factors for EHR Resilience the topic of digital health and are internationally recognised (SAFER) guides that was health informaticians. published by the ONC. − The guides were originally conceived using rigorous iterative methodologies that sourced material on unintended consequences of EHR implementation. To provide staff with skills to use EHRs in routine care to Sittig et al. (2018, 2020) improve patient safety. Implementation of the Safety − Healthcare organisations in Australia and the United States Assurance Factors for EHR self-assessed their adherence to 140 recommendations Resilience (SAFER) guides in contained across the SAFER guides. Australia and the United States. To provide patients with skill to use digital health. Wright et al. (2015) Sharing of clinician notes via a − Patients were invited to examine their records for perceived errors and mistakes. patient-facing web portal

Responding to a Pandemic Following the declaration of a global pandemic in March 2020 (WHO 2020), telehealth became according to some, a 20-year overnight success (Dyer 2020; Warraich 2020), as the only mechanism in many parts of the world by which to deliver healthcare without exacerbating disease spread; “all the red tape [had] suddenly been cut” (Webster 2020). Numerous resources were made available online worldwide to support health services as they struggled to respond to the immediate need to consult with patients via telephone or video.

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For example, the United Kingdom saw sweeping developments in the use of telehealth; a research project that had been tracking the use of video conferencing in Scotland for the previous 6 months described a 1000% increase in use in 2 weeks. Greenhalgh, a noted expert in the evaluation and use of digital health technologies, and colleagues (2020) developed a remote assessment tool to aid clinicians in treating new diseases such as COVID-19. The telehealth aspects of the assessment tool were developed at speed and crafted from published and unpublished research findings and official guidance. Due to the paucity of evidence on how to assess breathlessness over the phone, a straw poll was used to elicit (mostly) doctors’ expert advice. This was a case of marshalling and disseminating key learnings about how to use digital health technologies effectively in a crisis. Reeves et al. (2020) described a US digital health implementation in response to the COVID-19 outbreak, that saw over 300 health employees trained in telemedicine and approximately 1000 video visits scheduled, within 72 hours of the executive proclamation of a national emergency. This was enhanced by a pre-existing telemedicine infrastructure and associated specialist digital health workforce in situ. Online self-guided learning videos on virtual patient care in conjunction with “boots-on-the-ground” resources provided onboarding assistance to clinical areas, enabling rapid deployment of telemedicine visits and real-time responsiveness to the evolving situation. There were challenges, including the need to frequently adjust the system build, the voluminous stream of information and communication being broadcast, and the necessity to continue to provide safe high-quality healthcare to non-COVID-19 patients. This case offers lessons for the rapid deployment and translation of digital health expertise. Annis et al. (2020) describe a remote patient monitoring solution for patients with COVID-19 symptoms that provided healthcare to them while minimising virus exposure and in-patient admissions. The study also examined the impact of an existing rapid technology deployment platform that was repurposed as a remote monitoring solution, staffed by medical students and residents from multiple local programs through the timely establishment of a virtual healthcare rotation program at the University of Minnesota. The authors highlighted the “significant learning opportunities (in virtual health) for medical resources which may have otherwise been sidelined”. The virtual healthcare rotation program was an approach to ensure patients heard a consistent narrative about the care they were receiving, especially given the dynamic and evolving nature of treatment guidance. 2,255 patients reported a 74% satisfaction rating, and patients described a sense of safety overall.

Building a Cohesive Workforce Two Australian examples illustrate healthcare system approaches to define and shape the digital health workforce, as part of acknowledging the impact of digital health tools and technologies on patient safety and quality of care. In 2020, the

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Australian Digital Health Agency’s National Digital Health Workforce & Education Roadmap established eight digital role profiles as a mechanism to guide individuals and organisations in understanding their needs, roles, and responsibilities for the design, development, implementation, adoption, evaluation, and monitoring of digital health technologies. Further, these digital profiles were intended to aid the development of health workforce curricula and resources for the education and training sector, and specifically for developing specialist digital health expertise. The Clinical and Technology Bridging digital profile, for example, includes the functions of Clinical Designer and Specification Advisor; Clinical Information Analyser; Risk and Governance Enforcer; Digital Change Champion; User Tester; Problem Solver; Health Reformer and Innovator; and Quality Controller. The elements of this profile have been tested with this specific cohort for their relevance and applicability, as have the other seven profiles. The Queensland Digital Academy (QDA) is a cross-organisational collaboration working in partnership with multiple agencies, established in 2019. The Academy is addressing the burgeoning need for digital literacy and capability within the State of Queensland’s health workforce in order to assure and improve patient safety. Multiple locations across the State provide staff with education and training opportunities in digital healthcare, literacy, and leadership capability and capacity to help support digital transformation, with many achievements in a short time: The Academy has provided access through sponsorship to the Certified Health Informatician Australasia (CHIA) certification (2021), with additional learning support. It has provided access to basic computer training through a computer fundamentals course and touch-typing practice to ensure no one is left behind in the digital environment. It has provided digital onboarding sessions and electronic medical record introductory sessions to grow the digital capability of key leadership and governance group members. It has led to the development of university partnerships to design undergraduate and postgraduate curricula. It has provided monthly Digital Health Grand Rounds, an opportunity to showcase insights and engage academics, clinical informaticians, and the broader health workforce. It has provided just-in-­time training, leveraging technology to provide training directly to clinicians when needed.

Managing Risk A landmark study compared the effects of two commercial electronic prescribing systems on two Australian hospitals’ inpatients, examining effects on reducing prescribing error rates and on introducing new errors (Westbrook et  al. 2012). Both hospitals experienced statistically significant reductions in prescribing error rates, including a reduction in serious errors. Both hospitals also demonstrated system-­ related errors, which the study noted required ongoing monitoring to detect and address through redesign and user training. While the study did not comment specifically on the makeup of the workforce involved to introduce the two systems, it

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did note that clinicians’ greatest concern regarding systems introduction was their impact on workflows. The study highlighted the complexity of introducing such systems, particularly across an organisation, and the number of work process and cultural factors that must be considered, noting that organisations should commit to resourcing for the long term. It also emphasised the requirement for ongoing surveillance and refinement to ensure the systems are optimised for safer practice. Safety guides have appeared in response to the rapid implementation of EHRs in the United States—from the ONC HIT (2018) and earlier, from Sittig et al. (2014). The SAFER guides are in three broad categories: Foundational guides, Infrastructure guides, and Clinical Process guides. Recommendations within the guides are organised into three broad domains: “safe health IT” (45 recommendations), ‘using health IT safely’ (80 recommendations), and “monitoring health IT” (15 recommendations). The guides were designed to assess risk and eliminate or minimise the unintended consequences that arise from the implementation of EHRs into complex adaptive health systems. The guides also introduce the importance of the development and ongoing promotion of a safety culture across organisations, to identify and mitigate risks to patient safety (Ryan 2019). Risk assessments across eight different healthcare organisations spanning the United States and Australia, examining their adherence to the SAFER guides, found that only 25 of the 140 SAFER recommendations (18%) had been fully implemented, with adherence higher for the “safe health IT” domain (82.1%) vs “using health IT safely” (72.5%) and “monitoring health IT” (67.3%). The authors concluded that despite the availability of the guides, governments need to prioritise policy initiatives to ensure greater awareness of best practice guidelines and embed them as part of EHR implementations (Sittig et al. 2018). Similarly, a paper by Sittig et al. (2020) describe an approach to support healthcare organisations, researchers, funders, and policymakers to better contemplate and address digital health-related patient safety. The paper articulates areas of emphasis across the digital health lifecycle, providing a set of challenges to apply to every implementation to ensure patient safety. The challenges take a stepped approach and are described through nine different stages, across three themes. Design and Development challenges include Developing models, methods, and tools to enable risk assessment; Developing standard user interface design features and functions; Ensuring the safety of software in an interfaced, network-enabled clinical environment; and Implementing a method for unambiguous patient identification. Implementation and Use challenges include Developing and implementing decision support which improves safety; and Identifying practices to safely manage IT system transitions. Monitoring, Evaluation, and Optimization challenges include Developing real-time methods to enable automated surveillance and monitoring of system performance and safety; Establishing the cultural and legal framework/safe harbor to allow sharing information about hazards and adverse events; and Developing models and methods for consumers/patients to improve Health IT safety. Using a systems-based approach, risks can be minimised and EHR design optimised to drive healthcare safety and quality improvements.

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Improving Transparency OpenNotes (2010) is an “international movement committed to spreading the availability of open visit notes and studying the effects”, with a mission to provide patients, families, and caregivers with open access to their clinicians’ notes. OpenNotes was first conceived in 2010 following the launch of an exploratory study (Walker et al. 2011) examining the impact of sharing clinician notes with patients. Results from this study showed that patients felt more in control of their health as a result of the exposure. Since this initial study, the movement has grown to represent more than 40 million patients across the United States and Canada. A study by Wright et al. (2015) found greater adherence to medication regimes following exposure to primary care physician notes via a patient-facing web portal. The authors conducted a retrospective comparative analysis of 2417 patients aged 18 years or above. They found adherence rates of 79.7% among patients using antihypertensive medications versus 75.3% in a control group who did not have access to their notes; even though there was little difference among patients using antihyperlipidemic medications. The authors concluded that patient exposure to notes might be associated with positive behaviour change, particularly as records become more transparent over time. However, a report by Bell et al. (2020) found that patients perceived errors within their notes to be associated with important safety and quality implications. Among 29,656 patients invited to examine their records for perceived errors, 20% of the cohort reported a perceived mistake. Based on the findings, Bell et al. have concluded that note transparency may be associated with greater patient engagement in safety along with improved accuracy within their records, although further research is necessary to expand the evidence base. This study illustrates the potentially overlooked role of patients and consumers in the work of digital health safety and quality.

Operating a Virtual Clinic Bellringer et al. (2017) described the establishment of specialised in-house training for a virtual fracture clinic for acute fractures and soft tissue injuries at a hospital in the United Kingdom. Patients presenting in the Emergency Department with an ankle fracture were X-rayed and assessed, and then discharged when surgical intervention was not required. Patients were followed up post discharge with a telephone call, replacing the need to attend a clinic in person. Telephone calls were guided by the use of a consistent guideline, which articulated the treatment protocol. Education and training requirements were minimal due to the use of the protocol and the involvement of experienced staff. This study examined the safety and effectiveness of this intervention. In particular, the study found that the model was able to deliver safe care with no evidence of serious complications and delivered significant savings to the Clinical Commissioning Group. Similar studies in Australia have

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replicated these findings, demonstrating patient satisfaction with the quality of care: avoiding unnecessary travel, needing fewer outpatient appointments, experiencing shorter wait times for first orthopaedic contact, and being managed locally by primary care providers (Cross 2019).

Sharing Knowledge Globally In 2018, the Global Digital Health Partnership was established to bring together a network of countries to collaborate on best practices and to share evidence and learnings of digital health implementations. The vision of the partnership is to guide policy on the delivery of digital health services to drive improvements in patient safety and quality of care (GDHP 2020). Similar network-based learning health systems (or learning networks) have been established over the last 13 years (Britto et al. 2018), recognising the value of exchanging knowledge internationally among digital health specialists. Participant countries are organised across five different work streams, identified as key areas where there are shared challenges, covering clinical and consumer engagement, evidence and evaluation, policy environments, interoperability, and cybersecurity, with a number of white papers developed and published in collaboration. Further work will be required to determine the impact of these white papers on local country policies.

Discussion and Conclusions The case studies in this chapter examine different workforce development strategies and programs focused on improving digital health safety and quality, either directly through designing and implementing technology interventions or indirectly through improving the digital health literacy of the broader health workforce. The methods used to achieve these goals include the rapid dissemination of academic articles, online self-guided videos, virtual health rotations, certification programs, leadership and governance training, university partnerships, grand rounds, just-in-time training, guideline formulation, clinician note sharing, and learning networks. There is little consistency in their approach, and the accounts provided by researchers and policy-makers offer only fragments of insight into the role of digital health specialists in their execution. The explosion of online resources to support pandemic-driven use of telehealth has left many clinicians searching for standardised, reliable, and consistent guidelines. This is a prime example of an opportunity for digital health specialists to create and curate, in a centralised location, standardised programs and guidelines to

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support telehealth use at least at a national level. Such initiatives would be effective and efficient and would enable digital health specialists to monitor and evaluate the impact of their work on healthcare safety and quality. Many of the case studies did not include a patient safety framework, such as that described by Sittig et al. (2014), that contemplates the environment in which a digital health system is implemented. The implementation of patient safety frameworks should be considered at the outset and led and managed by digital health specialists working in concert with the broader health workforce. These frameworks can be applied across any healthcare environment—from primary and community care environments to hospital and health service environments. The aim should be to assign responsibility to digital health specialists, to maintain control of the intervention over different environments and hence minimising the risk for any adverse events. There is a need to place a greater emphasis on the complex adaptive nature of the health system as an underpinning tenet, and the overarching principles of patient safety and quality as the necessary outcomes. Further, there is limited application of ongoing monitoring and surveillance to detect and respond to issues that arise, leading to the likelihood of unintended consequences – consequences that the technologies have been put in place to avoid. The specialist digital health workforce has a growing role to play in the design, development, deployment and ongoing monitoring of these technologies and there is an opportunity for health leaders across the system to respond to this need and invest further in the education and training of this important workforce. The majority of the case studies showed training programs targeting the wider health workforce to utilise digital health tools safely. There has not been a concomitant investment in the training of the specialist digital health workforce, nor widespread recognition of the important role of the digital health specialist. There are a number of federally funded programs in the United States, including the National Library of Medicine (NLM 2021) fellowship program for pre- and postdoctoral trainees and those funded by the Agency for Healthcare Research and Quality (AHRQ 2021) regarding the dissemination of best practices around HIT, which support the emerging digital health specialist workforce. Alongside these are small-­scale fellowship programs in the United Kingdom (Faculty of Clinical Informatics 2021) and Australia and New Zealand (Australasian Institute of Digital Health 2021). Overall, greater investment in a confident and capable workforce could assist governments and health services to scale digital health projects and proofs of concept. We need to know more about the impact of digital health specialists’ work on safety and quality outcomes, and also about the impact of their efforts to train the health workforce to utilise digital health tools. Novel approaches are essential to move beyond traditional models of developing the specialist digital health workforce. Multi-actor collaborations at a global level, among governments, healthcare organisations, clinicians, consumers, and resources are critical to implementing strategies and programs to build the digital health

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workforce and digital health specialist workforce effectively. Learning networks (Britto et  al. 2018)—aligning participating healthcare agencies with a common goal, transparency of outcome measures, and collaborative shared resources for cooperative learning and co-production of ideas and knowledge at scale—could support the digital health specialist workforce to address and improve overall patient safety and health outcomes. It is clear that much more research into the specialist digital health workforce is required to understand its actual effectiveness and potential for impact with respect to patient safety and healthcare quality.

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OpenNotes. More than 40 million patients can access their clinicians’ visit notes via secure portals at 200 health systems. 2018. https://www.opennotes.org/news/more-­than-­40-­million-­patients-­ can-­access-­their-­clinicians-­visit-­notes-­via-­secure-­portals-­at-­200-­health-­systems/. Accessed 14 Feb 2021. Reeves JJ, Hollandsworth HM, Torriani FJ, Taplitz R, Abeles S, Tai-Seale M, Millen M, Clay BJ, Longhurst CA. Rapid response to COVID-19: health informatics support for outbreak management in an academic health system. J Am Med Inform Assoc. 2020;27(6):853–9. Ryan A.  To investigate methods to reduce patient harm through national digital health safety governance. Churchill Fellowship Report. 2019. https://www.churchilltrust.com.au/fellows/ detail/4293/Angela+Ryan. Accessed 31 Mar 2021. Schneider EC, Ridgely MS, Meeker D, Hunter LE, Khodyakov D, Rudin RS. Promoting patient safety through effective health information technology risk management. Rand Health Q. 2014;4(3):7. Shortliffe EH. AMIA testimony to HIT policy committee adoption/certification workgroup. 2010. https://www.amia.org/sites/default/files/files_2/Shortliffe-­HIT-­policy-­testimony-­feb2010.pdf. Accessed 14 Feb 2021. Singh H, Ash JS, Sittig DF.  Safety Assurance Factors for Electronic Health Record Resilience (SAFER): study protocol. BMC Med Inform Decis Mak. 2013;13:46. Sittig DF, Ash JS, Singh H. The SAFER guides: empowering organizations to improve the safety and effectiveness of electronic health records. Am J Manag Care. 2014;20(5):418–23. Sittig DF, Salimi M, Aiyagari R, Banas C, Clay B, Gibson KA, Goel A, Hines R, Longhurst CA, Mishra V, Sirajuddin AM, Satterly T, Singh H.  Adherence to recommended electronic health record safety practices across eight health care organizations. J Am Med Inform Assoc. 2018;25(7):913–8. Sittig DF, Wright A, Coiera E, Magrabi F, Ratwani R, Bates DW, Singh H. Current challenges in health information technology–related patient safety. Health Inform J. 2020:181–9. Topol E. The Topol review, preparing the healthcare workforce to deliver the digital future, An independent report on behalf of the Secretary of State for Health and Social Care February. 2019. https://topol.hee.nhs.uk/wp-­content/uploads/HEE-­Topol-­Review-­2019.pdf. Accessed 8 Feb 2021. Wachter RM. Making IT work: harnessing the power of health information technology to improve care in England. Report of the National Advisory Group on health information technology in England. 2016. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/ attachment_data/file/550866/Wachter_Review_Accessible.pdf. Accessed 8 Feb 2021. Walker J, Leveille SG, Ngo L, Vodicka E, Darer JD, Dhanireddy S, Elmore JG, Feldman HJ, Lichtenfeld MJ, Oster N, Ralston JD, Ross SE, Delbanco T. Inviting patients to read their doctors’ notes: patients and doctors look ahead: patient and physician surveys. Ann Intern Med. 2011;155(12):811–9. Warraich HJ.  As a doctor, I use telemedicine. With the coronavirus threat, it could revolutionize healthcare. Los Angeles Times (online). 2020. https://www.latimes.com/opinion/ story/2020-­03-­17/op-­ed-­as-­a-­doctor-­i-­use-­telemedicine-­with-­the-­coronavirus-­threat-­it-­could- ­ revolutionize-­healthcare. Accessed 14 Feb 2021. Webster P. Virtual health care in the era of COVID-19. Lancet. 2020;395(10231):1180–1. Westbrook JI, Reckmann M, Li L, Runciman WB, Burke R, Lo C, Baysari MT, Braithwaite J, Day RO.  Effects of two commercial electronic prescribing systems on prescribing error rates in hospital in-patients: a before and after study. PLoS Med. 2012;9(1):e1001164. WHO.  WHO timeline  – COVID-19. 2020. https://www.who.int/news-­room/detail/27-­04-­2020-­ who-­timeline%2D%2D-­covid-­19. Accessed 14 Feb 2021. Wright E, Darer J, Tang X, Thompson J, Tusing L, Fossa A, Delbanco T, Ngo L, Walker J. Sharing physician notes through an electronic portal is associated with improved medication adherence: quasi-experimental study. J Med Internet Res. 2015;17(10):e226.

Part V

Case Studies

Chapter 14

Working as a CIO in Healthcare Meredith Makeham, Angela Ryan, Richard Taggart, Clair Sullivan, Peter Sprivulis, and Keith McNeil

Abstract  Healthcare Chief Information Officers (CIOs), through their stewardship of technology, have become key players in the healthcare innovation agenda. However, the introduction of technology is much more than just a technical task; it involves new ways of working, networking and organising globally. These changes require the expertise and guidance of professionals with health informatics knowledge, combined with management and leadership skills (particularly in the context of change), as well as clinical experience and an understanding of the demands faced by frontline clinicians in healthcare services and settings. This chapter provides historical context for the development of these roles, their key attributes, challenges and next steps for the healthcare CIO of the future.

M. Makeham (*) Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia e-mail: [email protected] A. Ryan Australasian Institute of Digital Health, Sydney, NSW, Australia R. Taggart Sydney Local Health District, Sydney, NSW, Australia e-mail: [email protected] C. Sullivan Metro North Hospital and Health Service, Brisbane, QLD, Australia e-mail: [email protected] P. Sprivulis Western Australia Department of Health, Perth, WA, Australia e-mail: [email protected] K. McNeil Queensland Health, Brisbane, QLD, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_14

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Keywords Case study · Chief information officer · Chief clinical information officer · Chief digital health officer · Leadership

Introduction Healthcare planners and governments across the globe are involved in efforts to stimulate innovation and reform health systems to enhance the delivery of safe, effective and quality care ‘to patients’. Digital information and communication technologies (ICT) have become essential; today there are almost no parts of healthcare systems anywhere in the world that do not rely on any form of ICT (WHO 2020). Increasingly, technology underpins the continuum—the supply chain, medical research, people management, operations, finance and billing, patient diagnosis, treatment, monitoring, and handover of care. Health services are being technologyenabled to address consumer demands and expectations, provide relevant, appropriate and useful real-­time information to clinical practitioners, and enhance the management and monitoring of healthcare delivery performance. Whether it is the deployment of mobile phones to monitor pregnant mothers in Sub-Saharan Africa (Holst et al. 2020), or the implementation of a comprehensive electronic medicines system in hospitals across New South Wales, Australia (eHealth NSW 2019), the momentum of technology adoption has transformed the industry, and demonstrated improved quality, safety and efficiency of patient care (ACSQHC 2018). Governments and healthcare providers are cognisant of these opportunities but must balance up them up against available resources and possible risks. For every successful ICT implementation, there are countless examples of failure; however, these are often difficult to learn from, tending to be reported less frequently in the literature. ICT also presents new risks to health organisations such as cybersecurity and technology-related adverse events (Sujan et al. 2020). International collaborations such as the Global Digital Health Partnership are emerging to guide reform initiatives in the best use of evidence-based technologies (Global Digital Health Partnership 2020). Industry organisations have created maturity models (HIMSS 2020), best practice guidance, communities of practice (Australian Institute of Digital Health 2020) and training programs to help health organisations navigate the complexities involved in healthcare ICT.

Emergence of the CIO Chief Information Officers (CIOs) have become critical players in the healthcare innovation agenda, through stewardship of the technology. The introduction of ICT is much more than just a technical task; it involves new ways of working, networking and organising globally. These changes require the expertise and guidance of professionals with health informatics knowledge, combined with management and

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leadership skills, clinical experience and an understanding of the demands faced by frontline clinicians in healthcare services and settings. Specialisations within healthcare ICT leadership and CIO roles have emerged in response to the requirements for senior roles within the Health Informatics, Digital, Data, Information and kNowledge (HIDDIN) workforce. By way of background, although healthcare organisations had been establishing information management and technology functions since the 1970s, it was not until the mid-1990s that the CIO’s role was commonly found on the organisation charts of leading healthcare providers (Glasser 1993). In the early years, the CIO’s role was very technology-centric, responsible for establishing telephone and network infrastructure, email systems and necessary computing hardware (Sullivan and Miliard 2018). By the 2000s, health providers worldwide were beginning to identify the benefits that health information technology could make to the safety, efficiency and quality of care. In Australia, both public and private providers were making large investments into electronic medical records (EMRs) and several States and Territories committed significant funding to roll out programs. By the early 2010s, EMRs and other clinical information systems were becoming ubiquitous in Australia. CIOs became key to delivering these transformational technologies within government health departments and health provider organisations. The role moved from ‘back of house’ IT support, to become an essential strategic role within the business. Increasingly CIOs were required to have a deep understanding of healthcare and the delivery and management of technology operations. As more systems were implemented and new features added, the IT department needed to grow to keep these systems running, updated and secure, and to provide around-the-clock support to clinical and administrative staff users. Related healthcare CIO roles became more commonplace in healthcare, as demand increased for organisational leaders who could merge the traditional skills of a health CIO with deep clinical expertise and experience. Some original CIOs had this skill set, but it was not denoted in their position title. As the importance of dual qualifications increased, roles emerged with titles such as Chief Clinical Information Officer (CCIO), Chief Medical Information Officer (CMIO), Chief Nursing Information Officer (CNIO), and more recently, Chief Digital Health Officer (CDHO). These professionals were expected to have qualifications or in-­ depth knowledge and experience relevant to informatics, combined with either broad clinical expertise or qualifications in a specified clinical discipline. In many healthcare organisations the traditional technology focussed CIO role continues today, combined with a range of healthcare C‘X’IO. In larger healthcare organisations, their partnership plays a vital role in delivering digital health services to frontline clinicians and healthcare consumers, as well as balancing the technical and non-technical aspects of change. For example, in Australia the National Digital Health Strategy provides a plan and a vision of safer and better-connected healthcare, supported by digital health technology (Australian Digital Health Agency 2019); the application of this strategy in the late 2010s required CIOs to coordinate and lead the approach in many organisations (Eden et  al. 2020). These organisations also became increasingly

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interested in emerging technologies such as clinical mobility, voice assistants and artificial intelligence, to name but a few, and needed healthcare CIOs to stay current with the latest trends and opportunities created by these tools. In the 2020s, the healthcare CIO specialist must often balance maintaining the operation of complex clinical environments and providing line management to large departments of IT professionals. They must manage the expectations of healthcare professionals whilst controlling costs and delivering business innovation (Kark 2017). Their frontline healthcare delivery experiences and qualifications provide an important skillset to deliver these functions in a way that best incorporates the needs and expectations of patients and frontline clinical staff. It also helps them lead codesign processes with technologists, clinicians and healthcare consumers to develop and implement new digital health technologies and services for their organisations. An Australian case study is that of Clair Sullivan. She started her career intending to become an endocrinologist, completing her training and a research doctorate in the UK.  After 2 years as a consultant endocrinologist in a metropolitan academic tertiary hospital in Australia, she became the Director of Physician Training. This provided initial experience in system management. Following this role, she was promoted to Deputy Chair of Medicine. When the health system embarked on a digital transformation with an initial implementation of an integrated electronic medical record, she became the Clinical Lead for the AUD70 million project. She then moved to a State-wide role as the Medical Director of the EMR governance team. Her next role was as Chief Digital Health Officer for Australia’s largest hospital and healthcare service. Throughout this time, she maintained a portfolio of academic activity, continuing to publish, research, and supervise PhD students. She also undertook governance roles, serving on several national advisory boards. She has remained a practising clinician throughout this career trajectory.

Key Functions of CIOs CIO roles are unique leadership positions in healthcare. They have an extensive remit to transform the health systems in which they work. They must combine their knowledge of the health system with technology expertise and business acumen. To achieve success, people in these roles should have organisational knowledge and skills across strategy and vision, cybersecurity and privacy, and many domains of management: people; services; relationships; vendors; finances; technology; projects and portfolios. The healthcare CIO needs to work in alignment with their Chief Executive Officer and others in the organisation’s senior leadership team, on the business goals of the organisation and the role technology will play in achieving them. Although protecting patient confidentiality and data security is a shared organisational responsibility, the CIO is often the most senior executive accountable for protecting information systems—they must be abreast of the threats facing the health industry and be proactive in averting them.

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The ability of the CIO to lead a team and relate to others is one of the most important of all the domains. They must inspire their department, partners and key stakeholders to understand the opportunity that technology offers their business and overcome the inevitable challenges that arise. They must build a high-performing team through recruitment, talent management and professional development, manage the performance of their team, and deal with human resource issues as they arise. A key to their success is their knowledge of and experience with change management principles, since the effective implementation of any large-scale digital project is at its heart, a change management process involving all the people affected by the change. An essential part of the role is maintaining relationships both internal and external to the organisation, across both technology and healthcare stakeholders. From the Emergency Department to the fracture clinic up to the finance office, the CIO role must be responsive regarding the technology tools others in the organisation need to achieve their goals. The CIO must lead their teams to plan, build and deploy a complex set of ICT services, such as monitoring, maintaining and supporting production systems and managing incidents as they arise. They must also outsource some service provision, by interacting with a range of providers from hardware and software vendors to consultancies and resource agencies. They must navigate complicated procurement and contractual issues, and ensure that the deliverables provide value for money and achieve their stated outcomes. Information technology can add significant value to an organisation, but it can come at a substantial cost, so the CIO is likely to be responsible for a multi-million-dollar budget, and balance how to invest this in maintaining operations, upgrading systems and delivering innovative solutions. Although not required to be deep technical experts, the CIO must have a thorough understanding of technology, and how it should be governed and managed. They must continue to develop, maintain and manage the infrastructure and applications upon which their organisation depends. They must stay current with emerging technologies, understand their organisation’s needs and have a strategy that enables interoperability between systems. These roles will often be relied on to oversee cybersecurity systems, policies and procedures that are based on working knowledge of both the technical and behavioural aspects of cybersecurity. Especially in the wake of COVID-19, many healthcare organisations have a comprehensive digital transformation program underway, from EMRs to telehealth and everything in between. Effective program and project management are essential to ensure that these initiatives are delivered within the time, quality and cost parameters acceptable to their organisation. The CIO must therefore balance the portfolio of initiatives underway within the organisation, based on available resources and ability to deliver.

Becoming a CIO Many healthcare CIOs start from a health professional background, and then either through experience or by seeking post-graduate qualifications in IT, they develop their interest in health informatics and subsequently pursue careers in this

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specialised field. Many choose to undertake professional or industry certifications; some more senior executives undertake Master’s degrees, for example, in information management or business administration. CIOs and senior health technology executives have generally followed a more traditional technology career path. These professionals often hold a computer science or informatics-based undergraduate degree before entering the health industry. Then, either through further education or experience, they develop their knowledge of the healthcare industry. At the time of writing, the authors know of only one specific formal pathway to becoming a CIO—through the College of Healthcare Information Management Executives (CHIME 2020), a US-based non-profit organisation that offers a certification program open to international applicants. In addition or alternatively, many CIOs have obtained Fellowship with a professional or industry association, for example in health services management, health information management or health informatics.

Challenges and Directions for CIO Roles Healthcare CIOs are interpreters of information technology for their organisations and clinical colleagues. A principal barrier they face is the lack of digital health literacy in the management ranks of most health organisations and indeed, the clinical community. Being able to successfully answer an email on an iPhone or use common applications in a desktop environment may give people a sense that they understand the complexities of enabling digital health capabilities far more than they do. This makes the role of a CIO more challenging, as they balance the principles of co-design, engagement and feasibility. Demonstration rather than description is by far the best means of breaking through this barrier. An opportunistic approach is often required, such as taking opportunities to act as an interpreter while simultaneously increasing the digital literacy of management tiers. Active promotion of digital literacy in the clinical and management workforce is required for the successful engagement and implementation of new digital tools and services in a healthcare organisation. The investment required for workforce digital capacity and capability uplift varies between settings and is under-resourced in some. Progressing the role of CIOs in healthcare involves them in exercising leadership beyond their immediate roles, and working across the health sector broadly. They need to contribute to efforts that can support digital health specialist capacity building. They need to participate actively in the formulation and advancement of national digital health strategies and policies.

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References ACSQHC. Impact of digital health on the safety and quality of healthcare. Australian Commission on Safety and Quality in Health Care, 2018. https://www.safetyandquality.gov.au/sites/default/ files/migrated/Report-­The-­Impact-­of-­Digital-­Health-­on-­Safety-­and-­Quality-­of-­Healthcar.... pdf. Accessed 15 May 2021. Australian Digital Health Agency. Safe, seamless and secure: evolving health and care to meet the needs of modern Australia  – Australia’s National Digital Health Strategy. 2019. https:// www.digitalhealth.gov.au/about-­us/national-­digital-­health-­strategy-­and-­framework-­for-­action. Accessed 15 May 2021. Australian Institute of Digital Health. Communities of practice. 2020. https://digitalhealth.org.au/ communities-­of-­practice/. Accessed 15 May 2021. College of Healthcare Information Management Executives. About. 2020. https://chimecentral. org/. Accessed 15 May 2021. Eden R, Burton-Jones A, Ballantine C, Staib A, Sullivan C. The transformation of Australia’s first large digital hospital: a teaching case. In: Proceedings of the 41st International Conference on Information Systems (ICIS 2020). Association for Information Systems; 2020. eHealth NSW.  Milestone for electronic medication management roll-out. NSW Health. 2019. https://www.ehealth.nsw.gov.au/features/milestone-­for-­electronic-­medication-­management-­ roll-­out. Accessed 15 May 2021. Glasser JP. The role of the Chief Information Officer in the health care organization in the 1990s. Top Health Inf Manag. 1993;13(3):62–8. Global Digital Health Partnership. About the global digital health partnership: our vision. 2020. https://www.gdhp.org/our-­vision. Accessed 15 May 2021. HIMSS.  Adoption model for analytics maturity. 2020. https://www.himssanalytics.org/amam. Accessed 15 May 2021. Holst C, Sukums F, Radovanovic D, Ngowi B, Noll J, Winkler AS. Sub-Saharan Africa-the new breeding ground for global digital health. Lancet Digit Health. 2020;2(4):e160–e162. Kark K. The CIO balancing act: operations and business innovation. Harv Bus Rev. 2017. https:// hbr.org/webinar/2017/09/the-­cio-­balancing-­act-­operations-­and-­business-­innovation. Accessed 15 May 2021. Sujan M, Scott P, Cresswell K. Digital health and patient safety: technology is not a magic wand. Health Inform J. 2020;2295–2299. Sullivan T, Miliard M. Meet the modern healthcare CIO: a business leader that is casting off their traditional IT role, in Healthcare IT News. 2018. World Health Organization. Global strategy on digital health 2020-2025. 2020. https://www. who.int/docs/default-­s ource/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d. pdf?sfvrsn=f112ede5_42. Accessed 15 May 2021.

Chapter 15

Working as a Health Cybersecurity Specialist Patricia A. H. Williams, Simon Cowley, Christopher Bolan, Ken Fowle, and Richard Staynings

Abstract  The need for cybersecurity expertise in the health workforce is rapidly growing as security compromises and attacks on hospitals and government departments accelerate at an alarming rate. Healthcare presents a rich and unique environment to protect, requiring an informed perspective on how to protect it. The challenges of applying cybersecurity protections in healthcare, the pace of technical change, the complexity of regulation and the potential for patient safety impact means healthcare needs specialised skills in cybersecurity. The characterisation of this specialty, together with five case studies, describes its vital importance and the variety of careers in this area. Keywords  Case study · Cybersecurity · Data breach · Incident response · Risk management

P. A. H. Williams (*) Flinders University, Adelaide, SA, Australia e-mail: [email protected] S. Cowley Department of Health, Melbourne, VIC, Australia e-mail: [email protected] C. Bolan St John of God Healthcare, Perth, WA, Australia e-mail: [email protected] K. Fowle Child and Adolescent Health Service, Perth, WA, Australia e-mail: [email protected] R. Staynings Cylera, New York, NY, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_15

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Introduction Australia faces a shortage of job-ready cybersecurity professionals, and the demand will increase to 16,600 additional professionals by 2026 (AustCyber 2020). This is a microcosm of the situation around the world where a 2020 study found that organisations could use over 3 million additional workers, that is, nearly double the existing workforce ((ISC)2, 2020b). This shortage is a significant challenge for all industries; however, the healthcare sector has specific reasons to take this challenge seriously. Healthcare has become a high-profile target because of the breadth and amount of personal and sensitive information recorded. Healthcare has an ethical and regulatory obligation to society to keep patients’ information confidential and maintain their privacy because of the damage to reputation, identity and safety that can ensue from medical data breaches. Real-time access to patient histories, monitoring equipment data and medical device data is important, for example in emergency care; but threats to real-time access have increased, with disruptive ransomware attacks impacting critical healthcare delivery (Ronquillo et al. 2018; Chernyshev et al. 2018). The issues in cybersecurity will only increase for healthcare and are driven by the integration of systems, our necessity to share patient information, and the move to new models of patient-centred care (Coventry and Branley 2018; Williams et al. 2020). For instance, the increased use of telehealth and adoption of virtual care, accelerated by the COVID-19 pandemic, means the inherent vulnerability of connectivity becomes a broader problem for healthcare organisations (Langer 2020). Further, the shift to patient empowerment and demand for greater access to information (CSIRO 2018) are driving change in the expectations of digital technology users and the experiences they desire. Coupled with wireless medical devices, the Internet-of-Things (IoT), mobile data sharing and health apps, the growing complexity of the healthcare environment demands increased protection as the cyber-­ physical boundaries are integrated into our information systems (Williams and Woodward 2015; Webb and Dayal 2017; Altawy and Youssef 2016). It is widely acknowledged that security and privacy measures have not been able to keep up with such technology. The cybersecurity specialist role is at the centre of healthcare’s information infrastructure, to protect the information, systems and the infrastructure itself. The potential threats, whilst not unique to healthcare, include social engineering, insider misuse, malware, unsecured mobile devices, connected devices, unrestricted access, inadequate disposal of retired hardware and systems, and unintentional mistakes. This chapter does not detail the generic cybersecurity skills, nor the litany of cybersecurity breaches in healthcare. Instead, it highlights what makes cybersecurity in healthcare an advanced specialisation. Then it provides five case studies of how cybersecurity specialists characterise their work and the specific skills they need to have for this work.

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The Challenge of Being a Healthcare Cybersecurity Specialist Cybersecurity in healthcare is complicated. The reasons are fourfold. Firstly, security is not the core business of healthcare. We often rely on healthcare workers to adapt to the security procedures and the information systems developed to manage information securely; however, this can be problematic in the healthcare environment. Healthcare workers’ primary task is the delivery of quality healthcare to patients, and therefore we cannot arbitrarily change their clinical processes to adapt to the demands of information technology (IT) and cybersecurity. Better integration of cybersecurity is required, so that it is woven seamlessly into clinical workflow (Garg et al. 2018; Williams 2008). Education of non-security personnel in cybersecurity risks is also needed. Secondly, the pace of change, development and adoption of new technologies outpaces our ability to secure these technologies from an end-to-end systems perspective. For instance, the rise in the availability of IoT devices for healthcare, a technology that already has significant security concerns, will require knowledge of application programming interface (API) security, IoT security frameworks, integration risks and safe deployment management (Williams and McCauley 2016). The detection of physical signals and their translation into vast amounts of data that can be recorded and analysed requires oversight and understanding of the nature of these devices, in particular where this data is life critical (Mohapatro and Snigdh 2020). Also, health and wellness apps connected to health systems, proliferating in, and useful to, patient care are not well regulated. Thirdly, the lack of cybersecurity expertise is an issue, and cybersecurity skills are not the same as IT skills. Whilst IT staff play an important role in developing, implementing, and managing the technical aspects of patient health information systems and infrastructure, they generally do not have specialised skills in cybersecurity. In an environment with multiple networks, diverse health software systems, legacy systems, medical devices, together with the associated risks that integration of these systems poses, a comprehensive understanding of their operation is vitally important. Further, the threats to sensitive patient information and device data, and the availability, integrity and protection of these data, must be well understood in the context of their use in healthcare; the consequences for patient safety can be significant and life-threatening (Ross 2017). Lastly, a unique brand of complexity arises from the inter-related demands of legislation, regulation, data protection standards, breach notification rules, and expanded approaches to using data in healthcare; the increasing connectivity and digitisation of health technologies continue to create new vulnerabilities (Therapeutic Goods Administration 2019). A cybersecurity specialist is not just another IT person. There are numerous roles within the cybersecurity discipline, including cybersecurity analyst, operations, regulatory compliance, testing and architecture functions, each with its own

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specialised skill set (Australian Signals Directorate 2020). Frameworks such as the Workforce Framework for Cybersecurity (NICE Framework) (Petersen et al. 2020) detail the skills a cybersecurity specialist requires. In addition to the broad, fundamental, and necessary cybersecurity skills of information security governance, threat and risk assessment, operational security management, incident management, investigation and digital forensics, health cybersecurity specialists need the following skills. They need to understand the sensitive nature of health data and the associated privacy concerns, including how to meet regulatory requirements. Also, they need an in-depth knowledge of the complexity of the attack surface (the totality of ways that an IT system can be attacked) and how this interacts with the healthcare environment. They must understand how cybersecurity impacts patient safety and how to protect systems where attacks could result directly in adverse events and physical harm—for example, medical devices such as ventilators, incubators and pacemakers. They must have the knowledge to take responsibility for the safety and security of the complex healthcare ecosystem, not just the individual information systems, separate medical devices or sections of infrastructure. They need advanced knowledge of the risks related to artificial intelligence (AI) and machine learning algorithms. The healthcare cybersecurity specialist must not only understand but also communicate their contribution to the overall operation of healthcare facilities and health system performance; because awareness about cybersecurity amongst healthcare workers is low, the cybersecurity professional has a significant role to play to advocate for a cybersecurity culture and best practice ((ISC)2, 2020a).

Simon Cowley, Department of Health, Australia As Principal Cybersecurity Officer with the State of Victoria’s Department of Health, Digital Health Branch, I work with public health services on assurance activities, initiatives to uplift cybersecurity maturity and cyber incident response. The position of Principal Cybersecurity Officer involves engagement with health services across the Victorian public health sector. I assist with assurance activities, such as assessments to monitor the progress of health services implementing controls based on the NIST Cybersecurity Framework. These assessments often lead to initiatives such as implementing tools and technologies to assist health services to improve cybersecurity capabilities; I am involved in project design and project management. I also participate in cyber incident response and collaborate closely with an Incident Response Team and trusted partners in this work. Cybersecurity is a recent career change. I have a Bachelor of Science in Biomedical Sciences and electronics engineering qualifications from my previous career as a biomedical engineer for over 15 years in the public health service, with my last position at The Royal Melbourne Hospital. Prior to this, I worked in the industrial control sector, installing, and maintaining control systems used in manufacturing, petrochemical and food industries. My career progression from a biomedical engineer into healthcare cybersecurity had a natural flow. As a biomedical

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engineer working in a large health service, I was exposed on a daily basis to the operational challenges of network-connected medical devices. I worked collaboratively with key stakeholders (medical device manufacturers, IT professionals, facility managers, project managers) to ensure medical IT systems were safe and resilient. To equip me to confront these challenges, I drew on my experience from the industrial control sector and pursued further training in IT networking to understand the concepts and ‘language’ of IT. I observed that as medical device technology advanced, it became more digitised and interconnected. This increased system complexity also increased the cybersecurity risk through hardware and software vulnerabilities and increased exposure to network and internet-based threats. This risk to the safety of medical devices was emerging and unlike other risks to medical devices, it had a dynamic threat landscape. At The Royal Melbourne Hospital, I worked collaboratively with IT on secure medical IT network architectures, and practical cybersecurity controls for medical devices, adapted from best practices used in the industrial control sector. This work led to several biomedical engineering conference presentations on medical IT and cybersecurity, growing a further interest to move towards a career in healthcare cybersecurity. I concluded that it was necessary to pursue industry recognised training in cybersecurity to assist with this new career path, and therefore completed my first cybersecurity certification whilst working as a biomedical engineer. I hold an ISACA CSX Certification in Cybersecurity Fundamentals, and I am currently pursuing ethical hacking and cyber incident management training. It is essential to have a fundamental knowledge of patient care workflow, health technologies and health ICT systems used in health services. This background assists with the understanding of the practicalities of applying cybersecurity controls and technologies within the health sector. The completion of industry-recognised cybersecurity training/certifications is also beneficial. This training assists in building specific skill sets, such as penetration testing, cyber incident response and digital forensics. Inspiration comes from the knowledge that my work is instrumental in improving the safety and resilience of health technology and ICT systems within the public health sector. Cybersecurity is a challenging profession, primarily due to an ever-­ changing threat landscape. It requires regular analysis of threat intelligence and sharing actionable information with health services. This activity is rewarding, knowing that this is one of many preventative activities protecting hospitals from cyber incidents. The profession also provides an opportunity to apply my knowledge and experience from other fields throughout my career to real-world challenges, such as medical device security. Globally, there is a shortage of cybersecurity professionals across many industries, including healthcare. The healthcare sector is also particularly exposed to cybersecurity risk, due to low maturity and investment in security, along with vast repositories of protected health information, a commodity highly sought-after by threat actors. There have also been numerous cyber incidents globally within the healthcare sector, resulting in the disruption of clinical services, exposure of protected health information and extortion of health services/ individuals. I foresee the demand for healthcare cybersecurity professionals will continue to grow well into the future, driven by the increased digitisation of

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healthcare technologies and health ICT systems. The healthcare sector is well placed to build these professions from within, augmenting the experience of existing health professionals, similar to my experience.

Christopher Bolan, St John of God Healthcare, Australia I started my IT journey with a Bachelor of Science in Computer Science followed by Honours in Software Engineering, and this was eventually followed by a PhD in cybersecurity. I have also completed several industry certifications (e.g. CISA) and training on specific cyber technologies. I started my IT career as a programmer within the WA Government which grew into a project lead role. From there, I became a University Lecturer in both Australia and the United Kingdom whilst simultaneously keeping my hand in the industry as an IT Security consultant. When the time came to leave academia, I took up the position as the National IT Security, Risk & Audit manager for the prominent ANZ hardware retailer Bunnings. After several years, my career moved back to service provision, undertaking a role with Kinetic IT to establish their Security Operation Centre and lead the development of their Cybersecurity services. In 2017, I accepted the challenge to build an internal cyber capability for St John of God Healthcare, in Perth, Western Australia where I am now Group Manager, Digital Security. For someone like myself who has moved between industry verticals, the continuously evolving challenges of cybersecurity generally are coupled with the need to gain understanding rapidly of a large amount of healthcare-specific knowledge. Healthcare does offer some interesting challenges, such as in infection control, medical technologies and legacy systems, that require you to adapt your thinking. Healthcare is a people-focused industry that looks after those at their most vulnerable, and being able to use my skills to support the people in St John of God Healthcare, is a way to make a significant impact on all those within our care. My current role reports both to the senior executive and the Board and focuses on uplifting and ensuring security across the organisation. The role of cybersecurity specialists is undoubtedly becoming more critical within healthcare as each new technology and system increases the risk associated with a significant breach or attack. There are already suggestions that cyber incidents have had direct impacts on patient outcomes, and as these become more publicised the focus on cybersecurity’s role will be more prominent. I believe this will be driven in part by a push for more significant cybersecurity regulation and Board accountability.

Ken Fowle, Child and Adolescent Health Service, Australia I am the Director of Procurement and Contract Management, Child and Adolescent Health Service, Perth Western Australia. My education and training include a Bachelor of Business (Information Processing) from the Western Australian College

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of Advanced Education; a Doctor of Philosophy from the University of Nottingham, UK; and various short industry training programs. My previous work experience includes project and contract manager, investigator, academic and computer professional, and I have managed a range of multifaceted teams and led significantly sized and complex projects. I have undertaken complex procurement, contract and ICT project management activities on behalf of the Child and Adolescent Health Service, the WA Department of Health, the Department of Mines and Petroleum, and Edith Cowan University. The public service does not cater to specialists. There are Network people, Programmers, Project Leaders, and other ICT professionals who have good general knowledge in their area but no specialist knowledge of cybersecurity. Progression and maintaining permanency are difficult as the public service has restricted staffing numbers. Therefore, it is easier and beneficial for ICT departments to outsource this specialist area as these positions command higher salaries (something the public service cannot afford). My challenge was that once you remove yourself from ICT, the ICT community does not necessarily recognise your ICT skills and knowledge, they see you as the client and not as a resource who has been on “both sides of the fence”. I moved from ICT to Director of Procurement and Contract Manager and to undertake large-scale ICT work in my current role. The ICT knowledge was very beneficial in working with ICT people in developing specifications, evaluating ICT responses and managing the ICT contracts. When I was working in cybersecurity, what inspired me was looking at the issues (investigating), looking for a solution (researching) and seeing the outcome (realising the solution). The skills and knowledge that I think are needed, and are unique, for a health cybersecurity specialist are fivefold. Excellent communication is one—you need to explain the technical in simplified terms to enable your clients to understand the issue/concerns/solution. Relationship building is another—you want to continue the journey with your clients and if needed an external provider, with trust/honesty and knowing that your clients have confidence that you can or know someone who can. Hands-on technical skill, not just theoretical knowledge, is important. You need to be inquisitive—ask why did that happen and how did that happen? Doing research to keep up to date as to what is happening in the security area and providing an effective, comprehensive solution is the final thing I would emphasise. Currently establishing stable and fast networks and keeping all hardware up to date seems to be a priority as not all health services are operating on the same technology; however, as more information is transferred from hard copy to the digital platforms, there will be a greater emphasis on data security.

Richard Staynings, Cylera, USA My job is Chief Security Strategist at Cylera—a leading innovator in biomedical and healthcare Internet of Things cybersecurity—in New York, USA. Like many people today working in cybersecurity, I entered the field in a rather indirect way.

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My undergraduate degree was in Government and Public Policy, followed by post-­ graduate diplomas in Business and Public Administration. Still, a career in the Civil Service was not one that I aspired to. Instead, I began my working career in IT in the City of London, gathering business and systems requirements for high-volume, transaction processing trading systems for a merchant bank. Information technology fascinated me and had done so for many years. I was the very first student on my liberal arts degree to type out his dissertation on his computer, and the bug stuck with me. How could IT not be the future of business and society I rightly assumed? Leaving London some years later, I headed down-under to work on the Western Australia Gas Pipeline. Here, I was exposed to SCADA systems to control LPG flow rates, temperatures, and pressures—and more than a few snakes in the Western Desert. Trying to secure these simple systems proved to be a significant challenge. When my part of the project was completed, I headed to Hong Kong and found work deploying networks and IT systems, while training customers on proper computer usage. Following this, I headed to the USA and joined a Big-Six accounting firm as a Consultant. There I first worked in IT infrastructure management and systems development, where I led a team that developed one of the first identity and access management (IAM) systems. Assigned to various clients, over the years, I moved between information technology and Information Security projects, later known as “Cybersecurity”. I ended up leading security for the US Rocky Mountain region. Along the way, I gained a Master’s in Cybersecurity Management and Policy and multiple certificates in Cybersecurity Risk Management and related fields. Growing up in the United Kingdom, my father worked in the British National Health Service, so I was exposed to healthcare terminology and healthcare practices from an early age. My familiarity with the industry has led to working as a consultant on many security assessments and healthcare customer audits. I quickly became an expert in healthcare security risk and compliance, gaining a solid understanding of the nuances of healthcare security and privacy, and consequently leading a national consulting practice in healthcare security on behalf of a large company in the space. This consultancy led to a position as the global cybersecurity leader for healthcare life sciences, for a major IT vendor, as well as several corporate executive roles leading security and IT, and ultimately to my current role at Cylera. Cybersecurity was still an emerging discipline when I entered the field, so like many of my peers, we had to learn as we went along. Security frameworks like BS7799 later became ISO 27001, and the NIST CSF evolved, and the various healthcare compliance requirements changed frequently. Working with international life sciences organisations and supporting an extensive global list of healthcare delivery organisations exposed me to numerous national and supranational security and privacy requirements—GDPR, Caldicott, HIPAA, Joint Commission, APA and PDPA are just some of these. However, the major challenge in healthcare is not about compliance; the challenge is about patient safety and providing holistic security. Healthcare security is so much more than a compliance checklist today. If you are secure, you are usually compliant, but not the other way around. This is a lesson that some health systems and governments have yet to learn, unfortunately.

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My primary role today is advising CISOs and other security leaders on what they need to consider as threats and risks change. I also spend a lot of time educating and briefing their managers on cybersecurity risk concerns and remediation strategies. I am no longer focused on day-to-day operational security concerns but instead focus on providing the people in the driver’s seat with a lot of the information they need to be successful. I also share my research with peers, presenting at healthcare and security conferences. I teach master’s degree-level courses in cybersecurity and healthcare IT to the next generation of cybersecurity leaders. Unlike an attack against a bank, where funds are insured against theft, in healthcare, you cannot provide insurance against increased patient morbidity and mortality. There are real implications when critical life-sustaining systems are held to ransom while needed to diagnose, treat, monitor, and manage the sick, the elderly and infirm. Perhaps that is why I feel drawn to work in this space and to bring the weight of my knowledge and experience to bear. Healthcare is an area in which I can make a significant difference, and not just for myself but for the greater good of society. There are many ways to enter the profession of cybersecurity, laterally from various IT areas, from risk management, from engineering and with a background in IT audit and compliance. Also, there are academic, and vocational certification programs focused on the multiple disciplines in cybersecurity, at undergraduate and post-graduate levels, that equip students with the necessary skills to be successful. However, most important of all is experience and aptitude. As a cybersecurity professional, you need to continually think outside of the box and deal with the challenges of being knocked off your feet yet be able to get back up straight away. With demand for cybersecurity professionals far exceeding supply, those willing and capable of working in this space will never be unemployed for long. Tens of thousands are entering the discipline each year globally, but it is not enough. We need more, many times more, people to defend against rising attacks. It is a broadening profession with almost unlimited opportunities for specialisation and advancement. In healthcare it is a vital and necessary role, to protect patients and the hospitals and clinics we all need throughout our lives. A career in healthcare cybersecurity requires more than just an aptitude with different technologies. It requires an understanding and appreciation of the business of providing care, and of the risks that that healthcare provider organisations are facing.

Trish Williams, Flinders University, Australia I am the Cisco Chair and Professor of Digital Health Systems at Flinders University in Adelaide, South Australia. My education includes a Bachelor of Science (with Honours) in Mathematics and Computing, a Master of Science in Computer Science and a PhD in Medical Information Security. I am a Certified Health Informatician Australasia; I hold a Graduate Certificate in Tertiary Teaching; and I was a certified Unix Administrator. My entire working life has been in computing and security in

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healthcare. Coming from a medical family and growing up in the UK, I started as an Analyst/Programmer in the UK at Update Software, then moved to Australia to join Amfac/Medrecord to write clinical software and worked as the Customer Service Manager and Systems Consultant. After 15 years in the industry, I decided that passing on my knowledge was essential, joining academia in 2000. Commencing as Lecturer, I was promoted over the intervening years, becoming Associate Professor in 2013 and Associate Dean in 2015, at Edith Cowan University in Perth. In 2016, I took up the position of Cisco Chair and Professor of Digital Health Systems, Director of Flinders Digital Health Research Centre (Co-Director until 2021) and Director of Cisco-Flinders Digital Health Design Lab at Flinders University in Adelaide. I also hold the position of College of Science and Engineering, Research Section Head for Data and Information Science. Most obviously, I faced the challenge of being a female in male-dominated professions, and this applied equally to computer science in the 1980s and 1990s, as it did to cybersecurity in the 2000s and still does today. For me, this was not necessarily a negative but highlighted the reality that you need to focus without losing your feminine identity, and that you do not need to display male behaviour to succeed in cybersecurity. I had the opportunity to move from highly technical roles to management and leadership positions because of my ability to communicate effectively and understanding the broader perspective of the challenges healthcare faces. Despite the technical aspects of cybersecurity, there is an equally crucial human side to the profession. I believe that my communication abilities as well as my technical skills, allow me to bring different and complementary perspectives to cybersecurity. My current role includes many diverse activities related to cybersecurity in healthcare, and leadership within the university sector. My research activities include the application of cybersecurity in a practical manner and integrating this into healthcare workflow, and the impact of cybersecurity on patient safety. This work is about improving healthcare through digital infrastructure, including how to provide better experiences for patients and clinicians. I am heavily involved in developing international standards in healthcare cybersecurity and patient safety through HL7 and ISO. I am excited by the ability to make a difference in a sector that so needs it! Healthcare’s focus is on patient care, and as a profession, cybersecurity needs to help by understanding this working environment and adapting cybersecurity practice to work within this environment. The challenges of doing this with skill and compassion inspire me. In addition to superior cybersecurity skills, a cybersecurity specialist in healthcare needs to have a pragmatic perspective on how to protect the healthcare information environment and how to support the organisation, clinicians, administration and patients. Possessing a good understanding of, and empathy with, how the healthcare operates, and be responsive to the needs of this setting and the people working within it, is critical. The skills required will always include technical cybersecurity and risk management skills. However, as digital health disrupts healthcare and new technologies emerge, this will drive changes in how healthcare is provided. Virtual care is one such example, where care supported by technology and information sharing is taken out of the hospital and primary care practices, and often delivered in the home or

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perhaps an aged care facility. The increased use of healthcare technologies, outside the confines of an organisation, such as a hospital, results in a broader and less controlled digital footprint that needs protection. The use of the Internet of Medical Things, remote monitoring, health apps and other data sources will require new perspectives on how to protect data, networks, and communications, end-to-end, outside of the traditional controllable perimeter. This means the cybersecurity specialist will need to be knowledgeable and adaptable, and take a systems perspective to protections encompassing the technology, people and processes involved.

Conclusion The dynamic nature of healthcare makes it vital to stay up to date on new and emerging technologies that will transform healthcare. Artificial intelligence and machine learning will become commonplace, and there will always be new health software, apps, and connected devices. Increasingly, patients will use the Internet of Medical Things and personalised monitoring devices, and bring these into healthcare delivery environments. These innovations will certainly stretch, if not eliminate, traditional network boundaries and organisational cyber-perimeters. Future healthcare delivery scenarios that incorporate more virtual care will require advanced knowledge of API integration, interconnected cross-platform systems, secure data transfer and storage, integration into electronic health records, and secure communications. There is not yet a specific job title of healthcare cybersecurity specialist, nor explicit training for such a specialisation. Yet many cybersecurity specialists, such as those in these case studies, demonstrate the advanced knowledge and skills required to pursue this career. Healthcare is different from other sectors. It is critically reliant on national and international information infrastructure, and poor cybersecurity can lead to harm to human lives. The quantity, criticality and importance of the data held by healthcare providers and organisations, and the impact that breaches can have on individuals, mean that cybersecurity is now an essential component of management and governance across the whole of healthcare. Balancing digital health operations with appropriate protections is a complex task that requires specific skills and knowledge—it needs healthcare cybersecurity specialists.

References (ISC)2. Not all life savers wear white coats. 2020a. https://www.isc2.org/-­/media/ISC2/ Landing-­Pages/2020/HCISPP-­B2C-­White-­Paper/MAR-­HCISPP-­Not-­All-­Life-­Savers-­B2C-­ Whitepaper.ashx?la=en&hash=4487C2B81A8E6228E5DEC05DB34A7AD4EBBA3B90. Accessed 15 May 2021. (ISC)2. Cybersecurity experts stand up to a pandemic: (ISC)2 Cybersecurity workforce study. 2020b. https://www.isc2.org/Research/Workforce-­Study# Accessed 15 May 2021.

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Altawy R, Youssef AM.  Security tradeoffs in cyber physical systems: a case study survey on implantable medical devices. IEEE Access. 2016;4:959–79. AustCyber. SCP – Chapter 3 – Accelerating and sustaining growth. In: Australia’s Cyber Security: Sector Competitiveness Plan [Internet]. Canberra: Australian Cyber Security Growth Network; 2020. https://www.austcyber.com/resources/sector-­competitiveness-­plan/chapter3. Accessed 15 May 2021. Australian Signals Directorate. ASD cyber skills framework. 2020. https://www.cyber.gov.au/acsc/ view-­all-­content/publications/asd-­cyber-­skills-­framework. Accessed 15 May 2021. Chernyshev M, Zeadally S, Baig Z.  Healthcare data breaches: implications for digital forensic readiness. J Med Syst. 2018;43(1):7. Coventry L, Branley D. Cybersecurity in healthcare: a narrative review of trends, threats and ways forward. Maturitas. 2018;113:48–52. CSIRO. Cyber security: A roadmap to enable growth opportunities in Australia. CSIRO Futures. 2018. https://www.csiro.au/en/Do-­business/Futures/Reports/Future-­Industries/Cyber-­Security. Accessed 15 May 2021. Garg S, Williams NL, Ip A, Dicker AP. Clinical integration of digital solutions in health care: an overview of the current landscape of digital technologies in cancer care. JCO Clin Cancer Inform. 2018;2:1–9. Langer J. Mitigating health care’s cybersecurity risks in the era of hyperconnectivity. Med Econ. 2020;97(10):43–5. Mohapatro M, Snigdh I. Security in IoT healthcare. In: IoT security paradigms and applications: research and practices. Milton: Taylor & Francis Group; 2020. p. 237–60. Petersen R, Santos D, Smith MC, Wetzel KA, Witte G.  Workforce framework for cybersecurity (NICE Framework). 2020. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST. SP.800-­181r1.pdf. Accessed 15 May 2021. Ronquillo JG, Erik Winterholler J, Cwikla K, Szymanski R, Levy C.  Health IT, hacking, and cybersecurity: national trends in data breaches of protected health information. JAMIA Open. 2018;1(1):15–9. Ross J. Cybersecurity: a real threat to patient safety. J PeriAnesth Nurs. 2017;32(4):370–2. Therapeutic Goods Administration. Medical device cyber security guidance for industry. 2019. https://www.tga.gov.au/sites/default/files/medical-­device-­cyber-­security-­guidance-­industry. pdf. Accessed 15 May 2021. Webb T, Dayal S.  Building the wall: addressing cybersecurity risks in medical devices in the U.S.A. and Australia. Comput Law Secur Rev. 2017;33(4):559–63. Williams PAH. When trust defies common security sense. Health Inform J. 2008;14(3):211–21. Williams PAH, McCauley V. Always connected: the security challenges of the healthcare internet of things. IEEE World Forum in Internet of Things. Reston, VA: IEEE; 2016. p. 30–5. Williams PAH, Woodward A. Cybersecurity vulnerabilities in medical devices. Medical Devices Evid Res. 2015;8:305–16. Williams PAH, Perimal-Lewis L, Mudd G, Gunasekara G. Reimagining a better healthcare system through virtual care [White Paper]. 2020. https://www.cisco.com/c/dam/en_us/solutions/ industries/resources/healthcare/reimagining-­better-­healthcare-­system-­through-­virtual-­care. pdf. Accessed 15 May 2021.

Chapter 16

Working as a Health Data Scientist Natasha Donnolley, Lachlan Rudd, Oisin Fitzgerald, and Miranda Davies-Tuck

Abstract  The health data scientist is a relatively new specialty within the HIDDIN workforce. In this chapter we profile four very different people working in this critical role: a Research Project Manager, a Director for Data and Analytics, a Statistician and an Epidemiologist. While their job titles and journeys to become a health data scientist differ significantly, the case studies presented in this chapter demonstrate the one thing that they all have in common—harnessing the power of health data to address real-world problems. Keywords Case study · Health data scientist · Big data · Data analytics · Epidemiology

N. Donnolley (*) National Perinatal Epidemiology and Statistics Unit, UNSW, Sydney, NSW, Australia e-mail: [email protected] L. Rudd eHealth New South Wales, Sydney, NSW, Australia e-mail: [email protected] O. Fitzgerald Centre for Big Data Research in Health, UNSW, Sydney, NSW, Australia e-mail: [email protected] M. Davies-Tuck The Ritchie Centre, Hudson Institute of Medical Research, Clayton, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_16

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Introduction The increasing amount of real-world data—often known as “big data”—being generated through our health and welfare systems creates opportunities and careers never imagined before. If the COVID-19 pandemic highlighted just one thing, it was the need for real-time, real-world health data and specialists who know how to make use of it. Health data scientists have a lot in common with other specialists in the HIDDIN workforce (that is, Health Informatics, Digital, Data, Information and KNowledge experts) and often the roles overlap—epidemiologist, biostatistician, data analyst, data engineer, health information manager. An effective research team will involve collaboration between all these roles (Goldstein et al. 2020). However, one specialist—health data scientist—often brings together aspects of all these roles into one (Goldstein et al. 2020). According to Granville (2014) what sets them apart is the way they work with data: “DAD (Discover/Access/Distill)”. Data scientists are involved across the whole life cycle of data—they identify what data can be sourced or needs to be created, they identify how to access those data, and then they extract and distill information and knowledge in an applied manner. They often have the domain knowledge that a statistician may not have, the computer programming that an epidemiologist may not have, the communication skills that an engineer may not have, and they bring together different aspects of all those roles in order to address real-world, real-time questions. They are an essential part of the HIDDIN workforce that work with “big data”. Although this field was virtually non-existent 20 years ago, things are changing rapidly, particularly with the burgeoning availability and use of “big data”. Now it is not only a recognised career, as well there are university degrees specifically to teach the skills needed. Several exist in the USA and the UK, for example, and the Master of Science in Health Data Science run by the Centre for Big Data Research in Health at the University of New South Wales is the first postgraduate program in Australia to focus entirely on this discipline. As can be seen from the four case studies presented in this chapter, many people working in this relatively new specialty area may not necessarily go by the title of “Health Data Scientist”, but that is what their role encompasses. The case studies aim to showcase how the profession has evolved over the past two decades, how the paths to become a health data scientist are so varied, and some examples of the diverse work they undertake. Their titles are all very different, but the one thing they have in common is using their knowledge, experience and skills working with health data to address real-world health problems.

Natasha Donnolley, University of New South Wales, Australia Like many people who work in this area, I did not make a conscious decision to become a health data scientist. It took me several other careers and a personal tragedy before I ended up where I am, and even so, it still happened somewhat by

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accident. Having had a varied career since leaving school (including an unfinished science degree, a brief brush with politics and a series of jobs that had a health and later IT focus), I found myself out of the full-time workforce after having four children, and a need to retrain from my last career as an international IT consultant. Following the term stillbirth of my second child, I had become involved in consumer advocacy in maternity care. I knew I wanted to do something that could capitalise on my experience and networks in that area and make use of my background in IT and health. That is when I discovered the Bachelor of Science in Health Information Management then offered by distance education at Curtin University in Western Australia. I had never heard of Health Information Managers or clinical coding. I still was not entirely sure what I wanted to do with my future career, but I knew I wanted to work in research, and this degree seemed to combine my health and IT backgrounds perfectly. The BSc (HIM) was a great platform to develop the skills and knowledge I needed to work with health information and health data. Along with a foundation in anatomy and physiology, it gave me the knowledge and skills in nosology and clinical coding to work with the large routinely collected datasets generated by the Australian health system, and a basic level of biostatistics and epidemiology to get me started. I started a new career working in perinatal research at the National Perinatal Epidemiology and Statistics Unit (NPESU), at the University of New South Wales, in the last semester of my degree. I managed to combine the content knowledge I had developed over the previous 8 years as a maternity consumer advocate, with my experience in health and IT, with the health data science skills I had developed through my degree, and roll them all into the perfect role. Not only did my HIM degree prepare me for the role, it also gave me the grounding I needed later to undertake a PhD in the area. Even after commencing at the NPESU, I did not consciously think of myself as a health data scientist. When I started in 2011, it still was not a role or title that was used, yet there are many of us in the university sector who would identify as one. My titles have varied between Project Officer, Project Manager, Research Assistant and currently Research Project Manager. But essentially, I am a Health Data Scientist. All my projects revolve around creating, using, analysing and understanding health data—specifically related to mothers and babies—and increasingly utilising “big data”. The focus of my work at the NPESU initially was on data development. Using the skills I had developed during my degree in clinical classification, combined with my knowledge of maternity services, I developed the world-first Maternity Care Classification System (MaCCS) for the Australian Commonwealth government. The MaCCS is now being rolled out in all Australian maternity services to classify their models of maternity care as well as being used in the UK to evaluate components of the National Health Service Better Births policy. In addition to undertaking data development, I have worked with routinely collected national datasets, data collected through surveys, electronic medical records, focus groups and interviews, designed governance systems to manage “big data” platforms, and analysed and interpreted data. Everything I do revolves around health-related data and the “realworld” application of those data. For me, this work came from a very personal

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place—trying to understand why my own daughter died; trying to understand and improve the lives of pregnant and birthing women and their babies; and trying to prevent other families from experiencing the same unnecessary tragic loss that I experienced. I am not a doctor in a white coat, but I can make a contribution with health data science.

Lachlan Rudd, eHealth New South Wales, Australia Like many people in this sector, I took quite a round-about route to become a health data scientist. I completed my undergraduate Bachelor of Business at the Queensland University of Technology (QUT), majoring in Finance and Mandarin language. In my second and third year of that degree, I also worked with an innovative ICT start­up company led by a business mentor I had met during networking in my first year of study. I came in on the ground floor when the company was just a three-person garage start-up. I flew from Brisbane to Sydney to work 4 days per week and tried to condense my undergraduate coursework in Brisbane into a Friday. In the final year of my undergrad, the company moved to New Zealand to scale up development. I had an opportunity to defer completion of my degree and gain management experience, however, after a couple of weeks in New Zealand and much consideration, I decided to focus on finishing university first. At that stage, I had completed all subjects required for my finance major but still needed to complete six subjects in a minor. QUT had an international exchange program to learn Chinese, which I took advantage of. As I finished up my undergraduate degree in Shandong Province, China, my father and I decided to form a company together in the mining and infrastructure investment sector. I would base myself in China, focusing on Chinese government loans and provincial State-Owned Enterprise investors. My father looked at investment opportunities in Australasia that met the criteria of China’s regulators. After 5 years in China, I thought I’d take a break back in Australia and study mathematics, something I’d heavily considered out of high school, but feared to do as the career path was less clear. Two years later, I graduated with a graduate certificate and diploma of applied mathematics (statistics), followed by a Masters. I supported myself in between with a lead business development role, helping an ICT security penetration testing company expand into the Queensland market. Upon graduation, jobs in the applied mathematics space were highly competitive. My previous experience in China did not readily translate to the Australian market. I had spent 10 months in the final year of my Masters searching for opportunities without success. Unsuccessful in Australia, I was offered a private equity role in Shanghai, China. However, in my first 2 weeks there, an offer at CSIRO Canberra came through, so I returned to Australia to accept it. I spent several years at CSIRO building stochastic models for a behavioural economics team. We worked on a wide variety of projects predominately in the health, education, employment, auction design and strategic foresight domains. Following my time at CSIRO, I took a management job at Quantium, one of the first Australian private sector consultancies to focus on big

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data and advanced analytics. I spent just over 2 years there, leading several large international projects. I led teams of developers, data scientists, consultants and testers. Although none of the projects was health-based, my worked involved data analytics, cutting-edge machine learning and big data visual insight tools. All skills and experience that helped prepare me for my future work in health data science. Fast forward to today: I am now Director of Data and Analytics in eHealth NSW, the ICT services provider to the New South Wales Health Department. I have oversight of all data, analytics and research activity within eHealth NSW. I have been able to take the breadth of my experience and focus it on leveraging data and advanced analytics to drive value for NSW Health patients, clinicians and the system more broadly. Knowing I can make a meaningful difference, positively impacting patient experiences, motivates me to succeed. I see the future of data science focusing less on the development of complex and accurate forecasting techniques and more on how data can be made actionable. Analysing data in a dark room, and producing endless executive dashboards, lacking clearly actionable insights, will be a thing of the past. Data scientists will collaborate more with operational roles to truly understand where automation and prediction can be best applied to improve decision-making and maximise efficiency.

Oisin Fitzgerald, University of New South Wales, Australia My journey to health data science has taken me across the globe—from my home in Ireland studying a Bachelor of Science in Sports Science and Health to my new home in Sydney where I am currently completing my PhD at the University of New South Wales (UNSW). My first exposure to “big data” was during my honours project in sports biomechanics at Dublin City University, Ireland. I was very interested in the computational and signal processing aspects of the project and considered doing some postgraduate study in computing. Later I became more interested in how data could be used for decision-making, deciding to study statistics to further that interest. I enrolled in a Master of Statistics and Operations Research at RMIT University in Melbourne. I can distinctly remember a “light-bulb” moment where, despite being overwhelmed and lost in the equations, I experienced a level of satisfaction I had not gotten from previous study. Before finishing the degree, I left for Sydney, essentially doing the equivalent of a graduate diploma, and continued studying a Master of Statistics at UNSW.  The two courses had different focuses which I found very beneficial. At RMIT, there was a strong applied focus, on written and presented communication of results, including practice team consulting projects with real data which were very enjoyable and remarkably similar to workplace experiences. In contrast, UNSW had a more mathematical focus, on model derivations and fundamental thinking about data. This combination of experiences and courses was great as the mathematical approach gives an understanding that generalises well but learning how to communicate statistical results and make reports is an excellent job-ready skill.

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Like many students during university, I worked a variety of jobs. One summer, I worked as a research assistant during a PhD project on walkability which involved surveying households across Dublin, Ireland. I later did a similar job when studying in Melbourne, where I performed surveys for local councils. These seemingly unrelated jobs gave me a fascinating insight into the importance of the data collection process. Bias due to unrepresentative or poorly analysed datasets are two concerns in data science, and both these roles emphasised just how much work needs to go into well-designed research. While studying at UNSW, I began working as a Data Manager at the National Perinatal Epidemiology and Statistics Unit there, primarily working on the Australian and New Zealand Assisted Reproduction Database. This is a registry of all in-vitro fertilisation (IVF) cycles undertaken in Australia and New Zealand. The role involved a significant amount of reporting which resulted in developing my programming skills, learning about reproducibility, automation and building tools for making data cleaning and reporting easier. This was the real beginning of my work as a health data scientist as the role evolved into more of a statistician and data analyst, working on applied projects such as the detection of underperforming IVF clinics and developing IVF prediction models. The detection of underperforming IVF clinics was a particularly interesting challenge that taught me the importance of considering how the data was generated, and how this relates to actual clinical practice, the “map-territory” concept. I am currently doing a PhD at the Centre for Big Data Research in Health (UNSW Sydney) and working part-time. My PhD focuses on how to better use electronic medical records (EMRs) for personalised medicine—can EMRs inform clinical decisions about subgroups of patients? EMRs are very much a focus in health data science now, and an impressive data source as they are quite detailed, allowing for reasonably unconfounded analyses in some settings and modelling of the many decisions during a course of care. However, this detail is not without its challenges, with a lot of data cleaning and a general lack of interpretability of models that suitably account for the temporal and complex data generating process. I see a lot of changes for data science in the future, particularly around improving access to data while maintaining privacy. There has been a focus on software and empirical approaches to secure privacy-preserving data analytics in recent years, along with several advances in theoretical data science (e.g. homomorphic encryption), that makes me hopeful this will become an important toolset for data scientists. On the one hand, we have arguably intrusive use of our personal data by some companies, combined on the other hand with the general difficulty of access to healthcare data for research purposes (by both public and private institutions). I am hopeful we can move to a more principled approach to privacy-preserving analysis and data sharing that will ultimately benefit healthcare-focused research and technology. Lastly, I hope to see more practical applications of health data science. There are ethical issues that data scientists must understand about their work not existing in a vacuum. Taking a more methodological focus increases understanding of the importance of causality in data science. Data science is not about optimising the area under the receiver operating characteristic curve (AUROC) but improving decisions in real life. Students and practitioners need to learn and think about how

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data is created, and the biases and decisions that go into that process. Causal thinking about the data can make this clear and is an important skill for a data scientist.

 iranda Davies-Tuck, Hudson Institute of Medical M Research, Melbourne I have probably had a more traditional pathway to becoming a health data scientist than some. I completed an undergraduate degree in Biomedical Sciences with Honours. My undergraduate degree gave me a broad base in both basic and medical sciences. I then undertook a PhD at the School of Public Health and Preventive Medicine within the Department of Epidemiology and Biostatistics, at Monash University in Melbourne. My PhD focussed on non-communicable diseases epidemiology, and I was involved in several prospective cohort studies identifying modifiable factors for osteoarthritis development and disease progression. As part of my PhD, I also undertook coursework in epidemiology, biostatistics and research methodology. My PhD supervisors were both academic clinicians with strong collaborations with epidemiologists and biostatisticians. This provided many opportunities for me to learn new methods to analyse data and the importance of the clinical question being front and centre. During my PhD, I also had opportunities to be involved in teaching epidemiology, biostatistics and research methods and to work on large cohort studies and clinical trials where students were able to gain experience in fieldwork and data collection. These opportunities showed me many facets of clinical data science, including the challenges around recruitment and measurement. It was through my personal experiences with maternity care when I was having my children that my interest in maternal and perinatal research began. After 5 years at the School of Public Health, I moved to the Ritchie Centre, Victoria’s leading perinatal research centre. I extended my training by undertaking further coursework in biostatistics, and it was here that my exposure to large, routinely reported datasets began. My previous background had involved the recruitment, measurement and collection of data directly from individuals, rather than through accessing databases of clinical information or mandatory reported public health data. During my earlier work, my datasets may have had 300 or so participants, and now I had access to data on tens- and even hundreds-of thousands of women and babies. This created exciting opportunities to examine rare outcomes and overcame many of the sampling challenges I experienced undertaking prospective studies, as the entire population was captured. Perinatal datasets, however, have their specific challenges that I had to learn. There can be non-independence in the dataset as women may have multiple babies over the periods studied, and it is not always possible to identify them. There may be differences and or errors in how fields are reported, and sometimes the things you want are not captured. From an analysis point of view, it is imperative to carefully consider how to build models and how to present data, given the sheer number of fields available.

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My current work aims to address inequities in maternal and perinatal health; this includes inequities in access and care, and inequities in outcomes. My program of work spans laboratory-based discovery research embedded within prospective cohort studies, clinical trials, implementation science and health policy, working in partnership with academics, clinicians, health services and government. The opportunity that data science has to bridge these gaps is enormous; things can be observed at a population level in epidemiological studies, we can then take this back to the laboratory or undertake more detailed prospective studies and to understand the mechanisms. From there, we can then trial interventions or implement changes to practice. Coming full circle, large population-based datasets can then be used again to see whether these changes are effective. We also now have the ability to link datasets, so the life course impacts of different exposures and treatments can be observed. I see health data science and knowing how to analyse data as being the key to all aspects of medical research and the field of science that will drive future research, treatments and policy.

Conclusion These case studies highlight the diverse backgrounds, qualifications and work environments of health data scientists working in the field today. Health data scientists fill a variety of functions and roles within the health system in Australia and in other countries around the world: from Miranda’s more traditional epidemiology-based role, to the age of big data analytics in Lachlan’s role, and the use of data-rich electronic medical records that form the basis of Oisin’s PhD study. Despite their very different settings, the people profiled in this chapter are all part of one of the top emerging career fields. For all their differences, what ties them together is that the work they undertake has real-world application and impact; it combines the traditional areas of epidemiology and biostatistics with innovative cutting-edge computer science and machine-learning methodologies; and it involves the full life-cycle of health data. As their careers and skills have evolved, so too have their identities as health data scientists. “Big data” is the way of the future in health data science; with access to “real-­ world” data changing the way we view population-based health research and a greater focus on machine learning to find connections in data that may have been missed using more traditional research methods. Along with these changes comes the need for more formal career pathways and qualifications to prepare the health data science workforce of the future. Although Lachlan, Miranda, Oisin and Natasha have demonstrated how related qualifications and roles in epidemiology, statistics and health information management prepared them for their current roles as health data scientists, as this field matures we are beginning to see the emergence of new degrees and qualifications designed specifically for health data science. Along with maturity comes growing appreciation of the complexity and responsibility of the work of health data scientists: In the words of a prescient paper, their work spans

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re-use of clinical data for second order usages; design of artefacts and infrastructures; politics of creating and using data; algorithmic authority of information infrastructures in healthcare, and effects on the exercise of expertise and discretion of healthcare professions; new forms of healthcare data work, including new occupations; data-driven accountability and management in healthcare (Bossen et al. 2016).

References Bossen C, Pine K, Elllingsen G, Cabitza F.  Data-work in healthcare: the new work ecologies of healthcare infrastructures. In: Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion. 2016. p. 509–14. Goldstein ND, LeVasseur M, McClure LA.  On the convergence of epidemiology, biostatistics, and data science. Harv Data Sci Rev. [Internet]. 2020;2(2). https://hdsr.mitpress.mit.edu/pub/ twqhhlhr. Granville V. Developing analytic talent: becoming a data scientist. 1st ed. Somerset: WILEY; 2014.

Chapter 17

Working as a Health AI Specialist Angela C. Davies, Alan Davies, Anthony Wilson, Haroon Saeed, Catherine Pringle, Iliada Eleftheriou, and Paul A. Bromiley

Abstract  Artificial intelligence and the sub-field of machine learning offer the potential to deliver data-driven healthcare solutions that can improve patient care and increase efficiency in healthcare services. Despite this, the methods and models are new and complicated, to those who work in healthcare. This chapter explores the implementation of such solutions in healthcare settings, through five real-world case studies of experts applying this technology in a variety of clinical settings and at different stages of implementation. These cases highlight the challenges and opportunities posed by implementing artificial intelligence and data-driven solutions, and the lessons learnt from colleagues pioneering its adoption in the healthcare sector. Keywords Case study · Artificial intelligence · Machine learning · Clinical informatics · Predictive algorithms

A. C. Davies (*) · A. Davies · P. A. Bromiley School of Health Sciences, The University of Manchester, Manchester, UK e-mail: [email protected]; [email protected]; [email protected] A. Wilson Manchester University Hospitals NHS Foundation Trust, Manchester, UK e-mail: [email protected] H. Saeed · C. Pringle Royal Manchester Children’s Hospital, Manchester, UK e-mail: [email protected]; [email protected] I. Eleftheriou The Christie NHS Foundation Trust, Manchester, Manchester, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_17

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Introduction Artificial intelligence (AI) has been applied to digital transformation in healthcare services with the aim of deriving value from the volume of available digital data that is being gathered. AI is concerned with mimicking human activities related to cognition and problem solving. A sub-field of AI, machine learning (ML) allows us to generate mathematical models from “training data” that can be applied to new, hitherto unseen data in order to identify patterns and make predictions. There are four key considerations when embedding AI in healthcare: the digitisation of patient records; professional development, which includes staff being able to appraise data-­ driven technologies; the evidence base about AI technology; and the ethical and regulatory issues surrounding using AI in clinical settings (Topol 2019a). Ethical considerations are especially important given that some ML algorithms (especially deep neural networks) have been described as “black boxes”, where it is unclear how output is obtained (Topol 2019b), rendering clinical decisions based on such methods questionable from an ethical-legal perspective. Also an algorithm may obtain very high accuracy scores among other metrics but have little clinical utility if it fails to improve clinical outcomes (Topol 2019b). These caveats have led to the publication of advice relating to the transparency, human oversight and evaluation of such methods when they are applied to healthcare (for example, the European Commission, United States Food and Drug Administration (US FDA) and United Kingdom (UK) Government) (US FDA 2019; European Commission 2020; Department of Health and Social Care 2020). Areas of healthcare like medical imaging have seen a growth in interest in line with increases in computational performance (Wernick et al. 2010), and ML has been applied to other areas such as cardiac conditions detectable on electrocardiograms (Alfaras et al. 2019) and mental health conditions (Triffin and Paton 2018). This chapter explores five case studies based in the National Health Service (NHS) in the UK, covering a range of projects at different stages of development, all using or aiming to use AI in healthcare settings: initial feasibility work to introduce ML models for chemotherapy screening in a hospital pharmacy department; two paediatrics proofs-of-concept for ML applications, in rare children’s tumours and congenital hearing loss; promoting quality improvements in an intensive care unit; and automating vertebral fracture detection and reporting. In each case study, the project lead was interviewed by author Alan Davies or Angela Davies.

Case Study 1: Feasibility of ML for Chemotherapy Screening The project lead, Iliada Eleftheriou is an academic consultant at The Christie NHS Foundation Trust (a leading centre for cancer care in England) and a lecturer in healthcare sciences at The University of Manchester. She has a background in

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Computer Science (a PhD) and IT business management. Her expertise lies in mapping data landscapes in large-scale organisations for the identification of challenges and costs related to the movement and flow of data in digital transformation projects. This project looks at the feasibility of embedding ML models into clinical pathways in the chemotherapy screening process. When thought to be of benefit, chemotherapy is prescribed to cancer patients, in a regimen to prolong the patient’s life, for curative purposes or for palliative purposes. A regimen defines the types of drugs used, duration, dose and frequency (Feather et  al. 2020). Pharmacists validate the regimen prescribed by a consultant (called chemotherapy screening) to ensure there are no contraindications with any other medications the patient may have been prescribed. There are well-defined criteria, baselines and protocols for screening; around 90% of screenings find no further action required. However, the number of screenings is estimated to increase by 5–7% each year, thus increasing clinical workload and the need for additional capacity. ML approaches have the potential to automate parts of this process, to decrease the time pharmacists must spend on screening and at the same time to scale up the volume of screening. Introducing a new AI model within an already complex infrastructure comes with a myriad of challenges. These challenges are not merely technical in nature but often stem from the variety of organisational structures or human processes involved in the patient and clinical pathways (Eleftheriou et al. 2016b). Technical difficulties arise when sharing or integrating information from the diverse data sources involved. Other challenges stem from the social aspects of the organisation, its people, policies, processes and governance. For example, we find people being reluctant to change their current processes to use the new system in place, or user requirements are not met because of conflicting organisational policies and governance issues (Eleftheriou et al. 2016a). So Iliada’s project used a mix of methods and tools to assess feasibility: focus groups and observation sessions with pharmacists were undertaken in order to understand the current workflows and processes. The Landscape, Organisations, Actors and Data (LOAD) model (Fig. 17.1) was used to organise and structure findings. A data journey model was used to map the current data landscape and existing data movements to identify challenges and potential risks. A new data landscape was proposed to reduce risks and costs of embedding ML models in the workflows. The data to be used for the ML project are in both structured and unstructured forms, including unstructured data in scanned text and documents and hand-written notes. Data are stored and captured in several systems and require significant manual curation. The data landscape refers to the infrastructure within the Trust where the data reside; the storage locations of the data (both digital and physical) as well as where the data come from, where it goes and who accesses it. Data journey modelling can be utilised to map the movement of data around the systems in the organisation, to find areas of increased risk or areas that could be improved. This can model the human, technical and organisational challenges in data flow (Eleftheriou et al. 2016a, 2018).

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L: Interactions between OAD under various contexts (research, application areas). E.g. Dashboards

O: Organisational structures within the NHS, myriad of policies and agreements (often confilicting requirements), data sharing standards (HL7 messages) A: Actor (both people and systems) interactions with data, but also org.policies, Human Computer Interactions. User Interfaces E.g. Swimlanes D: data types, formats, datasets available, how they vary across trusts and teams

Fig. 17.1  LOAD dimensions and factors affecting the adoption of new AI models

In Iliada’s words: “We’re analysing the pathway associated with the Trastuzumab, a monoclonal antibody used to treat breast cancer. We’re particularly focusing on this drug, as prescribed for early breast cancer, because the clinical guidelines involved in this drug are simpler with a very well-defined, straightforward protocol (National Institute for Health and Care Excellence 2002). Other drug protocols are more complicated, so we wanted an easy example to start our analysis. In order to map the current data landscape, we’re not only analysing the data itself, we’re also analysing the processes needed to capture the required data and which data to collect and store in the systems. We had to look into how this information is being used, how the information is being transformed, the various actors involved, the people along the patient pathway that would have to interact with the information, as well as the systems that will store and use the information.” From this analysis, the primary challenges to feasibility have been found to be: processing large volumes of unstructured data; dealing with missing information; identifying the correct document and determining its position in the timeline; determining data provenance. Many clinical datasets have missing values (Köse et  al. 2020), but discounting missing data during analysis can reduce statistical power (Enders 2010). Many ML algorithms are adversely affected by missing data and decision-making using such methods (support vector machines, principal component analysis, random forests and neural networks) would be inadvisable (Köse et al. 2020). Observation sessions with pharmacists going through several screening processes found that most of the time of a pharmacist’s workflow is taken to trace the right documents in the system, trying to identify the timeline of events that happened for a particular patient, in order to identify the missing values. Human factors and the mode of engagement with stakeholders are critical, although some may think that automation is a straightforward process—we have a protocol, we create an ML tool, we embed it into the workflow, and we hope for the best. Doing feasibility analysis early on will flag up any warning signs, identify the socio-technical factors and potential costs and risks that might arise throughout the project, try to anticipate the human and organisational elements; it is equally important to keep analysing and identifying upcoming factors throughout the life cycle of the project. The acceptability of the solution is as important as achieving good quality outcomes from the tools developed. A challenging part of the work is simply

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finding time to work with busy clinicians and pharmacists, to illustrate the benefits for their everyday routines and encourage their questions—but it is crucial for them to be invested in the project. Some may fear AI replacing human jobs, so part of the feasibility analysis ensures appropriate communication that such tools are designed to support end-users, not to replace them, and to work alongside the essential human element of healthcare. The project team involves many different disciplines, trying to break the boundaries between academia and healthcare, academia and industry and governmental bodies, to work on this clinical problem: pharmacists, the head of the pharmacy department, the analytics team, the data warehouse team, machine learning experts, and the business intelligence team and directors and managers. Having to analyse the social aspects of the project—for example, the people involved, the governance of bringing the data into that form where it can be used—is complicated and takes time, but the lessons learned are very important to create the right foundation for developing the right AI model. For example, one of the biggest challenges originally was having access to the data and processes involved; this was overcome by relatively quickly setting up contracts for honorary appointments, with the organisation providing the data. This brought the data scientists to the data instead of trying to move the data to the data scientists, and created a new way to collaborate. The project makes use of the Agile software development methodology (Agile Alliance 2020) that focuses on iterative development. Agile is often also considered a mindset rather than just a methodology, and the way that we have tried to establish effective practices and ways of working within the team, is by following the Agile mindset. So, we emphasise a set of values within the team, for example, including respecting each other, collaboration within the team and continuous improvement. We have learning cycles, we take pride in ownership, focus on delivering value and, most importantly, the ability to adapt to change. The impact of the feasibility work is difficult to measure during the first stage of a larger project, but this phase concludes with a report summarising the data journey models (Eleftheriou et al. 2018) and feeding into the next phase, where a team will produce the ML tool/algorithm that will be embedded within the patient pathway. The technical aspect of the project—the maths behind it, the data, transforming the data into a form where you can use it and train your AI models—is the easy part, in Iliada’s experience.

 ase Study 2: Predicting Outcomes in Children’s C Brain Tumours Catherine Pringle is a neurosurgery trainee based at the Royal Manchester Children’s Hospital and also a PhD student at The University of Manchester. Catherine works in an extremely emotive area of medicine, children’s brain tumours, aiming to

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provide more personalised outcome predictions and recurrence and relapse profiles. As a study cohort, they are quite a challenging group to deal with, and it is a heterogeneous diagnostic field. As more is being discovered regarding these tumours from a molecular and biological point of view, observations indicate that they fall into more and more diverse sub-groups. Children’s brain tumours are the second most common childhood malignancy and the leading cause of cancer-related death in children (Shih and Koeller 2018), however the overall numbers are very small, compared to adult colon or breast cancers. This creates a high dimensional data field, with relatively small numbers of study patients, but with a lot of deep information about each individual patient. Firstly, Catherine and colleagues are looking to harness the information generated from advanced multiparametric magnetic resonance imaging (MRI) of brain tumours, but then to combine it with the full data set held for these patients. Using this large retrospective data set they hope to identify more accurate and precise survival trends within these patient groups than detected by traditional methods such as Kaplan Meier survival analysis (Rich et  al. 2010). Repeated patient imaging has associated patient-centred risks of repeated general anaesthetics and impacts on anxiety and quality of life (Cravero et al. 2009), and more precise and individualised survival outcomes could streamline its use. Current predictive models for childhood brain tumours use tumour diagnostic type data and possibly surgical resection data, however, Catherine’s work is one of the first projects aiming to incorporate whole patient data to make predictive models. This involves collecting large amounts of structured and unstructured data from tumours in the posterior fossa in the cerebellum of children (the most commonly occurring tumour location). Additional data may include histological data and biological diagnoses, which are identifying a lot more discrete tumour types. This kind of data can be used to support imaging data, historically CT imaging or more advanced MRI imaging. In addition to diagnostic data, there is also a large amount of demographic data such as presenting signs and symptoms, how long patients had symptoms before they presented, the surgical techniques used and whether any surgical adjuncts were used during the procedure, and also oncological treatment such as chemotherapy or radiotherapy. Relapse and recurrence data too are being captured, including when, how long after initial presentation, surgery, any other treatments and whether at the same site or distant sites. The data are heterogeneous: from newborns to 18-year-olds; located in a variety of notes, scan reports and histological data; many are paper-based but some are held within a partial electronic system within the hospital. Catherine’s patients can come from a large catchment area, meaning that some of their imaging is done at other hospitals and they attend clinics in other hospitals, so their data may be held elsewhere. Decentralised storage makes accessing imaging data and scan reports challenging, and could lead to gaps in the data sets. Imaging data files are large so moving them between hospitals would be challenging, but fortunately the focus of Catherine’s work is on specific findings already known from imaging that has been done. Magnetic resonance imaging as a technique is evolving every few years, meaning that more recent scans may have more detailed imaging in comparison with earlier scans, which could affect outcome predictions (Villanueva-Meyer et al.

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2017). Additionally the content in scan reports can vary, sometimes simply providing a disease assessment and not always describing the scan features in sufficient detail. Progress toward undertaking machine learning is in its early stages: following data collection, the data will be split into trial and test sets, thereby creating a set of data not previously used during the study, ready to trial with any potential successful techniques. The trial set is going to raise the question of statistical overfitting, and whether increasing our data set size can overcome that problem. Ethical approval covers two external NHS sites, so if the study set is too small, there is access to additional cases to build up the trial set and to create a completely unseen test set. In terms of human factors, Catherine’s project is supervised by a small but highly qualified academic team, including clinical professors in paediatric neurosurgery, paediatric neuro-oncology and paediatric neuroradiology, whom she works with on a daily basis, so their shared language already is familiar and routinely used in such teams. Her project also involves working with a professor of data science and will grow to incorporate further data science based academic collaborations.

Case Study 3: Precision Medicine for Congenital Hearing Loss Haroon Saeed is a specialist trainee in ear nose and throat (ENT) surgery, currently employed by Manchester University NHS Foundation Trust and also a PhD student based at The University of Manchester. Haroon’s project involves assessing a group of children who have a congenital and progressive hearing loss condition, called enlarged vestibular aqueduct (EVA) syndrome, caused by an enlargement of one of the bones in the inner ear known as the vestibular aqueduct (Belenky et al. 1993). This condition causes progressive hearing loss and ultimately it leads children to be profoundly deaf by adolescence. Haroon is able to rehabilitate children’s hearing by implanting a hearing aid device, known as a cochlear implant, and audiologists are able to provide hearing aids to them. The issue for these children and their parents is that it is very difficult to predict when their child’s hearing loss is likely to worsen, how fast, which ear it will be affected the most, and importantly when to do cochlear implantation surgery. Haroon’s work aims to improve precision medicine in EVA syndrome, first by discovering the prognostic features or clinical biomarkers that relate to worsening hearing and then by using these to build a prognostic model or decision support tool. The aim for a newly diagnosed patient is to predict the severity and degree of their hearing loss, and plan appropriately for their surgery or hearing aid implementation. This will ensure that the team provides the right treatment to the right patient at the right time and will streamline the correct hearing surveillance and audiological testing. In data science terms, Haroon and colleagues are using different methodologies to approach this research—one approach is based on established inferential statistics and the other is exploring the role of ML. They have data about a large number of patients from cochlear implant centres in Manchester and London. Haroon’s

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project is using retrospective unstructured data, collected from approximately 450 patients’ ears, across three different NHS sites. Each site has different electronic patient record systems, ways of coding their data and different scanning machines. The data collection for this project has been informed by the current prognostic literature for this disorder and includes: genetic variant data, features in the inner ear from the radiological scan, demographic features (age and sex) and hearing loss data (Aimoni et al. 2017). These prognostic biomarkers are a mixture of quantitative and qualitative data. An important consideration when collecting the radiological data will be to ensure that the measurements and features selected are reproducible. It is vital that reproducible measures are used to form the building blocks of a clinical prediction model to ensure that it is both generalisable and transferable across healthcare settings. The data are being collected in a bespoke cloud database platform, which means pseudo-anonymised data can be uploaded remotely, from different sites, ensuring that the data are collected in a unified way. In terms of human factors, Haroon has brought together an interdisciplinary team with complementary expertise, including an ENT surgeon specialising in working with children with this condition, a professor of genetics, a neuroradiologist, an academic with expertise in data science and also another academic with experience in inferential statistics. Haroon has had to invest time in developing his knowledge and understanding of the respective areas of expertise; in doing so, he has developed a shared language and knowledge base for the team to use to discuss the project.

Ethics and Governance in Cases 2 and 3 Catherine’s project is a retrospective study requiring ethical approval to access and work with data from her own hospital and two external sites but in this small disease cohort even pseudo-anonymised data have the potential to identify a patient. The system for ethical approval through the Integrated Research Application System (IRAS) within the NHS assumes randomised controlled studies involving very large cohort sizes. It is not set up to deal with projects that are applying ML techniques to investigate much smaller patient cohorts, using retrospective data (Grist et al. 2020). Generally ethics committee members lack understanding of ML and this can result in rejections or issues being raised during review, even though the studies are based on safe and appropriate approaches. Similarly, funding bodies might not always see the applicability of ML approaches to healthcare data, perhaps deeming that this is not sufficiently big enough data to be used to make predictive models. There may also be debate over whether the tool or model is a medical device, which would then need approval by appropriate regulatory bodies such as the US FDA (2019) or UK MHRA (Rowley et al. 2019). The studies described by both Catherine and Haroon aim to produce an academic model rather than an externally validated clinical prediction model. It can be

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extremely time-consuming to get the correct ethical approval and informatics governance in place and to set up contracts and data processing agreements for these kinds of multi-site retrospective studies. A longer term issue even after a study is approved is to build clinician trust and confidence in accepting electronically generated outcomes, therefore successful implementation depends on robust evaluation and clinical governance of ML approaches. This can be difficult to achieve, as demonstrated in these cases, where the data have never been assembled and interrogated in a similar way before.

Training the Humans, in Cases 2 and 3 Haroon had no previous experience in machine learning or programming, and it is unusual for a clinician to have these skill sets. As more concrete examples come through from landmark studies that illustrate real benefits to patients (Hildebrand et  al. 2009; Taylor et  al. 2013), Catherine and Haroon agreed that these sorts of cases should be integrated into the undergraduate medical curriculum. They also thought that ML or data science should be included within the medical curriculum. At an introductory level medical students have the option of undertaking 6- to 12-week clinical or research based modules, which they suggested could be based on an underlying artificial intelligence methodology. Medical students with a strong interest in ML and data science could intercalate a Bachelor’s or Master’s degree into their medical degree. In terms of postgraduate opportunities for specialist training in this area clinicians could contribute to audit or quality improvement projects, which are more of a taster-level introduction to ML approaches. In terms of more formal postgraduate training in the UK, AI could be taken up through some of the limited numbers of highly competitive clinical training posts, called academic foundation posts, with designated research time. Once in a clinical post, carving time out to do research can be challenging. Both Haroon and Catherine were successful in applying for independent charitable funding to support this, but they also do intense out-of-hours clinical work to make time for their research. Health Education England’s Topol Fellowship programme (NHS Health Education England 2019) is another avenue for healthcare professionals to dedicate time to data science driven projects.

Impact in Cases 2 and 3 Both Catherine and Haroon were asked what the impact of their projects had been to date. Haroon: “When you are within your speciality as a trainee, that is your world essentially, and you’re not aware of all these other things that are going on

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around you. One of the added benefits of this project is actually eye-opening, in terms of what expertise there is within the university and, indeed, within our other specialities within our NHS Trust. So I’d say those two are impacts, and the culmination of that has been a collaborative paper.” Catherine: “We’ve looked at a rare children’s tumour group and we think we’ve found a radiological phenotype for separating it into two tumours, one of which you can operate on, and, theoretically have a big impact on survival. The other still unfortunately has a dismal outcome, but they are two completely discrete tumour groups and when you apply these levels of filters of the advanced imaging, you can separate them out and decide which one should have surgery and which should not. This reinforces the role for using these advanced metric scans to characterise small rare tumour groups and, even though it’s not something big from a health economics point of view, you can actually make a big impact to a child’s survival.” Haroon has published a literature search investigating potential prognostic markers for his project in a very high impact journal, disseminating his research and hopefully influencing others in their fields in the future (Saeed et al. 2020).

Conclusions Regarding Cases 2 and 3 Although Catherine and Haroon’s projects are quite different in their clinical focus, they are both proof-of-concept projects to determine if AI approaches can help to discover prognostic markers within the retrospective study groups and so both raise similar workforce issues. Data collection for these kinds of prognostic studies concerning heterogeneous data is laborious and time-consuming but requires clinical expertise and domain knowledge to enable assessment of the quality of the data to see if it is suitable to be used for ML algorithms. Data need to be collected in an agile manner; databases for the collection of this sort of complex data need to quickly adapt to capture the richness of the data, particularly with techniques such as MRI which are developing all the time. When data are captured from very diverse systems and reporting structures, manual curation and data entry may lead to introduced errors, which could flaw the starting data sets. A secondary benefit of a detailed analysis of retrospective data is that it can also be used to find important patterns in the data, which have not been previously possible as the data have not been held in one single place, this may help to drive quality improvement projects. Education about ML and how we can use it on health data to improve patient outcomes needs to be much more accessible—among the healthcare workforce, among important gatekeepers such as ethics committees, and among community stakeholders such as future patients. Within hospitals there is a need for a translator role, that is, clinicians who understand the data in detail know the questions that the data can answer and are conversant with the kinds of ML to apply. Placing people in such roles would make it much easier to have conversations in collaborative teams with experts in ML and data science, to drive healthcare projects forward.

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 ase Study 4: Building a Learning System in the Intensive C Care Unit Anthony Wilson is a consultant in anaesthesia and critical care medicine at Manchester University Hospitals NHS Foundation Trust. He is the clinical lead for healthcare informatics in the Department of Adult Critical Care and works in the Intensive Care Unit (ICU). Within the adult ICU where Anthony works there are a number of healthcare databases that record vital signs and details of interventions that doctors and nurses carry out with the patients. Anthony’s project aims to bring together all those data, link them and start to apply ML techniques to the data set to promote quality improvements for patient care. Anthony aspires to “mimic” MIMIC III (Medical Information Mart for Intensive Care), a landmark US project that created an open access single database with integrated de-identified clinical data of patients admitted to a tertiary care hospital (Johnson et al. 2016), or emulate other initiatives of this kind such as the Paediatric Intensive Care (PIC) database developed by a tertiary hospital in China, freely accessible under the terms of a data access agreement (Zeng et al. 2020). Bringing all the data together is not a simple matter. In the ICU patients are monitored in more detail and more frequently than anywhere else in the hospital. For the entirety of their stay, each patient is connected to a continuous vital-signs monitor, recording their heart rate and blood pressure, and also closely observed by a critical care nurse stationed at the end of their bed. Unfortunately, patient data are sequestered in many data silos and not easy to access in one central database. One of these is the ICU Electronic Patient Record (EPR); it contains approximately 10 million records, including electronic free-text notes written by doctors and nurses about the patients they’re caring for, and structured forms that doctors and nurses use to enter specific care events such as admission and discharge data. It also collates information such as vital-signs data pulled directly from monitors and ventilators and, from other hospital departments, blood, X-ray and microbiology results. In addition the EPR contains quantitative data that the nurses measure and record and enter manually, such as volume of urine, or blood exiting from a drain. The hospital also has an ICU outcomes database covering key patient parameters and critical care outcomes, where data are entered retrospectively by data clerks. Some information also is drawn from a national audit project that supports comparisons among ICUs. Data are not always used to advantage; one example is that the patient vital-­ signs monitor outputs a lot of data such as heart rates and oxygen saturations as waveforms, complex and useful data that currently are discarded after 72  hours. Another example is that when a patient leaves the ICU, their still largely paperbased notes are scanned into the EPR as a PDF file, an unstructured document that makes it difficult to automate extraction of information. The benefit for patients is personalised local healthcare, which Anthony calls “evidence-based medicine 2.0”, because it is an advance on how doctors now practice evidence-based medicine and use trials and apply trials in their specific patient

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circumstance. What ML enables is to personalise the care that patients receive down to the individual level and the local level as well; clinicians will be able to look through the data and understand what they’ve done for ICU patients in Manchester and how that has worked. So, they can say to a patient: “bearing in mind all this evidence that’s already come from our intensive care unit and with our patient population, this is what we think will work best for you”. ML also enables clinicians to improve the amount of harm-free care that they give. To minimise the chance of iatrogenic injury constant vigilance is required, but no human being is able to provide this level of vigilance, and no ICU doctor is able to look at all the available data all the time. According to Anthony, “So much of intensive care is about doing simple things well; it is not actually that difficult to provide organ support and look after a patient intensively, it just needs to be done systematically and well all the time.” A quality improvement project underway across the entire NHS called “Getting it Right First Time” has a large focus on anaesthesia (Getting It Right First Time 2020). Anthony believes the data that he is collecting can be used to provide individualised performance feedback to clinicians about patients they have treated and whether a patient deteriorated following discharge (similar to the system implemented for measuring the outcomes for surgeons in the NHS). “So one of the things that we’re doing with the data at the moment is a little extubation project. Patients go on a mechanical ventilator with an endotracheal tube, not having an anaesthetic, and then at some point in time when they get better we take them off the ventilator. You have to choose the right time to take the breathing tube out, and one of the markers of performance is how many patients need to go back on the breathing machine within 48 hours. That’s really based on a decision about whether or not this is the right time to take the tube out or not. [Staff in the ICU] are really interested to see how we do as a unit and how they all perform individually.” Getting access to the data, which is held in a Structured Query Language (SQL) database, was initially difficult as local informatics teams are not used to clinicians requesting this kind of access, Anthony and a colleague approached this by teaching themselves to programme in SQL, enabling access to the data needed. The database is complex as it holds data from four different ICUs in the hospital, each of which set up their own data structure. The database from Philips is based on SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) (Bodenreider et al. 2018) and has all of the SNOMED codes built into it. However, each ICU team decided to make their own structure within the existing SNOMED structure as each team had specific requirements, meaning that mining the data for the right clinical term was very complicated. In addition, as clinicians in the ICU weren’t able to visualise the data in its rawest form they couldn’t advise the informaticians over the best way to address the database. The key step forward was when Anthony and his colleague were given access to the SQL database, in order to query it and work through the structure, and mine the database much more effectively. Access to the database still needs to be very controlled, ensuring that hospital laptops and VPN access are used if accessing the database from off-site. Mutual trust and deeper understanding of the SNOMED codes used allowed them to work together with the informatics team to

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build a dashboard with a graphical user interface to display patients’ ventilatory parameters during the COVID-19 pandemic. The importance of developing a culture of mutual trust was exemplified in another project focused on outcomes from ICU patients with COVID-19. In this example clinical colleagues appeared reticent to undertake any statistics or propose models themselves, however Anthony would like to see a much more proactive approach—clinicians taking the lead with more of the data analysis in the future, working collaboratively with statisticians to refine the models. He aspires to promote a two-way dialogue regarding what could be achieved with the data, arguing that if some of the data cleansing and structuring can be undertaken by clinicians with experience in that clinical area then it will reduce input and time needed from the data scientists. In terms of the broader team Anthony describes that it is important to have support from the Chief Informatics Officer (CIO) or equivalent informatics lead in the hospital. In his hospital they are not only the data controller but also somebody who can act as a figurehead and be an advocate for data science in the ICU. Having the support of the Medical Director is also important in taking any data science projects forward in the ICU. Anthony’s ICU now has a member of the hospital informatics team embedded within the ICU, who has been integral to setting up the databases and now is particularly invested in the unit’s data science projects and keen to see the outcomes from using the collected data. The collaborative team within the ICU also consists of an ICU registrar, contract software and database engineers and research software engineers collaborating from the academic side, all working to create an API linked to a server to collect the waveform data. As yet there are no data scientists or statisticians collaborating on the project, but the focus to date has been on assembling and curating the data to see what is feasible, before the main clinical research questions are defined. In terms of ethics and governance within this project, Anthony has found it challenging to find out exactly what is permissible with the data he is collecting, and hard to find expert governance advice from somebody with a strong understanding of ML approaches. There is a need to widen the pool of colleagues who can act as decision makers for information governance, enabling better use of the data that the hospital is collecting and improved guidance on how to pseudo-anonymise data for use in these sorts of studies. His view was that the hospital has a duty to undertake health-related research, and that this should be the main aim of any such work with the priority being to ensure that individual patient anonymity is retained. Much of Anthony’s work will lead to service improvements; simply having all the data located in one place will save huge amounts of staff time during audit and quality projects, typically undertaken by junior doctors attempting to collate disparate and distributed datasets. Describing himself as a “positive disrupter”, he argues that the time saved could be used to change attitudes and effect more immediate changes in performance, through constant monitoring using dashboards for example. His aim is to see patterns and new things in the data that perhaps would not have been possible previously whilst all the data were distributed; using ML approaches to make predictions from the data would be beyond the primary reason for collection because

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such manipulation and interrogation of the data means that the project is defined as research and therefore requires ethical approval. Anthony describes mistrust of ML, elaborating that although colleagues may know that these approaches exist they may not understand what they can do or how they might relate to existing statistical techniques. Best practice in scientific computing advises working in small steps and obtaining frequent feedback from end-­ users, working in an agile manner, responsive to changing demands (Wilson et al. 2014). For Anthony’s projects one of his main concerns relates to the end-user interface where any final tools will be deployed, as this will influence adoption by clinical colleagues. He suggests adoption is most likely to be supported if implemented within a device that was easily accessible at the time needed. The type of interface and choice of device for deployment might vary considerably depending on the part of the care pathway it was being used to support. For example, there would be some tools that would be beneficial at the bedside for direct patient care; others during ward-round events where Anthony might be leading overall direction of care decisions with a team of junior doctors; still others in critical care and management events which might provide direction on quality of care for the entire unit. The requirements and the questions that each of these scenarios would ask of the data vary considerably. Anthony’s view is that it is likely to be in the more senior management events where any kind of decision-making tool would have the most likelihood of adoption and success. Regarding integration of decision-making tools, Anthony comments: “How you put that interface into people’s hands in the hospital is a concern for anything that I do with machine learning or whatever it might be in any data-driven project. The set tools that people use to interact with data and hospital patients, like the Philips database system—the front end of that is the monitor for the ventilator. [It is unsatisfactory that] these are all manufactured pieces of equipment where I can’t interact with the source code at all, I can’t do anything about that; so anything that I produce is going to sit on some random computer screen, or it’s going to be separate from the work streams that people use routinely.” In terms of training, Anthony has built up his expertise in AI in healthcare informally. Prior to becoming a doctor Anthony completed a physics degree which has helped him with the maths and statistical knowledge needed to understand ML. He has also accessed informal training through online freely accessible courses such as Massive Online Open Courses (MOOCs) and also open community resources to support programmers, such as Stack Overflow (https://stackoverflow.com/). Like other UK doctors, he has a limited budget towards his own training and development; in the future, he would like to use this to develop his knowledge through more formal training in high quality data science courses. He, like Catherine and Haroon, feels the need to strengthen the data science taught in the medical curriculum, including data cleansing, structuring and interpretation and argues that too much of the curriculum focuses on interpreting tests and confidence intervals, rather than focusing on the data, and what is possible from analysing it. He also supports much earlier immersion in data science as a trainee doctor, such as options to do intercalating degrees within your fifth year of study. Following completion of a medical degree there is the option to undertake an academic foundation training programme,

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which could incorporate a 4 month data-driven research project; however, this would need champions and supervisors among consultants with experience of these kinds of projects. The next level up would then be clinical fellowships where clinicians would be able to pursue dedicated master’s degrees to develop data science expertise. Conclusions: • Collaborative relationships between informatics teams and clinicians where the barriers are less distinct develops a mutual understanding and trust of what is achievable and what each party can bring to a data-driven project. • To improve outcomes for patients, we need more clinical colleagues with expertise in the governance of data-driven projects, who understand what is possible and facilitate discussions with research ethics and governance bodies. • The data that is routinely collected within a critical care unit is complex, rich and large, and can be used to drive data-driven clinical decision support and improve the quality of care. The choice of interface and route of deployment for any such tool needs to involve the end-user to ensure adoption. • Training in data science should follow the pathway of training for a clinician, being embedded within research opportunities within the medical degree through to data-intensive research projects during registrar training, supported by clinical champions with data science expertise.

 ase Study 5: Automating Vertebral Fracture Detection C and Reporting Paul A.  Bromiley is a lecturer in Health Data Sciences at The University of Manchester, where he works on the development of computer-aided diagnostics for medical imaging, with a focus on musculoskeletal radiology. Paul’s project involves the development of a system for the opportunistic detection of vertebral fractures on computed tomography (CT) images. These images are requested for other clinical reasons, but often image the spine and so provide the opportunity to detect vertebral fractures. Vertebral fracture detection is important because these fractures are often the earliest clinical manifestation of osteoporosis (Gonnelli et al. 2013); the earlier a patient can be diagnosed, the earlier they can be treated, so improving current diagnostic rates will in turn have a positive impact on healthcare quality and cost. Paul says: “Vertebral fractures should be reported by the radiologist as an incidental finding, but we know from clinical practice that those vertebral fractures are quite rarely reported. The Royal College of Radiologists’ national audit in 2019 (Howlett et al. 2020) found that on average, only 26.4% of the vertebral fractures visualised incidentally in CT images were accurately reported and 2.6% of the patients were referred on for proper management.” One of the issues affecting diagnostic rates is the shortfall of available radiologists in the country; it isn’t just a matter of asking radiologists to spend more time processing images and reporting vertebral fractures;

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there simply are not enough of them. This is where the idea of using ML and computer-­aided diagnostic systems comes into Paul’s project. Clinical image data are often presented in the DICOM (Digital Imaging and Communications in Medicine) format. These images have an extensive header which contains information regarding the patient, including, weight, height and sex, some of which provide information on fracture risk. They also, however, contain personal identifiable information such as the patient’s name, address, referring physician and NHS number, which need to be anonymised. Despite the DICOM format being a standard, it is often poorly adhered to. There is a DICOM dictionary which is used to provide information on what should be included; each entry has a tag number, and these are all even numbers. The DICOM format allows individual manufacturers to add non-standard, odd-numbered tags to include additional information, which can include a field containing patient information, also requiring removal to prevent identification. In this case study the quality of the data itself is fairly high as it is used for diagnostic purposes. However radiographic artefacts and the presence of extraneous items like cardiac pacemakers/defibrillators, stents, shunts, surgical clips and other medical devices can obscure bony edges. For data analysis, the team has developed several bespoke software packages, most of which are open source or licensed to users, e.g. TINA (Schunke et al. 2012) and VXL (University of Manchester 2020), to identify landmark points in images. Landmark detection has been used for tasks like facial recognition, where facial landmarks like the eyes, nose and mouth can be detected. The team Paul works in has been developing algorithms for 25 years and aims to develop an algorithm that can annotate landmark points on structures in a given image. These points should be identifiable across all the images in a given image class. Aside from developing their own algorithms, the team also use “off the shelf” solutions, for example various Python libraries for results processing and producing graphics. The team often combine methods (e.g. TINA, VXL and Python) for the purpose of rapid prototyping. When identifying landmark points, it is important to try to identify commonality. For spinal image annotation, points are marked on the edges of the vertebrae. This allows the development of a shape model. By annotating landmark points across a number of individuals with variation in shape, the covariances of the points can be determined to produce a model with parameters that can be varied to fit a given image. Image intensity can also be included, producing an appearance model. The most recent version of this approach uses a random forest to model the image intensities around each landmark point, together with a shape model describing the whole set of points. The shape model acts as a constraint during fitting of the individual intensity models, helping to guide them to the correct locations in the image (Lindner et al. 2015). The team has had considerable success with using that particular kind of model in a whole range of different image types, for example, various different musculoskeletal imaging problems, in the spine, the hip and the knee. When it comes to training data, humans are required to mark up the images to define these landmarks, annotating points in training data. This involves potentially marking up thousands of points in hundreds of images, each image taking about

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typically 30–40 min. For Paul’s project, external contractors in the form of retired radiographers from the Manchester Royal Infirmary were used. The contractors had all worked in the bone health unit and had undergone training in this area. Ethics and governance in this project respect all seven key requirements of the European Commission’s report on ethical guidelines for trustworthy AI (European Commission 2020). However Paul argues that in a medical context, human agency and oversight is of particular relevance. This includes having a human “in the loop”, demonstrated here by having a trained radiologist as part of the loop. The algorithm generates the clinical reports that would be reviewed and, if necessary, corrected by the radiologist, prior to return to the hospital. Aside from the standard issues of applying for ethical approval to access clinical data and the need to anonymise the data, deeper ethical and philosophical issues have arisen around how to handle hitherto undiagnosed conditions detected through the course of the project. Since the data are anonymised, there is no easy way to feed such diagnoses back to inform (and potentially treat) a patient. The dilemma, thus, is whether or not the team should maintain a pseudo-anonymisation system that, if required, could enable the re-­identification of patients by the clinical facing part of the system. The main drawback of this kind of system is increased risk of identification of patients by staff unauthorised to do so. The eventual compromise was to anonymise everything. In Paul’s words: “we argued this back and forwards for years, I’m very unhappy with that system, particularly in the case of osteoporosis where we’re talking about images that we know will be under-diagnosed in clinical practice, that’s the whole point. We know we’ve got diagnoses of people that, when those scans were taken, those fractures were missed. I think there are ethical risks on both sides of the equation there. So, ultimately the solution is probably a technological one.” Paul considers that such technological solutions could allow the patient to be identified by the clinical team if a previously undiagnosed condition is detected. This project involved an academic group from the University, clinical partners and also a commercial partner, Optasia Medical (https://www.optasiamedical. com/). Clinical partners are necessary for several reasons including access to the data and their clinical knowledge, specifically relating to what the intended solution should look like and its clinical utility. This partnership has helped to overcome certain issues, especially around the longer term support and maintenance of such solutions. Paul highlights that the kind of approach applied to developing open-­ source software in other domains doesn’t translate well into the clinical arena. In clinical medicine the solutions require specialised knowledge and expertise and regulatory approval as well as long-term maintenance of the software. Working with commercial companies can bring different expertise to such projects. They are often familiar with setting up and running customer support services that extend over time and have different funding models that can support such services. Companies can also assist in managing scaling of projects. Paul identifies that there is a significant difference between supporting a single hospital site and nationwide service, which could rapidly increase image processing demands from a few thousand images to millions, and he stresses the importance of sourcing expertise about high tech, rapid expansion types of companies from the people who work in that area and who are

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familiar with ways that you mitigate the kinds of commercial risks involved. Paul talks about the importance of a tight-knit, close team, who are all committed to getting the formal contracts complete and out of the way as soon as possible so the project can succeed. Paul talks about the value of leadership in place of management, with a mix of people with different skills all invested towards a single mutually understood goal. Working with medical charities (Royal Osteoporosis Society) and organisations (Bone Research Academy), especially for generating grant proposals, is useful, and carrying out public and patient involvement and consultation throughout the course of the project. The work carried out by the team in this area has already had a positive direct impact on patient outcomes. By working with Fracture Liaison Services (FLS), it has provided a direct route to feedback information about patients so they could be treated. The FLS model was pioneered in Glasgow (Scotland) and has emerged as the most effective service model for the management of osteoporosis (McLellan et al. 2003). Around 2000 people were reported to the FLS that otherwise would have remained undiagnosed, which potentially has saved the lives across the next 5 years of somewhere between 10 and 30 people who would have died from a hip fracture that now has been avoided by early diagnosis. One of the trials Paul’s team was involved with in London influenced an NHS Trust to put in place a system similar to cancer reporting, to ensure that all patients with fractures were referred to the bone health team—a permanent improvement in health services for around 360,000 people in that Trust’s catchment (Rajak et al. 2019). Paul has a background in physics and astrophysics. He taught himself C programming during his PhD and then undertook on-the-job training in computer vision when he started at Manchester, but received no real formal training in computer vision or data science. Paul recommends a solid foundation in statistics and maths for this type of work, otherwise: “It’s relatively easy to take a bunch of data, put it in your model and look at the response [...] it won’t be a particularly useful solution because you haven’t got any of the expertise to know what you are doing, how it is working, whether it’s working properly, whether there are particular things you could enforce invariances in to make it much more accurate. All of this is basically statistics… the science of analysing things where you’ve got a stochastic element, where there’s noise in the data.” Paul commented on the sort of training that might be of use to clinicians approaching this subject, considering the level of understanding and application required. Like Haroon, he cited the Topol Fellowships (NHS Health Education England 2019) as a good opportunity for selected clinicians to specialise in AI. For the majority though, given that they already have very extensive training, adding more requirements to this already considerable workload may lead to burnout (Dauphinee 2020). They need to be in a position, not necessarily where they can program AI systems or train a convolutional neural network to solve a problem, but where they have enough knowledge to be able to understand what those systems are doing, what they mean and how to use the results from them. Paul suggested that clinicians do not necessarily need to understand the intricate inner workings of ML processes in order to make effective use of their output. The key for clinicians is to

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understand and interpret the output with some understanding of how reliable it is. Clinicians must recognise the models’ limitations and vulnerabilities as well as their potential applications. Adversarial ML can be used to fool models, and show that sometimes just changing a few pixels in an image is enough to change the output of an ML model, even though such a change is imperceptible to a human viewing the image.

Conclusions and Recommendations The use of patient data either retrospectively or prospectively is central to the success of any ML-based clinical decision approach. Patients need to understand how their data might be used and why it might aid improvements in health and treatment for other patients, and patients must be consented appropriately. This is more complicated when it concerns the data of children and adolescents, and needs some careful public and patient involvement. Active dialogue and involvement with patients and the public can be facilitated by those who work in and with appropriate patient support and advocacy groups, such as the UK organisation “Understanding Patient Data” (Wellcome Trust Ltd. 2020). Largely, many patients are very positive with respect to wanting to share their data, but they want close regulation of how it will be used and by whom; perceptions that ML approaches might replace clinicians may be a negative factor, or alternatively may be welcomed by the public (Wong et al. 2019). Mistrust of ML approaches is not unique to patients—many clinicians and senior stakeholders in hospitals remain sceptical. Therefore clinicians, whilst not necessarily needing to become experts in the field, must be equipped with sufficient knowledge to rigorously question and appraise any potential approaches that are being considered in their hospital. Health AI experts need to assist their healthcare co-­ workers and colleagues to understand the richness of their data, its complexity and what is and is not achievable when considering ML approaches. The use of patient data to inform ML approaches in healthcare must be undertaken within ethical and governance guidelines and frameworks for research and practice; these are still developing and therefore not always fit-for-purpose. It is incumbent upon the collective community working in this area to actively define and refine appropriate uses of AI in healthcare, and work together closely with regulators to minimise risks and barriers. AI has not yet reached the point where it is capable of fully replicating human performance in complex data analysis tasks, so its clinical utility relies on incorporating AI into existing workflows, rather than replacing them. Human oversight is essential. Clinicians need to be able to interpret the results that algorithms produce. Those who develop algorithms must ensure that they are not “black boxes”. Data-driven ML projects require a collaborative team approach, garnering interdisciplinary skills and domain-specific knowledge. Team members must develop a culture of trust, be willing to develop themselves and others, and work towards

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developing a shared common language. A collaborative team working on this kind of project is likely to be most successful with the support of a senior champion or stakeholder, such as a Chief Clinical Information Officer, who is well-versed in these kinds of approaches and can act as an advocate in strategic planning and management meetings.

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Chapter 18

Working as a Health Information Manager Trixie Kemp, Lorraine Fernandes, Cameron Barnes, Deneice Marshall, Mandy Burns, Sabu Karakka Mandapam, Gemala Hatta, Oknam Kim, and Kerryn Butler-Henderson

T. Kemp (*) College of Health and Medicine, University of Tasmania, Burnie, TAS, Australia e-mail: [email protected] L. Fernandes Fernandes Healthcare Insights, Bigfork, MT, USA e-mail: [email protected] C. Barnes Cabrini Health, Malvern, VIC, Australia e-mail: [email protected] D. Marshall Barbados Community College, Bridgetown, St. Michael, Barbados e-mail: [email protected] M. Burns Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK e-mail: [email protected] S. K. Mandapam MCHP, Manipal Academy of Higher Education, Manipal, Karnataka, India e-mail: [email protected] G. Hatta University of Indonesia and Repati Indonesia University, Jakarta, Indonesia O. Kim Sungkyunkwan University, Suwon, Republic of Korea K. Butler-Henderson Digital Health Hub, College of STEM, RMIT University, Bundoora, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_18

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Abstract  Health Information Management (HIM) professionals have knowledge and skills in health record management, clinical coding and classification, information management, human resource management, and healthcare processes. HIM professionals take responsible roles in data integrity, information governance, and health informatics, at the same time as they continue to modernise the fundamentals of HIM. This occupation has a range of options that allow people to work within a health-related field while not being on the frontline providing direct care. There are opportunities to work in healthcare, academia, research, health IT, and anywhere that uses health data, across the globe. Case studies from leaders in the field tell stories of personal growth, education, camaraderie, and contributions to health systems that impact lives globally. Keywords  Case study · Health information management · Clinical documentation · Information governance · Data governance

Introduction The work of documentation to support the safe delivery of health care has evolved over thousands of years (Kemp et  al. 2021). Today, the Health Information Management (HIM) professional “plans, develops, implements and manages health information services, such as patient information systems, and clinical and administrative data, to meet the medical, legal, ethical and administrative requirements of health care delivery” (HIMAA 2016). HIM professionals may work in any organisation which requires the creation, management, governance, analysis, or destruction of health information. They may manage the paper health record, develop and implement the electronic health record, classify and analyse health information, or protect and safeguard health data entrusted to the organisation. Further, they may implement and manage the technology to collect health data, and distil, analyse, and report health information to inform health decisions. Professionalisation of this work has increased over time. Positions of medical record librarian were created in hospitals (Watson 2013) and in the first half of the twentieth century professional bodies formed in the USA (AHIMA 2020), Canada (CHIMA 2019), UK (Abdelhak 2016), and Australia (HIMAA 2016). Formal vocational qualifications started to emerge around the same time. The International Federation of Health Record Organisations (IFHRO) was formed in 1968 (IFHIMA 2020) and established official relations with the World Health Organization in 1970. As their roles evolved to have greater responsibility, medical record librarians became medical record administrators (~1970s) then health information managers (~1990s). Bachelor qualifications emerged in the 1980s in countries where the role had become a recognised profession. In 1987 HIM was included in the International Classification of Occupations. IFHRO changed its name to the International Federation of Health Information Management Associations (IFHIMA) in 2010. The following case

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studies highlight the diversity of HIM roles across all health system areas, and the importance of the HIM professional in supporting safe health delivery and management.

Lorraine Fernandes, Fernandes Healthcare Insights, USA My career has been a wandering path of education and experience in different roles as I have worked in many aspects of the health industry, both domestically and internationally. I started as a medical record clerk in a 20-bed hospital in rural South Dakota, in the middle of the USA. My formal health information education has been at the technical and baccalaureate levels. During the first decade of my career, I worked in teaching hospitals and small community hospitals. I managed 10–50 staff in areas including traditional HIM departments, trauma and cancer registries, and ancillary departments including risk management, discharge planning, and utilisation review. These management roles prepared me for opportunities in large and small technology companies where I served as a healthcare subject matter expert developing or executing corporate strategy, or directly interacting with customers. Additionally, at various points in my career, I have worked as a management consultant, and more recently as a data governance consultant. At the beginning of my career, I did not appreciate the importance of the two fields I would cross: health information management and health informatics. But I have learned the value of data, from managing clinical registries such as trauma and cancer and being responsible for reporting data to meet local, state, and federal requirements. I witnessed the shortcomings when data in the applications and systems could not easily be exported and used or understood for broader purposes. I have become a believer in the critical need for accurate, trusted data. This need creates many roles, such as privacy or compliance officer, data steward, data analyst, for HIM professionals. A couple of years ago, I read The Fourth Industrial Revolution by Klaus Schwab (2016), which discussed the need for a multi-stakeholder approach in using data and new technologies and advocated for a comprehensive, global view of how technology is reshaping our world economically, socially, and culturally. HIM professionals can play leadership roles in the new multi-stakeholder era as organisations define their information and data governance practices. Schwab (2016) summarised the tension between the past and the future, which the HIM profession certainly sees: “decision-makers are too often caught in traditional, linear (and non-disruptive) thinking or are too absorbed by immediate concerns to think strategically about the forces of disruption and innovation shaping our future”. Throughout my career volunteerism has played a crucial role in expanding my personal and professional horizons. I have enjoyed roles as Board member and President of the California Health Information Association and served on national

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workgroups and committees. Since 2013 I have served IFHIMA through being the Editor of Global News, website coordinator, Board member, and President 2019–2022.

Cameron Barnes, Cabrini Health, Australia Despite a privileged school setting, there was not an abundance of career advice in my final year of secondary school. My application for several courses failed leaving my only option for tertiary education, a humble number five on my wish list, a Bachelor of Applied Science in Medical Record Administration. My mother had suggested it as the course prerequisites included biology, typing 60 words per minute (thankfully not required), and only the 11th form maths I had completed the previous year. I commenced the course in 1986 and promptly failed most of the first-year subjects; I was forced to present myself to the “Committee to Review Unsatisfactory Progress” and plead my case to continue. Thankfully, they recognised my unfulfilled potential and allowed me to complete my first year. I subsequently passed the remaining 2 years and was set to embrace my Medical Record Administrator’s career, after a 9-month break to explore Western Europe. On return to Australia, I applied for a position at St Vincent’s Public Hospital in Melbourne. I was 23, with no experience but of course pretty sure that I knew everything I needed to know about managing 15 staff all older than me, acute coding, the Freedom of Information Act, submitting data to the Department of Health and many other duties. I stumbled through my first few years with help from my manager, peers, and the staff themselves who took pity on me as a young and foolish boy whose heart was in the right place. It was an excellent grounding, and I learnt a lot in 7 years at St Vincent’s, knowledge that I still refer to today. I became involved in our professional association (now the Health Information Management Association of Australia) at this time, an involvement which I continue 30 years later. During my tenure at St Vincent’s two co-workers and I established an independent coding contracting business that kept 15 casual staff busy, and us busy too most weekends for a couple of years. Our increasing success and workload eventually meant we had to either move into this full time or sell up. We sold up. After becoming Deputy Manager at St Vincent’s, I was approached to do a role at The Royal Children’s and Women’s Hospitals, managing 70 staff and a budget that made my eyes water. I accepted this role with glee and subsequently failed impressively in the first 12 months. Working across two big campuses meant I was never in the place I needed to be, and the staff were not impressed. The decision was made to base me solely at “The Children’s”, and I set about my department manager tasks. This role included more strategic work, interaction with executive, planning the building of a brand-new hospital and department, a patient administration system implementation, and planning for a scanned medical record solution. I also went to Vietnam for a period as an advisor for Royal Children’s Hospital International. During this time, I completed my Masters in Health Administration,

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and I did surprisingly well because the study seemed so relevant to my day-to-­ day work. I worked at ‘The Children’s’ for 12 years until I was approached by Cabrini Hospital, a private hospital with around 700 beds and five sites, to take on a new role that included a departmental cultural transformation. I am now the Director, Health Information Services and Information Governance. My position’s information governance aspect came about due to the increasing realisation of the need for good quality data to inform business decision making. It is a perfect role for a HIM professional as we generally have an eye for detail, like to solve problems, and understand the interface between administrative and clinical work. We are also used to data reporting and know that “data” is not always “information”. During my 10 years at Cabrini, I have also instigated some ground-breaking projects including secure messaging, data integrity software, and e-referrals. My ongoing interest and desire to “be part of the solution” meant I became involved with the IFHRO (now IFHIMA) as the Australian representative. I also presented papers internationally. I have been able to travel extensively nationally and internationally as part of my profession and make friends and colleagues throughout the world. As an elder statesman of the profession now, I still love to solve problems, make work more efficient, and mentor HIM staff.

Deneice Marshall, Barbados Community College, Barbados My career in HIM started 17 years ago. I never imagined I would have the opportunity to serve my country, the region, and my profession as IFHIMA Director for the Americas 2019–2022, or become the Founding President of the Barbados Health Information Management Association. Further, I could not have envisioned being appointed HIM Coordinator at my country’s only community college. In this role, I administer and provide leadership in the day-to-day operations of the HIM education programme. My responsibilities also include developing, implementing, and delivering an updated HIM curriculum. Besides my educational responsibilities, I have conducted HIM consultancies as a Pan American Health Organization (PAHO) Temporary Advisor for several regional Ministries of Health in the Caribbean. My unpredictable journey started in 2003 at the Barbados Community College. Initially, I wanted to pursue a degree in environmental sciences; however, the programme was fully subscribed. The alternative options were minimal, and HIM was the only option that intrigued me at the time; little did I know that it would become my profession and my passion today. At that time, the HIM curriculum combined the disciplines of medical records management, basic information technology, health care, and the traditional principles and practices of acquiring, analysing, and protecting health information. Upon completing the programme’s theoretical components, I embarked on a 3-month directed practice training period at the local hospital, a polyclinic, and a health insurance company. This work-based learning allowed me to connect the dots, to

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acquire a genuine appreciation and understanding for the concepts taught and the importance of the HIM profession. Many people equated this profession with the simple filing of records; however, my academic programme and directed practice exposed me to a much broader and more detailed view. After completing my associate degree, my first job was as a medical records clerk at the local hospital for a short period; I worked primarily in the appointment and library section. I subsequently accepted a job offer from the insurance company where I had completed a part of my directed practice, working as a healthcare administrative assistant. My duties included researching and managing health claims inquiries, advising customers on claim-filing procedures and the status of health claims cases and insurance policies, coding clients’ claims using International Classification of Disease version 9 Clinical Modification (ICD-9-CM) and Current Procedural Terminology (CPT) and adjusting clients’ requests for reimbursement. I began to think more deeply about how my profession impacted the community’s life and health. I started to reflect on how a lack of knowledge about the value added by my chosen profession could stifle its growth. Moreover, I became curious about how other parts of the world were using HIM to better manage patient care. Further study and professional development were warranted. My HIM education continued at Tennessee State University, where I completed a Bachelor of Science in HIM with a minor in General Business in 2009. I subsequently earned my American Health Information Management Association (AHIMA) Registered Health Information Administrator certification in 2010. While studying in the United States, I capitalised on opportunities to work at both acute and long-term care settings as a HIM Consultant. I worked primarily on clinical coding projects, Joint Commission accreditation, and quality improvement initiatives. My research and exposure to international conferences and other forums showed me a whole new world. The use of health data and new technologies to manage patient information was more extensive, with closer links acknowledged between HIM and patient care outcomes. Inexorably, my educational journey continued; in 2014, I completed my joint Master of Science from the College of St Scholastica in Duluth, Minnesota, in HIM and Information Technology Leadership. My published research thesis focused on HIM and information technology education challenges in developing countries (Marshall 2014). In 1978 the Associate of Applied Science degree in HIM programme formerly known as the Medical Records Technology programme was established at the Barbados Community College. This programme has evolved to integrate health information technology, current trends in HIM, and various continuing education courses in health informatics, coding, and classification of health data (Barbados Community College 2020). The Medical Records Management/HIM profession in Barbados has a cadre of trained HIM professionals working across various health institutions locally, regionally, and internationally. Employment opportunities on the island are mainly at acute, ambulatory, and behavioural health facilities in the public and private sectors. However, on the island, there is still a lack of awareness of the value of the HIM professional, or of the importance of their roles and responsibilities, especially in regard to the integration of information communication and

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technology (ICT) and electronic health records in health care delivery. We are still recognised by traditional role names such as chief medical records officer, medical records officer, medical records supervisor, and medical records clerk, designations that are no longer aligned with global benchmarks and tend to undervalue the work. The Barbados Health Information Management Association is working to bring awareness and enact reform of the HIM profession.

Mandy Burns, Manchester University, UK My education journey to becoming a HIM professional included Institute of Health Records and Information Management (IHRIM) Foundation Qualification, IHRIM Certificate Qualification, IHRIM Diploma Qualification, IHRIM Certificate of Technical Competence Assessor Certificate, Certificate in Post 16 Education, and a variety of training and courses including a National Vocation Qualification (NVQ) through the City and Guilds of London Institute, local courses such as anatomy and physiology, along with attending numerous conferences and congresses. I started my career as a secretary for a therapy service in a small district general hospital, taking minutes, organising appointments, typing, and other administration work. I then moved to work at a large teaching hospital as a secretary/reception manager with the district therapy services; while there I started training therapists and administrative staff on the hospital patient administration system (PAS). This opportunity provided me with the knowledge for my next role as the Training Coordinator, Medical Records, training new staff on all areas within the department including library, reception, booking, referrals, templates, call centre, bed bureau, clinical coding, ward clerks, secretaries, waiting list, and preoperative staff. I delivered the mandatory training and undertook the NVQ training and assessments. I progressed to a large teaching hospital as Medical Records Manager, where I managed staff across two sites delivering all the services just listed. I also became Master Trainer for the newly implemented PAS; I provided train the trainer sessions and direct teaching sessions. My responsibilities included coordinating the clinic template and outpatient appointment migration across systems and staffing the Go-Live helpline for a month. I had key responsibility for sections of the Information Governance Toolkit, which is mandatory for all National Health Service organisations to complete and reach acceptable levels. My next role was Admin, Records, Child Health, Data Quality, and Information Governance Manager for a Community Trust. I was responsible for supporting all community services within these functions over a large and diverse population. No services had previously existed for records or data quality in this organisation. I established and delivered them, ensuring compliance with all legal requirements. Here my responsibilities also included vital sections of the Information Governance Toolkit. The Trust went through various mergers and transformations as part of National Transforming Community Services work, which involved many changes in records, information governance, and data quality.

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Next I went to a Paediatric Hospital as Patient Services Manager, with responsibility for records, transcription, booking, admissions, and reception and becoming lead for Outpatient Global Digital Exemplar (GDE) work. I made decisions and informed direction for the organisation as it moved towards the Healthcare Information and Management Systems Society (HIMSS) Level 7. I am currently the Informatics Head of Patient Services for the second largest NHS Trust in England, where I manage records and coding services. I work with both teams to support daily management and the organisation’s single electronic patient record programme, i.e. merging nine hospital systems and records. During COVID, I worked alongside the Director of Technology in establishing the local Nightingale Hospital. I was responsible for establishing and agreeing on the documentation requirements for the unit and all administration services. In addition to my employment, I have been the CEO of IHIRM for the last 6 years and served on the Board of the Federation for Informatics Professionals (FEDIP). I also set and mark papers within IHRIM’s Informatics suite of exams and am part of the Education Board for the Institute. I am the United Kingdom IFHIMA representative.

 abu Karakka Mandapam, Manipal Academy of Higher S Education, India The HIM profession’s prospects in India were not clear when I joined the Medical Documentation Master’s programme in 1996. The curriculum and training mainly focused on medical records and management (MRM) in a traditional hospital setting. My professional career started in 2000 as an Administrator—Medical Records in a private medical college hospital. The role’s primary responsibilities were to assemble a multitude of patient clinical information for various administrative decisions at the Medical Director level. It was a great learning experience, as my activities were different from my education and training. In 2000, I joined as a lecturer with my current organisation and started teaching medical terminology, disease classification systems, and basic MRM subjects to Master students. In 2003, I was entrusted with the responsibility of managing the department. I have utilised the initial period of my career to strengthen my HIM academic competencies mainly through the self-learning. In 2006, I earned the first Doctor of Philosophy degree awarded to a HIM professional in India. My progression to Associate Professor and Head of the department has allowed me to persuade the university to establish the first full-fledged HIM programme of global curriculum standards in India. As a senior faculty member and administrator, my key responsibilities included teaching, guiding student projects, research activities, streamlining curriculum, creating awareness about the HIM profession, exploring international collaborations, and strengthening the academic processes as per regulatory requirements. During this phase, the department built up the value of

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HIM professionals among many corporate hospitals through guided student projects, which increased demand for HIM trainees in the market. I also initiated the preliminary work for a Master level Health Informatics programme during this period. In 2012, I was promoted to Professor of HIM.  I was assigned additional administrative responsibility as Associate Dean of the institution to oversee several health professional departments’ academic and non-academic functions. My exposure to many national and international conferences worldwide has enabled me to learn and integrate evolving HIM concepts in my profession. My first ever participation in an IFHIMA international congress in Canada in 2013 gave me a professional breakthrough by establishing connections with many international experts from academia, industry, and professional bodies. This network helped me associate with several global councils and continued collaboration with various HIM professional bodies worldwide to strengthen the HIM workforce and training competencies. As a member of a national taskforce formed by the Government of India, I have influenced the national committee to gain approval for a model curriculum for HIM education and HIM as a professional title for the first time in India. More institutions and universities must start formal education and training programmes in the HIM domain, as the existing volume of HIM graduates is insufficient to meet the demand for qualified and trained HIM professionals. India needs a large workforce of trained HIM professionals to efficiently integrate newer technologies and manage health information at multiple levels in its evolving healthcare system.

 emala Hatta, University of Indonesia and Repati G Indonesia University Starting in 1976, upon my graduation from Medical Record Administration (MRA) School in Sydney, I joined the Ministry of Health (MOH) of Indonesia. A new chapter of progress towards a medical record system had commenced. In 1978 the Minister of Health signed the first organogram for Indonesia’s hospitals (SK 134/1978), known as MOH Decree 134/1978. For the first time, a medical records section in the hospital had a place, i.e. under the secretariat. This was a critical recognition and the beginning of medical records in Indonesia’s hospital organisation. In 1977 I asked MOH’s Directorate General of Disease Prevention and Control to design medical record systems and MRA short training courses for the MoH in Indonesia. From 1980 to 1989 Jakarta Municipality Health Department established a Medical Record Working Group Committee which I chaired. We published the first medical record serial publication (MR News) and distributed copies of 29 quarterly editions to 100 hospitals. We also delivered a regular 1-day training session per month and provided medical record training courses for 2 months for each group of trainees (once or twice a year), training more than a thousand hospital medical record staff from many provinces this way. In 1989 the first MRA school’s 3-year

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programme was opened; I designed the curriculum based on the MRA School in Sydney but with some local adjustments. Even though there has been significant work undertaken to progress medical record administration towards the HIM era in Indonesia, even with 70 schools now teaching HIM, there is still a way to go. We need to supply a HIM workforce for a country covering three time zones with more than 2000 hospitals and 17,000 health centres that provide services to 267 million people across 17,000 islands. By building on lessons learned from other nations, and by involving myself in numerous public policy and education initiatives related to professional practice, I have been able to help Indonesia step towards excellence.

Oknam Kim, Sungkyunkwan University, Republic of Korea My HIM career began in 1976 at Yonsei University Severance Hospital in Seoul after completing the Severance Hospital Institution Registered Record Administration (RRA) Training Course. The concept of medical record management began at Severance Hospital in 1962 when a Canadian missionary established unit numbering, disease classification, and a patient index system. Also, Severance Hospital had established and operated the only specialised curriculum for medical record librarians (Severance Hospital Institution RRA Training Course) since 1965 and produced about two or three RRAs every year. Fewer than 20 RRAs were employed in the medical record department when I completed my training. In 1977, the Korea Medical Records Association (KMRA) was approved by the Ministry of Health and Welfare as a legal entity—in 2018, the association’s name was changed to the Korea Health Information Management Association (KHIMA). From 1978 to 1983, KMRA operated a private qualification screening course in medical record libraries for Certification as Accredited Record Technician (ART) and Registered Records Administrator (RRA). Despite my short career, I had the opportunity to participate as an educator, teaching ART trainees medical terminology, disease classification, and hospital statistics every weekend for several years. RRAs who graduated from Severance Training Institution voluntarily participated in KRMA-led ART education and led the curriculum to organise medical record management as a speciality. From the beginning, KMRA had promoted the legislation of a medical record librarian licence system as a top priority, and I devoted myself to this project as a lead member. A review of the Medical Service Technologies, etc., Act in 1983 led to the establishment of the Medical Record Librarian Licence System. With the introduction of the national licence system, regular HIM curriculum for colleges and universities was opened to enhance professionalism, and record librarians were included in the government-managed medical personnel. There was a shortage of professional education about medical information management theory and practice, so I worked as an administrator of a medical record department at a hospital and continued to teach medical information management at a university. To meet university faculty requirements, I obtained master’s and doctoral degrees in

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health information management. Now I have worked as an administrator in several university hospital medical records departments for 30 years and taught health information management at colleges and universities. My life as a HIM is made meaningful by volunteer activities. From the beginning of KMRA/KHIMA, I participated as an executive member prominent in advocating for the medical record librarian license system. I served as President and the Director of the Research Institute; most memorably as KMRA President I organised the 15th IFHRO Assembly in Seoul in May 2007. Currently I am working as a member of the Korean National Standards Committee for the International Organization for Standardization’s Technical Committee on health informatics (ISO/TC 215). Korea urgently needs to establish standards for interoperability of health information; therefore, I plan to contribute to the development of health information interoperability standards during the rest of my lifelong HIM activities.

Conclusion These case studies have shown similarities and differences in the HIM profession across the world. Each demonstrates the personal growth of the individuals from entry-level positions, working their way up to leadership positions in a range of roles that demonstrate the profession’s breadth of knowledge and skill. Their careers include managing health information services in health care facilities, consultancy, projects, legislation and standards development, education curriculum, and working directly with the professional associations that represent HIM. Each person featured in this chapter received formal education at the start of their career, but that was only the start of their lifelong learning and growth in the field. Many have taught or mentored other HIM professionals and established education pathways for HIM students and graduates. Each of these HIM leaders embraced opportunities to further their skills and knowledge, with several working towards or completing a PhD. Some of these careers have unfolded under strict regulation and support of the HIM profession; others occurred while working towards recognition as a HIM professional, rather than as a medical records clerk or administrator of the past. A few of the case studies show HIM professionals in senior information governance roles. They provide examples of work in developing strategy and standards to manage health information. There is also recognition of the synergies among health information management and health informatics and data management and high-­ quality information for decision making. Amid rapid growth of data with the implementation and expansion of digital health, the HIM workforce is well placed due to their qualifications and training (Butler-Henderson 2017). Challenges for the HIM profession have been discussed also. The domain is relatively unknown in some countries. There is difficulty in keeping up with the demand for a qualified HIM workforce to support the needs of millions of people across various geographic landscapes. Each country has its own professional education

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and regulation system, with varying degrees of engagement or support from governing bodies. The introduction of the International Classification of Diseases 11th edition (ICD-11) poses the big imminent challenge of educating future HIMs and preparing the existing workforce to transition from existing versions and modifications of ICD (Fenton et al. 2017). But the most significant point to emerge from these cases is the passion and commitment of HIM professionals to improve citizens’ lives, make healthcare safe, and drive the growth of HIM as a profession.

References Abdelhak MH.  Health information: management of a strategy resource 5th edition. Elsevier Saunders: Missouri; 2016. AHIMA. AHIMA who we are web. AHIMA; 2020. https://www.ahima.org/who-­we-­are/about-­us/ history/. Barbados Community College. Overview of health information management web. Barbados Community College; 2020. http://www.bcc.edu.bb/Divisions/HealthSciences/ HealthInformationManagement.aspx. Butler-Henderson K. Health information management 2025: ‘What is required to create a sustainable profession in the face of digital transformation?’. Launceston: University of Tasmania; 2017. Contract No.: ISBN 978-1-82695-88-3. CHIMA.  The history of the Canadian Health Information Management Association. Canadian Health Information Management Association; 2019. https://www.echima.ca/uploaded/pdf/ CHIMA_History.pdf. Accessed 28 Jun 2019. Fenton SH, Low S, Abrams KJ, Butler-Henderson K. Health information management: changing with time. Yearb Med Inform. 2017;26(1):72–7. HIMAA. About HIMAA. Health Information Management Association of Australia; 2016. http:// www.himaa2.org.au/index.php?q=node/41. HIMAA. About HIMAA. Health Information Management Association of Australia; 2016. http:// www.himaa2.org.au/index.php?q=node/41. IFHIMA.  IFHIMA about us web. International Federation of Health Information Management Associations; 2020. https://ifhima.org/about-­us-­2/. Kemp T, Butler-Henderson K, Allen P, Ayton JE. Evolution of the health record as a communication tool to support patient safety. In: Chisita C, Enakrire R, Durodolu O, Tsabedze V, editors. Handbook of research on records and information management strategies for enhanced knowledge coordination. Hershey, PA: IGI Global; 2021. p. 127–155. Marshall DL. A guide for restructuring and transforming health information management education in Barbados and other developing countries [M.S.]. Ann Arbor: The College of St. Scholastica; 2014. Schwab K. The fourth industrial revolution. Geneva: World Economic Forum; 2016. Watson PJ. The first fifty years 1949-1999 Medical Record Librarian to Health Information Manager. Kingswood, Australia: Health Information Management Association of Australia; 2013.

Chapter 19

Working as a Health Librarian Ann Ritchie, Sarah Hayman, Aoife Lawton, Gemma Siemensma, Helen Baxter, Meena Gupta, and Blair Kelly

Abstract  Health librarians were one of the first health information specialist groups to organise as a professional association, dating back to the late nineteenth century and the early days of organising the medical literature. The late twentieth century saw the introduction of many digital health information products and services and the transformation of health librarianship’s models of service delivery. Six case studies in this chapter apply a practitioner’s lens to the transition that has taken place over the last 30 years. They highlight the impact of several major influences, in particular, evidence-based medicine, health literacy and digital health or e-health. Their careers illustrate a range of educational pathways to becoming a health librarian and the variety of roles and settings in which they work. Their motivations provide A. Ritchie (*) Independent Consultant, Melbourne, VIC, Australia S. Hayman Barwon Health, Geelong, VIC, Australia A. Lawton National Health Service, Dublin, Ireland e-mail: [email protected] G. Siemensma Ballarat Health Services, Ballarat, VIC, Australia e-mail: [email protected] H. Baxter Austin Health, Melbourne, VIC, Australia M. Gupta Australian Catholic University, Melbourne, VIC, Australia e-mail: [email protected] B. Kelly Deakin University, Geelong, VIC, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_19

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insights into an evolving future for this professional group. The conclusion summarises the impact of health librarians in terms of their organisational roles in knowledge management and information governance, and their value to society in advancing equitable access to high-quality evidence-based information. Keywords  Case study · Evidence-based healthcare · Governance · Health librarians · Health literacy

Introduction In responding to the question “what is a health librarian?” it is tempting to reply: “any librarian who works in a health organisation”. But as with any simple answer to a complex question, this does not tell the whole story; it belies the intricacies, specialist nature, knowledge and advanced skill set required to practise competently as a health librarian (Ritchie 2020), and may, in fact, be misleading. Health library and information specialists work in various types of organisations and a number work outside of traditional libraries (Kammermann 2016). Anecdotal evidence suggests that there may be a growing number of health librarians working online and remotely, and a couple of the case studies in this chapter illustrate this tendency towards remote work. Most health librarians do, however, work in hospitals, community care and social care organisations, meeting the needs of clinicians who deliver direct care and services to individual patients and defined communities (Kammermann 2016). Others are located in government health departments and agencies that operate at population and policy levels; and still, another group is located in academic health organisations, including universities and institutes that have teaching and research remits. Some health librarians work in specialist health information roles in commercial organisations, such as pharmaceutical, biomedical and publishing companies. The common characteristic for all the health librarians who work in these various types of organisations is the set of competencies that define the scope of their professional practice (ALIA HLA 2018). Depending on their organisational context and the focus of their roles (generally reflected in their position title), they may specialise in particular areas. The six case studies in this chapter sample the different organisational contexts and various types of health librarian roles. Before delving into the case studies, however, it is instructive to give some perspective on the development of this relatively small but dynamic profession by examining briefly some of the main factors that have influenced the development of health librarianship in the recent past. As with all service professions, health librarianship has been exposed to the disruptive influence of digital technologies that have transformed many traditional models of in-person service delivery. The past three decades have witnessed the evolution of health library and information services from largely physical, face-to-face interactions and print-based transactions, to

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online, remote and electronic modes of delivery. The US Medical Library Association was established in 1898 at the time when Index Medicus, the forerunner of the US National Library of Medicine’s PubMed database, was the ever-growing, bulky and voluminous, paper-based tool used for finding the evidence located in the published literature. In the transition to the digital health world, health librarians have proved to be responsive, adaptable and collaborative, often early adopters and experimenters with new technologies. They have transferred the principles of their professional practice in information and knowledge management to the digital products and services of the e-health world. For example, health librarians no longer deal with just books and journals—there is an ever-expanding range of electronic resource formats, products and bundles of offerings from publishers. Developing library collections is no longer as simple as buying a resource once from a known publisher, owning it forever, lending it to whomever you choose—there are intricate permutations on licencing and purchasing models for different conditions and rights of access, available from an array of suppliers. Working collaboratively across departments, professional groups and organisations, health librarians have used digital technologies to deliver services beyond the boundaries of a physical library; for example, online and real-time reference and research consultations with national and international reach; embedded librarians (also known as “informationists”) working in multidisciplinary clinical, research and teaching teams. Health librarians have been on the front foot in creating innovative digital solutions to problems stemming from the limitations imposed by print-based, physical service delivery models. They realised quickly the advantages of electronic technologies for improving equity of access, making information available regardless of time and place. At the same time, health librarians recognised that the digital divide has exacerbated the “information rich/information poor” divergence. They have driven digital literacy and information literacy training programs that have reached out to health consumers, as well as to practicing and trainee medical, nursing and allied health professionals. Health librarians have been champions of the evidence-based practice (EBP) model of clinical decision-making. In this model, the current best available evidence (most often supplied through the specialised health reference services of librarians) is combined with health professionals’ expertise, knowledge and understanding of their individual patients, and the values, preferences and informed consent of the patients themselves. Health librarians very quickly recognised the connection between EBP, consumer health literacy and patients’ ability to find, understand and apply good quality information in their choices about their treatment (Schardt 2011). Hospital librarians have taken their support for EBP and health literacy one step further through their assistance in developing and implementing organisational literacy strategies. Recent research shows how their consumer health literacy initiatives support hospital accreditation requirements and objectives—patient engagement, health literacy and patient empowerment—and thus patients’ ability to give their truly informed consent (Ritchie et al. 2020). All has not run smoothly for health librarians in the transition to digital models of delivering health library services. One of the biggest risks in moving to a digital

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world has been the problem of invisibility of both the information resources and the health librarians who are delivering the services. While the aim has been to create seamless and joined-up services, the unintended consequence has often been that the professional expertise that makes it all happen has been taken for granted. Digital health means more than simply delivering health services online. The clinicians delivering consultations are the face of telemedicine, but there are many health information professionals behind the technology—collectively they are the human infrastructure who bring it all together. The ability of clinical health professionals and health policy-makers to apply the principles of good governance and evidence-­ based decision-making is dependent on the specialist health librarians who manage the information that is the stuff of decision-making. They ensure that the right information (the best available evidence) is delivered in the right place (whether physical or online), at the right time (at point-of-need), to the right person, so that evidence-­ based decisions can be made. This chapter contains six case studies of health librarians working in three sectors—hospitals, universities and government health departments. They illustrate some of the challenges they have encountered along the way and touch on their ideas and visions for the future of health librarians in the digital health world.

Sarah Hayman, Barwon Health, Australia I gained a Bachelor of Arts from The University of Adelaide with majors in English, Old and Middle English and Philosophy in 1977, and a Graduate Diploma of Librarianship from the University of New South Wales (1979). I have worked almost always in special (research) libraries, in education and health. My first job was running a small research library for the Australian Wool Corporation. I followed that with 2 years in the central cataloguing department of NSW TAFE, where I received a thorough grounding in cataloguing rules; it gave me an excellent grasp of the principles of structured information, which I believe to be essential for understanding information retrieval. My main job after that was for the National Centre for Vocational Education Research, where I managed a team responsible for producing the then VOCED research database. We moved from a disc-based and physically mailed-out database to the excitement of developing a web-based bibliographic research database. It was one of the earliest to be linked to an online thesaurus, and we achieved UNESCO endorsement for the database as a vital underpinning for research in that field worldwide. This job instilled my passion for excellence and for meeting the needs of information users; our work supported and enabled research, practice and policy-making, with the ultimate beneficiaries being those engaged in vocational education, and especially the students. (Now, in health, I see a similar ultimate aim of meeting the needs of the patients.) After a brief stint in health libraries, I moved to education.au, a leading government-­funded education technology company. Here I built on my more formal understanding of information provision to incorporate growing concepts of the

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opening up of user access and user evaluation via web 2.0 philosophies and mechanisms such as crowd-sourcing and user tagging. It gave me a new understanding of different ways of determining quality while maintaining the need to support practice with evidence of the highest quality. I had the opportunity to write and publish papers and present at conferences. Still, my most cited paper is one on taxonomy-­ directed folksonomies—how to maximise benefit from user knowledge and formal taxonomies combined. Through these jobs I was developing my passion for supporting practice and research with evidence, using my own technical knowledge, experience and understanding of user needs and approaches. I then had an opportunity to move back into the health world with a position in the CareSearch palliative care project, at Flinders University, to work in the highly specialised and fascinating field of search filter development. I loved the mix of technical and intellectual challenges along with the chance it gave to me to develop my own searching skills and to support work in the valuable field of palliative care. Working in an academic environment gave me the opportunity to write and publish in peer-reviewed journals. In 2017 I moved to my current job at Barwon Health where I am a research librarian, undertaking literature searches for hospital staff— for research, clinical and protocol development purposes—and I do the entire job remotely from my home in Adelaide! Meanwhile, alongside my formal employment, I always took every opportunity for involvement in wider professional work through committees, conferences, working groups, etc. This can seem tedious at times but I have met some wonderful colleagues and learned a huge amount from them. Indirectly it is through these contacts that I found my way into my present role. In this role, I search a range of health and other databases for evidence that answers questions that come from clinical, research and administrative staff. These questions are hugely varied and I get excited each time I open a new one—I love the technical and even sometimes creative challenge of finding the best possible information, of being as sure as I can that I have not missed any vital reference, and of fitting what I find to what the user wants. They may want a comprehensive search (recently I have done searches for five systematic reviews) or they may want only a few key recent references, and there is an art to selecting those. I also love the feeling of learning new information each time I do a search; I often do not retain it beyond the end of the search, and I certainly could not begin to work as a clinician, but I find health knowledge fascinating. Most importantly, I love knowing that I am part of the cohort of people contributing to health outcomes for patients. Health is an area that touches every single person, and it is a privilege to work in it. The remote nature of the work I do is less remarkable than formerly, after the time of the COVID-19 lockdowns. In this period the world has woken up to the importance of having the highest quality medical evidence delivered quickly, which is the heart of our job as health librarians. Librarians do need to adapt and respond to the changing world, as we always have done. Our role is one of navigating information and facilitating user access to it. In the health world, this means customising what we do to fit the needs of our users. We can provide tools and training for those who want to do their own searches, for those who want all the searches and

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acquisition of the information done for them, and for those at all points in between. We fit in that space between the users and the information and we can teach them how to find, identify and evaluate evidence. I believe we need to say “yes” to all opportunities to be involved in the transition to digital health. This may mean that we are moving further into research roles, learning more about technological approaches and embedding ourselves into research and clinical teams. I think we may be required in future to go beyond basic literature searches to undertaking more synthesis of evidence—and this may require the development of different more research-aligned skills. I have always found health librarians to be extraordinarily passionate, skilled and dedicated and I believe we are well capable of moving into these areas.

 oife Lawton, National Health Service Executive A Librarian, Ireland My personal journey to become the first national health service librarian in Ireland was a mix of serendipity and silent determination. I am not sure that anyone answers the question “what would you like to be when you grow up?” with the response “a librarian”! It is a path that I consciously chose at a certain point in my life when it seemed like a job I would like to try out. I did not know much about it at the time and ended up enjoying it. My education is a primary honour’s degree in European Studies from Trinity College Dublin which places an emphasis on languages, culture, politics and the history of ideas. It was a good grounding in critical thinking skills which translate well to the work environment regardless of the profession. I was offered my first job with the IBM corporation, after my final exams, at the same the time as I was offered a place on a Master of Arts course in journalism. I came to an early career crossroad with an important decision to make; regular income held a lot of appeal as a recent graduate so I accepted the job with IBM and reasoned that I could always return to journalism at a later stage. I was thrust into a world of computers and corporate culture at IBM which gave me solid training and foundational knowledge of hardware, software, teamwork and customer service which benefits me to this day. In a conversation with a work colleague about the future and where we saw our careers going, she suggested to me that I might like to pursue a career in librarianship. She had worked in an architectural library and it had been her happiest job with the greatest job satisfaction. I got a place on a Higher Diploma in Library and Information Studies at University College Dublin (UCD) and gained experience in a public library as a library assistant. Fast forward a few years, I gained international experience with a library management system supplier and got a Master’s in Library and Information Studies from UCD while working full-time. I held other roles including a brief period as a sole trader selling school library management systems, as a librarian in a specialist nursing library, and eventually as a systems librarian with the National Health Service. Despite other opportunities, I held this job for

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almost 14 years. Working in the health service is a great way to keep your work interesting and your skills up-to-date because it is a challenging environment and an area where information is continually changing and needed. I got the job of National Health Service librarian at the end of 2016, a position I hold today. This role is brand new and with no predecessors, I get to walk in my own shoes, set out a new path, inspire others to hopefully follow it and lay foundations for librarians and paraprofessionals for a solid future in the health service. This position inspires me because I see the difference that library staff makes to patient care and the potential that we have as an integrated service going forward. This is a leadership role and it requires strategic planning, positioning and management of resources and people. There is a staff of 53 with a mix of librarians, library assistants, senior library assistants and library managers; they are geographically dispersed with approximately 30 libraries located primarily in hospitals throughout the country. This separation has meant that virtual teams and digital communication are the primary way of working, with their own unique challenges. I set the direction of travel for the library service and work at positioning the library in a busy and changing organisation so that it is responsive to the needs of the health workforce and its service is aligned with the strategic objectives and values of the organisation. Ensuring that the library team has adequate and suitable resources and competencies to do the job to the best of their ability is a key function of the role. I enjoy many aspects of the job, from planning annual staff engagement days, to participating in five-nation knowledge exchanges with counterparts in England, Scotland, Northern Ireland and Wales. Working in partnership with others in the health sector is key to getting things done. In the area of innovation for example we have introduced an “Energy Pod” as a rest facility for clinical staff at one of our hospital libraries. This has been a big success, attracting new clients into the library and affording an opportunity to promote library services at the same time. Reporting performance indicators that make sense to stakeholders is very important. Integrating library services with the broader infrastructure of the health service is also key to sustainability and growth. Reflecting on the transition to digital health, the skills required by the health librarian will continue to be specialised. The traditional skills of critical thinking, logical reasoning, collating information and making it easily accessible to busy clinicians will all remain important, while seeking innovative ways to deliver and filter information, for example, using artificial intelligence and big data may become more mainstream. The specialist health librarian will need to keep ICT skills up-to-­ date to make the best contribution to the health system. The COVID-19 global pandemic has highlighted the value of advanced searching and research skills that health librarian specialists have across the world. As long as platforms like PubMed, Cochrane Library, Embase, medRxiv and bioRxiv continue to exist, these searching skills will be in high demand. Evidence searching, synthesis, summaries and reviews are all knowledge products that health librarians provide; this is a niche but respected area. We save the health service time and money by carrying out these searches expertly; we stamp out “fake news” and stand up for truth. Evidence-based librarianship will hold its own, although the future will see more integration of our services

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with complementary departments, e.g., Research & Evidence, ICT. We are a wellorganised profession and our national and international professional associations do the hard work of continually updating competencies to future proof our skills.

Gemma Siemensma, Ballarat Health Services, Australia My journey to health libraries came in a rambling way when I was 21. As a child and adolescent, I adored reading and really had no grand plans for my future but after secondary school, chose to undertake the TAFE qualification for Library Technician because I loved to read (such a cliché)! During this time, I worked at a public library. My partner lived a few hours away and when I finished TAFE, a job for a Library Technician came up in the hospital library near his hometown, Ballarat. Thus began my career in health libraries. I knew very little about health libraries but was enthusiastic. I had an inspiring boss who encouraged me to study for a Bachelor’s degree in Librarianship via distance education. I completed this and went on to study my Master’s in Librarianship and then my MBA.  Working in health libraries has allowed me to gain an understanding of all the different functions library staff undertake. As I worked my way up, I undertook tasks including article requests, document delivery, literature searches, alerting services, implementing online systems, complex research queries and cataloguing. Now in the Library Manager position, I get to enjoy budgeting, planning, inspiring my team and aligning health librarian expertise with the goals of the organisation. This is coupled with professional development opportunities which help me hone my skills, gain new knowledge and implement changing practices. I am motivated by the people within my workplace. Clinicians do an incredible job of caring for those in need and I think the interprofessional collaboration between clinicians and health librarians creates a continuous improvement ethos. In a hospital, the ultimate goals relate to patient care and population health, and the scope and focus of hospital libraries’ activities are defined by reference to these primary areas of responsibility. This includes providing access to the best available evidence, and provision of professional information services delivered by trained and credentialed information professionals. The information we provide modifies current clinical practice, diagnosis and medication choices. It is an empowering role, knowing that the information we make available improves clinical outcomes. I have been involved with my professional library association for almost 20 years. I am a big believer that you get out more than you put in. I am currently the Convenor of Health Libraries Australia. This voluntary role has opened up collegial networks, amazing opportunities and friendships. I have worked on projects I never thought I would have been capable of, have been pushed to undertake challenging tasks, and grown as an individual and as a librarian. I get to work with colleagues across the country who are helping to shape health libraries in Australia and advocate and influence our professional landscape. In turn, these opportunities have opened up other avenues within my organisation and allowed me to dream big and

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strategically place the library in new and emerging situations. My library offers services to other health organisations within our region as none have a library or librarian onsite. This makes equity of access to information a possibility for my whole region. Statistics show that those in rural and regional areas have poorer health outcomes but we know that evidence-based information can bridge this divide. Getting the right information, to the right people, at the right time is one of the reasons I love working as a health librarian. One of my passions is equality. Smaller services should not be disadvantaged because they do not have resources or access to much-needed information. Filling this evidence gap allows the health system to work as a whole, with better outcomes for individuals and organisations. As we have moved into the digital health realm, technology has enabled health librarians to make information seamlessly available; as technology develops, I think more avenues will open up and we will see more information in more places. One of the challenges for health librarians is to ensure clinicians and consumers are accessing reliable and accurate information—health literacy is a growing area as we grapple with information overload. Another huge shift in the coming years for health librarians is in open access publishing; this is evolving rapidly and as mediators of information our roles and systems need to change and develop. Also, as technology advances, systems need to be interoperable rather than the current piecemeal and disjointed experience. The evolution of digital health brings new ways to get information to people; this, in turn, leads to better clinical outcomes which is what everyone working in health is striving for.

Helen Baxter, Austin Health, Australia I work as the Clinical Librarian at Austin Health, a major teaching hospital in Melbourne, Australia. Like many librarians this was not my first career; my initial university studies were in nursing followed by several years working in the health care system as a Registered Nurse. I returned to university, undertaking postgraduate study in Library and Information Management. I was keen to combine these two qualifications and therefore actively pursued health librarianship opportunities. I was initially employed as a member of the Health Library team at Austin Health, working at its rehabilitation campus, then later at the main hospital campus. During this time, it became clear to me that a health librarian’s role does not need to be defined or constrained by the physical library space. In 2014, a new Chief Librarian at Austin Health envisaged a new role for a Clinical Librarian and I was given the opportunity to build and shape this outward-facing role for the organisation. As a Clinical Librarian, my goal is ensuring the timely provision of evidence at the point of care, or as learning opportunities arise, for all clinicians. I am passionate about contributing to research evidence that can be translated into clinical practice that ultimately improves the quality and safety of patient care. The clinical knowledge gained by studying and working as a nurse is a considerable advantage to my role today. Information is often required quickly by clinicians and being able to

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rapidly decipher the medical literature aids in effective searching to find relevant evidence. My role is visible to hospital staff via my participation in clinical meetings, grand rounds, journal clubs and education sessions, and it carries a broad range of responsibilities. The Library is a pivotal contributor to the organisation’s research outputs, and in this sphere, I design complex search strategies, provide comprehensive advice for complex topics, and as a result, have contributed as a co-­ author on several systematic and scoping reviews. As Clinical Librarian I lead the development and delivery of tailored education and training to promote the culture of evidence-based practice across all levels of the organisation. In collaboration with the Library team, I contribute to the planning, coordination and delivery of interdisciplinary research courses; prepare synthesised evidence briefs for the hospital executive; contribute to the development of our library website; and actively keep the team abreast of best practice literature searching methods to support the convergence of new technologies and clinical decisions. I am conscious of how a Clinical Librarian’s expertise can contribute to the decisions of care teams and research groups. With an enormous variety of clinical questions arising in the hospital setting, and clinicians keen to ensure that the care they deliver aligns with best practice recommendations, there is little time for sitting still! Being part of a process that ultimately improves patient care is what drives my passion for the job: playing a pivotal role in supporting an evidence-based practice model to enable practitioners at the coalface to know how to access the latest evidence in their specialty, critically consider and apply it. Professional health librarians face a rapidly evolving work environment. The burgeoning of internet-based health information means that both digital information literacy and health literacy are critical. In the future of evidence-based healthcare, health librarians have a role in ensuring evidence underpins the adoption of new digital health technologies, particularly clinical decision-support systems. In the research realm, data management and the interoperability of technologies are emerging areas of focus—this will require expanding the health librarian’s skills further to meet the challenges associated with the digital transformation of healthcare information systems and information management. We will need to adapt as software automating the management of large evidence reviews becomes ubiquitous, and as “living documents” become mainstream. Our ability to work across health disciplines and contribute a broad knowledge perspective of the local healthcare environment remains an important asset and enables us to tailor large amounts of evidence to an immediate clinically relevant context.

Meena Gupta, Australian Catholic University, Australia I am an early career health liaison librarian in an academic setting. The three pillars of an academic institution, namely teaching, learning and research, underpin my primary mission. Goal setting and being agile in researching and using educational opportunities have been central to perform my liaison tasks better. I have a Bachelor’s

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degree in Business Information and Knowledge Management along with a Master of Business (Information Technology). Both courses helped me understand information needs, information-seeking behaviour, and the value of credible knowledge sources to enable informed decision-making processes in personal and professional endeavours. Whilst working, I wanted to learn more about teaching in adult learning environments, with a Certificate IV in Workplace Training & Assessment. There is no formula when choosing the qualifications befitting a health librarian. However, given the opportunity to become one, I identified a skills and knowledge gap so I enrolled in 2019  in a micro-credential on Digital Health Information Services offered by the University of Melbourne and Australian Library and Information Association Health Libraries Australia (ALIA HLA). The course was aligned with the ALIA HLA (2018) competencies framework and gave me confidence in health information work. I also volunteer as a member of the ALIA HLA Executive Committee and in that way actively contribute towards professional development opportunities for myself and others. I always wanted to be a librarian from a young age—an information detective! The knowledge and skills obtained from my education informed my choice to pursue a career in academic librarianship. I began in a liaison position at Australia’s Deakin University as Science, Engineering & Built Environment Liaison Librarian; then at Australian Catholic University (ACU) as Senior Librarian in Business; now continuing as a Senior Librarian in Health (Nursing, Midwifery and Paramedicine). I deliver flexible access to information resources, providing services that partner with academics and researchers to achieve their goals of furthering knowledge and education. I liaise with other health librarian colleagues, academic e-learning advisors and students to determine their data and information needs, and I enable them to do independent research locating, summarising and synthesising evidence tailored to meet their needs. I provide outreach services such as videoconferencing, scaffolded information literacy classes, understanding relevant tools within specialist health databases, providing research metric services to enhance academic researcher profiles. I can identify gaps in where information users search for information and present the information via learning management platforms or reports. Previous positions in marketing, law firm libraries and market research helped me appreciate how I could help people understand the importance of informed decision-­making in a digital environment, with expert knowledge of what, where, how and when information was needed. Being an academic librarian/health information specialist is an opportunity to contribute to researcher and student success. Enabling users to find and use information ethically within a professional capacity, engaging in real-world research, and demonstrating ways to access information are only the tip of the iceberg. Integral parts of my role include being able to show people how to use the information and think critically about health information systems, use decision-making tools, processes and strategies to enable evidence-based practice. My personal motto has always been “providing quality information services with a smile”; it offers ways to engage in daily acts of kindness in a sensitive and volatile work environment. The future of the health librarian is now, with increased access points for health information and health-related data. Therefore, we must continue to work on

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finding (e.g. delivering tailored information literacy and resource literacy), on using (e.g. understanding systematic searching, on informing the development of systematic reviews and academic curriculum development) and on disseminating information (e.g. research impact reports). Future health librarians may use and adapt technology such as virtual and augmented reality to inform critical decision-making around health professional practice; these kinds of innovations may keep them central in the health professionals team.

Blair Kelly, Deakin University, Australia I came to librarianship as a career change, seeking a profession that helped others, solved problems and performed a social good; librarianship met these criteria. I began by enrolling in a library technician degree and in the ensuing move from the classroom to employment found library work to be greatly satisfying. My first library job was at the University of Western Australia, followed by a longer period working at the University of Notre Dame Australia. Here I worked in a number of positions, from library assistant to technician to senior library technician, eventually moving into the roles of liaison librarian and university copyright coordinator. Being in a small library there was an expectation that daily duties covered a wide range of tasks and in hindsight, this was an excellent experience for my move into health librarianship as a Reference Librarian at Barwon Health, another small library environment. My initial challenge was shifting from an education-focused mindset to one which focused on providing a service while including elements of education where appropriate; I was used to teaching people to search the literature, not searching the literature on their behalf. In my current role as Medical Librarian at Deakin University, I am grateful for my time working in healthcare service. An understanding of the Victorian public health system and clinician life is something I now regularly use to connect the experiences of the student to their post-­graduation professional life. As Medical Librarian I am part of the Deakin University Library’s Faculty of Health team, with an emphasis on its School of Medicine. The Library plays an important part in supporting digital literacy and this gives focus to my interactions with the School. These involve working with teachers, students and researchers. These interactions may happen in person, including instances where I am teaching students in physical classrooms, or online, commonly in the form of slide presentations or through the creation of learning resources such as library resource guides and information pages. The aim is to provide learning experiences that are aligned, and where possible integrated, with curriculum, and this relies on me maintaining close relationships with teaching staff. Collection development is part of my role which supports teaching, learning and research. This includes value analysis for purchases or subscriptions and decisions on how the Library budget could best be spent. Here I work closely with colleagues in the Library’s Collections, Copyright and Licensing department to identify resources for inclusion or removal from the

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collection, keeping in mind the teaching and research needs of the School, as well as the university’s broader areas of focus, with the aim of providing a solid return on investment. Finally, my role includes leadership activities. In the Library’s Health Team, I assist the Team Manager to build capacity among team members. I also assist the Campus Manager with actions related to the campus library building and team. My role has also presented me with opportunities to conduct my own research; dissemination of this research via journals and conferences is another area where I provide leadership by example. This role is exciting and dynamic; every day is different, there is always something new to learn, and while the learning and the work are never done, things are never boring. It is rewarding to come to work with intelligent and creative people—library staff, academics and students—and to play a part in the development of future health professionals and the success of health research. Looking to the future, research support is an area where health librarians will see rising demand for their skills and, in some organisations, have the opportunity to step into areas of unmet need. Today’s deluge of research publications, and the accompanying feedback loop of an increased push for practitioners and researchers to publish, mean that skills with the activities in the research cycle will be valued, including teaching and/or direct application of advanced literature search skills, including using text mining and artificial intelligence/machine learning to speed up comprehensive literature reviews. Another area will be research data management best practices, to support research integrity, transparency and replicability. Expertise in methods for demonstrating research impact beyond citation counts will be increasingly valuable as competition for research funding increases. Lastly understanding and working with open science principles, including open access options for dissemination, will be valued. Advocating for the importance of the above skills, and developing relationships in order to produce and promote reliable, replicable processes, will continue to underpin the work of the specialist health librarian.

Conclusion The case studies in this chapter convey a pragmatic approach combined with a principled vision and a passion for helping people. They demonstrate health librarians’ distinctive discipline knowledge and technical skills in managing health information to deliver highly valued services to clients at the coalface of health care delivery, education and research. They provide examples of the range of education and employment pathways that can lead an individual to a career in the specialist area of health librarianship. They highlight health librarians’ advanced competencies and expertise in literature searching and knowledge of health research information sources beyond the generic librarians’ skills base. They also highlight some of the national and international collaborative professional networks and systems that facilitate resource sharing and seamless access to information for all those who work in the health world.

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Health librarians’ stories have highlighted transformative changes that have occurred over the past decades in the ways that health library services are delivered, particularly in relation to the impact of evidence-based medicine, health literacy and digital health. As well as delivering information services, health librarians have been at the forefront of teaching others the information and digital literacy skills associated with evidence-based practice. Collectively the case studies serve to illustrate health librarians’ knowledge management and information governance functions and roles in their organisations. On a societal level, health librarians have been champions and enablers of equitable access to high-quality health information.

References ALIA HLA. ALIA HLA competencies. 2018. https://www.alia.org.au/sites/default/files/HLA%20 Competencies_0.pdf. Accessed 1 Jun 2020. Kammermann M.  The census of Australian health libraries and health librarians working outside the traditional library setting: the final report. ALIA. 2016. https://www.alia.org.au/sites/ default/files/CENSUS%20of%20Aus%20Hlth%20Libs%202012-­14_Final%20Report_2016. pdf. Accessed 1 Jun 2020. Ritchie A. ALIA/HLA competencies review 2018: What is a health library and information professional? What do they do and why do they do it? JoHILA. 2020;1:28–35. Ritchie A, Siemensma G, Gilbert C, Gaca M, Taylor J. Hospital librarians’ contributions to health services’ accreditation: an account of the Health Libraries for the National Safety and Quality in Health Services Standards (HeLiNS) research project, 2016-18. JALIA. 2020;69(2):215–45. Schardt C.  Health information literacy meets evidence-based practice. J Med Libr Assoc. 2011;99:1–2.

Chapter 20

Working as a Health Research Information Specialist Ann Ritchie, Steve McDonald, Suzanne Lewis, Cecily Gilbert, Terena Solomons, Kristan Kang, and Mari-Elisa Kuusniemi

Abstract  Working with health research information is an evolving and specialised area of health data, information, and knowledge management. The individuals who perform these roles have been characterised as being an emerging third tribe, sitting between and complementing the research work of scientists and administrators. Their specialist information management work facilitates the research process, intersecting at many points and stages in the research lifecycle. A thorough knowledge of scientific research methods as well as information and knowledge management competencies are essential. The six research information specialist case A. Ritchie (*) Independent Consultant, Melbourne, VIC, Australia S. McDonald Cochrane Australia, Melbourne, VIC, Australia e-mail: [email protected] S. Lewis Central Coast Local Health District, Gosford, NSW, Australia e-mail: [email protected] C. Gilbert Centre for Digital Transformation of Health, The University of Melbourne, Parkville, VIC, Australia e-mail: [email protected] T. Solomons Western Australian Group for Evidence-Informed Healthcare Practice, Perth, WA, Australia e-mail: [email protected] K. Kang Australian Research Data Commons, Canberra, ACT, Australia e-mail: [email protected] M.-E. Kuusniemi Research Services, Helsinki University Library, Helsinki, Finland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_20

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studies described in this chapter have been selected to demonstrate the variety of the roles, types of work, and skillsets that are required. Keywords  Case study · Evidence-based healthcare · Health research lifecycle · Knowledge translation · Research data management

Introduction This chapter contains six case studies that create a composite picture of how and where the work of health information research specialists has an impact on the different stages in the health research lifecycle. During the early research scoping stages, conducting a literature search helps to identify and refine a research question and find the evidence on a particular topic; searching the literature begins to reveal what is already known as well as alerting the researcher to potential gaps in the current evidence base. Research information specialists have unique skills in literature searching and devising and implementing search strategies according to established and robust protocols, which ensure replicability. They are skilled in managing the resultant reference lists, and identifying abstracts and full text articles for appraisal, evidence syntheses, and the creation of the various types of reviews. Artificial Intelligence (AI) and machine learning tools are already assisting in the task of sifting through extraordinary numbers of abstracts, selecting articles for further scrutiny, and critical appraisal before synthesising in reviews of various sorts. Designing and executing a comprehensive literature search forms the basis of a solid systematic review; literature searches for systematic reviews are, in themselves substantial pieces of research often taking around 6 months to complete. In the planning stages of the research lifecycle, decisions affecting research records and data management need to be addressed, usually through the production of research data management plans. This is a growing area of specialisation that calls for a range of skills, with the study data needing to be actively managed during the project and for years after the research has been completed. Also in the planning stages, the literature searching services of research information specialists contribute to the production of research protocols, research grant proposals, and ethics submissions. How to translate research knowledge into practice is a problem that has preoccupied many researchers, practitioners, and policy-makers. Research information and knowledge management specialists bring an evidence-based healthcare philosophy to a multidisciplinary review team. Their expertise is critical in the scholarly communications and publication stages, when choices about how to disseminate results and the most effective publication channels are important. An open science model allows freedom of access to the research literature (via open access publishing models) and re-use of health research data (by adherence to the FAIR—Findable, Accessible, Interoperable, Re-usable—principles).

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The integration of research-derived knowledge obtained from the published literature with real-world data from accessible datasets produces what has been termed “living” evidence—applicable to individual health care decision-making as well as the creation and ongoing review of health policies and guidelines. The exciting possibility of continuous evidence surveillance is created when barriers to access (such as paywalls and restrictions on re-use of data) are removed.

Steve McDonald, Cochrane Australia, Australia Studying social and political science was my ringside seat in momentous times— during the fall of communism in Eastern Europe and the demise of Thatcherism in Britain. A few years later, faced with economic realities, my interest in revolutionary politics gave way to the more refined pursuit of indexing and classification. To be fair, by the mid-1990s, library and information science was on the verge of its own revolution, sparked by the arrival of the internet. Medicine, too, was a profession in the throes of radical change: evidence-based medicine was shiny and new (and viewed with suspicion by many) and the Cochrane Collaboration (formed in 1993) quickly became the vanguard of the evidence movement, coalescing around a formidable group of pioneers and iconoclasts. This was the environment I stepped into as a naïve information science graduate 25 years ago in Oxford. They were genuinely exciting times; to be a health information specialist as evidence synthesis took off, was to feel that you were helping to right a long-standing wrong. My career has been spent on the frontline of evidence synthesis—doing, supporting, teaching, advocating, and researching. The focus of those early years was to find the evidence to feed into systematic reviews. In the absence of adequate indexing, we scoured Medline (and later Embase) for reports of trials, screening tens of thousands of abstracts and working with the National Library of Medicine to re-tag records. I supervised volunteers who would spend hours in libraries searching journals by hand to find trials that either pre-dated databases or were so poorly reported they were unlikely ever to be retrieved. The Cochrane trials register is the ongoing legacy of that early effort. Knowledge and skills acquired during those formative years with Cochrane in the United Kingdom—around search strategy development and design, research methods, and supporting systematic reviews—have evolved and, happily, are once again central to my roles and responsibilities. I have been the resident health information specialist at Cochrane Australia for 20 years, contributing to commissioned evidence syntheses, getting involved in research, and teaching search methods to systematic reviewers. However, only in the last few years have I returned to my information science roots, having previously managed international research projects with our Cochrane partners in the region and served as a board member for Cochrane internationally. Although evidence-based medicine is now so mainstream it is a more historical than contemporary movement, the impetus to see that decisions about health and health care are informed by reliable evidence remains personally highly motivating.

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Alongside this is a desire to improve the efficiency of evidence synthesis by improving timeliness and reducing duplication of effort. The health information specialist’s role in supporting traditional systematic reviews is not radically different from that of yesteryear—the expert searcher’s value is well-recognised in the field of evidence synthesis—but we need to continually acquire diverse skills and techniques that reflect new technologies and approaches. Some of these new technologies, such as machine learning and text mining, bear directly on how we design search strategies and facilitate the assessment of study eligibility. New approaches to evidence synthesis, such as living systematic reviews and guidelines, are becoming more commonplace and more feasible because of these new technologies—a trend that accelerated during the COVID-19 pandemic. My involvement in some of the early pioneering work around living evidence has led me to PhD studies in which I am evaluating how continuous evidence surveillance and the use of machine learning classifiers can improve the efficiency and feasibility of living reviews and guidelines. These are interesting times in evidence synthesis. While fully computable evidence syntheses, in which structured data are derived from different sources, are not a reality yet, it is also true that the days of relying on a single study publication from a bibliographic database as the sole source of evidence are disappearing. We recognise that studies have lifecycles and shed data and information along the way that are critical to the reliability of evidence synthesis (via protocols, study registers, data repositories, publications, and open science platforms). As health information specialists, we have skills in searching, data storage and data linking, but we must be prepared to embrace new technologies, hone our skills, and consider how we position ourselves in the changing landscape of evidence synthesis.

 uzanne Lewis, NSW Central Coast Local Health S District, Australia I came to health librarianship via a Bachelor of Arts and PhD in English literature, followed by a Graduate Diploma in Library and Information Science. I became a health librarian almost by default as I was successful in the first job I applied for after graduation. The fact that I am still a health librarian more than 20 years later is a reflection of how much I enjoy my role, and the opportunities I have been afforded by my employer and via my engagement with the professional community of health sciences librarians in Australia and internationally. During the first few years of my career learning the foundations of health librarianship, I became aware of the evidence-based practice philosophy and framework as it applied to both health and librarianship. I completed one of the early online courses in evidence-based librarianship offered by the University of Sheffield School of Health and Related Research and made available to Australian librarians via the Australian Library and Information Association (ALIA). Together with several enthusiastic colleagues, I began applying evidence-based principles to workplace projects, and disseminating the results via several publications in the Evidence-Based Library

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and Information Practice journal. I became an evidence summary writer for the journal, which involved critiquing a recent research publication in the library and information science (LIS) field. I progressed to reviewing evidence summaries and peer reviewing original research for publication. As I became more confident with critical appraisal of LIS research, I wanted to be able to apply those skills to the health and biomedical research I dealt with every day on behalf of clinicians. I could search the biomedical literature and was confident in selecting the most relevant literature from the results, but was aware of my uncertainty around the concepts of methodological rigour and quality of published research. What were these randomized controlled trials, cohort studies, and metaanalyses that health professionals talked about? I completed a Graduate Certificate in Epidemiology, which enabled me to teach critical appraisal skills in addition to literature searching skills, and also to talk to clinicians about clinical research. Having an understanding of clinical research methodology, including the best research design to answer different types of clinical questions, is a valuable foundation for advanced skills in searching the biomedical literature. In 2011, I was asked to be a tutor at the first Australian Evidence-Based Practice Librarians’ Institute (AEBPLI), a residential workshop for health librarians on the principles of evidence-based practice, research methodology, and critical appraisal. Working in a team of Australian and US-based health librarians was a game-­changer for me. The intensive preparation for teaching at the Institute, plus mentoring from the highly experienced US-based tutors, consolidated my knowledge. However, the learning is not all one-way; the format of the Institute encourages knowledge sharing among tutors and students in the programmed sessions and via informal networking. I have returned as a tutor and co-convenor of the Institute every year it has been held since 2011. Another teaching opportunity arose when the Australian Library and Information Association Health Libraries Australia (ALIA HLA) group developed and delivered an online subject in health librarianship in partnership with the Queensland University of Technology (Health Librarianship Essentials) and later with the University of Melbourne (Digital Health Information Services). I led the development and teaching of an Evidence-Based Practice module each time this course ran. These four initiatives—early adoption of evidence-based library and information practice, a Graduate Certificate in Epidemiology, the AEBPLI, and the online specialist health librarianship/digital health information subject—have allowed me to achieve three ALIA HLA competencies (ALIA HLA 2018): health literacy, curricular design and instruction, and teaching the information skills associated with evidence-­based practice; health research and the application of health research methodologies; and health information professionalism. Like most of my colleagues, I spent the early years of my health librarian career achieving competence in delivering reference and information services (a separate HLA competency), including completing a course in medical terminology and building skills in searching the biomedical and nursing bibliographic databases. More recently, I led a project to build a validated search filter for retrieval of integrated care literature in PubMed. This gave me the opportunity to further develop my knowledge of

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bibliometrics, Medical Subject Headings (MeSH), and search syntax. Moreover, the process of building a tool that is of direct benefit to clinicians and researchers. Working with an expert advisory group gave me invaluable insights into researchers, clinicians, and health service planners and their priorities and how they approach information in a digital world. Over the last 20 years, improvements in online search platforms and discovery tools, and increased access to digital information resources, mean that most clinicians are able to find the information they need to ensure their routine clinical decision-making is based on reliable evidence. The role of the health research information specialist has changed from research gatekeeper to a more nuanced role perhaps best described as a research information facilitator. Such a role includes searching for evidence on the complex, multi-faceted, wicked questions that arise from clinical practice and health service planning; appraisal and summary of evidence in the form of evidence checks or rapid reviews; creation and testing of tools such as validated search filters that provide fast-track access to published research; teaching health literacy skills in context; and facilitating knowledge translation from research to practice. I think it is this last facet of the role that inspires me most—being part of the process that translates evidence into clinical practice that directly benefits patients. As healthcare organisations make the transition to digital health, the discipline of health research information specialists will continue to work at the evidence/clinical practice interface, continually adapting to new technology but retaining the role of facilitator.

Cecily Gilbert, University of Melbourne, Australia Since 2012, I have worked as a health information specialist and research assistant in the Centre for Digital Transformation of Health (CDTH) at the University of Melbourne. But my working life began in libraries, and most of it has been in health; I count myself lucky to have experienced the huge transition in information management, which has occurred in the course of my career. After matriculation in 1970, I did a 3-year diploma of librarianship at RMIT University in Melbourne. We were introduced to principles and practice in reference service, cataloguing and classification, systems analysis, and library administration; in 1990 I upgraded this qualification to a library and information science degree. Between 1974 and 1978, I held librarian positions in higher education and science, maintaining and searching large card catalogues, and using printed subject indexes for reference queries. My first taste of health work was as a librarian at Melbourne’s Queen Victoria Medical Centre between 1978 and 1984. In this period, I accessed remote databases using an acoustic coupler to connect a dumb terminal to the phone line, to send literature search commands and to receive the results. I returned to university library reference work in 1987, then moved back to a hospital librarian position 10 years later. By 1997, web access was possible in hospitals, PubMed provided free MEDLINE access, and gradually other databases transitioned to

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online services. These technological developments fully enabled end-users to search their own questions: a dramatic change that also altered my role in teaching and information provision. The Cochrane Collaboration, which had emerged in 1993, promoted systematic reviews to examine the evidence on healthcare questions; as a result, my literature searching was transformed by the levels-of-evidence paradigm. In 2004, I undertook a 12-week placement at Monash Health Centre for Clinical Effectiveness (CCE), which conducts tailored syntheses of health information sources, in response to clinicians’ enquiries, and fosters evidence-informed practice. I was supervised by an experienced information specialist, participated in syntheses on two detailed topics, became familiar with health technology assessment resources, and honed my skills in critical appraisal. I gained greater confidence and a broader view of information specialist work, which fits my current role at CDTH. I am embedded in the health informatician research team, providing customised information services and research assistance to between 10 and 15 researchers in various roles including practitioners, clinicians, researchers, and educators. My tasks include: literature searching; analysing, summarising, and scoping information sources; preparing quick environmental scans; obtaining, extracting, managing, and analysing data; teaching postgraduate students; research data management; writing and proofreading; and preparing ethics and funding applications. These activities are in line with four of the seven competency areas for Australian health library and information professionals (ALIA HLA 2018): health reference and research services and delivering best practice information services; management of health knowledge and information resources in a variety of formats; the digital and ehealth context, and the technology and systems used to manage data, information and knowledge resources in the delivery of library and information services; health research and the application of health research methodologies. While many of the duties are similar to those performed by health librarians in a library, there are differences. The key variation is my ongoing link with a defined group of informaticians: it is a huge advantage to be attached to a specialist group, to gain a deeper knowledge of the body of relevant information resources, as well as the research projects and interests. It allows me to anticipate some information needs, and to draw on conceptual sources that apply in this field. I am challenged by involvement in new tasks (such as supporting the weekly presentation of a fully online subject on digital health resources, and becoming competent with visualisation tools). I have three main observations about the health research information specialist in the digital health future. First, more widespread research use of health datasets of many kinds will require the information specialist to keep abreast of such sources and where to locate them, effectively extending their “knowing where to find” capability to non-bibliographic repositories. Second, the shift to translational research—translating research findings into evidence-based practice and policy—means that time-poor clinicians and researchers will require and value literature reviews that synthesise current knowledge and lessons from existing research. There is an imperative from government to gain maximum benefit from its research investment, and health research information specialists with the capability to synthesise and assess sources will need to be ready for this. Third, health research

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information specialists should claim a place in the frameworks for learning health systems (LHS), which integrate real-world data and evidence in a health system to improve outcomes. The LHS literature is replete with references to digital knowledge objects “curated and managed in digital libraries…and made available to users” (Guise et al. 2018: 2237), but lack any mention of digital health librarians, whose skills in filtering, summarising, and organising information would be highly relevant. Finally, as a general point, the health research information specialist workforce does not have the level of diversity that is evident in the general population; we have an obligation to redress this.

 erena Solomons, Western Australian Group T of Evidence-­Informed Healthcare Practice, Australia With my father a surgeon and mother a nurse, it was by osmosis that I picked up medical terminology over dinner table conversations whilst growing up. Since 2013, I have worked as a health research information specialist and as a research librarian for the Western Australian Group for Evidence Informed Healthcare Practice (WAGEIHP), a Joanna Briggs Institute (JBI) collaboration centre. I completed a Bachelor of Arts majoring in History and Geography from James Cook University in Townsville, Queensland, Australia. I wanted a profession that allowed me to travel and work overseas without having to re-qualify. Librarianship offered this and I studied the Graduate Diploma of Library Science at the Queensland University of Technology. I worked in higher education and special libraries in Australia and the United Kingdom before my first foray into health libraries, a 6-month contract in the Health Science team at Curtin University Library in 1999 in Perth, Western Australia (WA). I ran information literacy workshops focusing on the main health databases and worked on a project evaluating their platforms. For 17 years I managed Hollywood Private Hospital Library, overseeing the change from print to digital collections. The service I enjoyed most was searching the literature for busy doctors, nurses, allied health, and corporate staff. With ALIA HLA support, in 2002 I attended the Evidence Based Clinical Practice Workshop in Adelaide, along with two other WA health librarians with whom I ran Evidence Based Practice (EBP) workshops for WA librarians. This experience gave me a good understanding of EBP and was the catalyst for working with the Clinical Pathways Co-ordinator at Hollywood Hospital in finding evidence to support clinical pathways and the clinical practice manual. Serendipitously, discussions over a cup of coffee with school mums led me to my role with WAGEIHP, based at the School of Nursing, Midwifery, and Paramedicine at Curtin University. I met a paediatric nurse who had conducted a systematic review as part of her Masters of Nursing degree and had been approached to write evidence summaries for the JBI. The thought of searching databases, however, was daunting for her. We began a job-share—I would find the evidence and she would synthesise it.

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I am now embedded within research teams at WAGEIHP, where I began 7 years ago searching for studies relating to wound care. The focus of the initial evidence summaries was on low resource countries’ use of naturally available wound products like banana leaves, potato peels, acetic acid, turmeric, and coconut products. To get up to speed with wound management practices and to understand the fundamentals of wound healing I undertook Wound Care Australia’s online learning modules. My childhood years of flicking through my father’s surgical textbooks prepared me for seeing photos of some horrible wounds. My self-education to acquire a wound care vocabulary was important for identifying keywords and indexed terms and developing comprehensive search strategies to identify relevant studies. I learnt many interesting facts in my research and one article I will always remember about the use of coconut products for wound healing was “Skull trepanation in the Bismarck Archipelago”; coconut juice was used to irrigate the wound from trepanation (the process of drilling a hole in the skull) and it was also used to wash the hands of the surgeon (Watters 2007). Since 2015, I have been involved with research teams undertaking systematic and scoping reviews. Tasks include preliminary environmental scans; conducting searches across all databases and grey literature; establishing shared EndNote or Mendeley libraries and de-duplicating results; exporting results to Rayyan for the team to screen, finding full text articles, PRISMA flowchart and writing the search methodology section for the protocol and review. I also critique search strategies for postgraduate students planning to submit manuscripts to JBI. I enjoy working with researchers at the start of a project when they are working out their protocol. I like scoping the literature and using text mining tools like MeSH on Demand, PubMed Reminer, and Yale MeSH analyser to develop the Concept Table (sometimes referred to as Logic Grid, Matrix) to capture all keywords and indexed terms. Extensive literature searching as a hospital librarian gave me the initial skills and experience for this role. Since then I have gained knowledge and developed skills in the following ways: reading JBI, Cochrane and Campbell Collaboration reviewer manuals and professional literature related to evidence synthesis; participating in the Expert Searchers listserv and following information specialists on Twitter; co-­ chairing the JBI Information Science Methods Group; attending HLA professional development events (particularly advanced searching workshops led by Julie Glanville and Carol Lefebvre); and the continuing education workshops at EAHIL (European Association for Health Information and Libraries). Learning about new sources of evidence and new software tools like Rayyan, DistillerSR, EndNote sharing, and Mendeley for managing results is what excites me (or horrifies me when it does not work as expected!). In the digital health future, the tsunami of systematic and other types of reviews offers fantastic opportunities for health research information specialists to make a valuable contribution to review teams. Finding the evidence is a crucial step in the process, and there is further scope for information specialists to improve practice by peer-reviewing search strategies for systematic and scoping reviews (Grossetta Nardini et  al. 2019). From my experience, there is demand for health research

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information specialist skills that are not always explicit; often word-of-mouth recommendations have led to me being involved in reviews. Artificial intelligence applications will assist in managing large amounts of data for evidence synthesis in the future, but they are unlikely to take over the role of the health research information specialist. This work will always need human input and checking.

Kristan Kang, Australian Research Data Commons, Australia Inspired (embarrassingly enough) by FBI Agent Dana Scully from the TV show “The X-Files”, I started a Bachelor of Science degree fascinated by the idea of using evidence to solve complex problems. I majored in psychology/neuroscience, completed an Honours research project on brain activity in attention deficit hyperactivity disorder (ADHD), and then continued to a PhD project, which aimed to understand the neural mechanisms underlying consciousness. I was part of a group effort to collect large amounts of information on brain function and human behaviour that was divided up among many different projects. Many of the business processes to support this effort were being made up along the way, and my project became deprioritised as time went on; I completed my degree but the experience was not pleasant. My research was limited by the practicalities of obtaining good data in a timely fashion, and my introduction to doing science as a profession was less than ideal. After 6 years as a trainee researcher, I was turned off being a career academic, but I had no idea what I did want. Eventually, I accepted a research support role with a dementia research group that needed someone to manage their large data collection and data sharing activities. Initially, I was not sure it was the right fit for me. I understood the science they were doing, but my role as data manager required me to learn a whole new set of skills on the job: technical knowledge of databases and applications, data storage and security, as well as a range of general business skills like strategic prioritisation, relationship building, and project management. As the group evolved into a research centre, I gained additional experience in research governance, ethics, and privacy and contract law. I never had formal training in these things apart from the occasional half-day staff development sessions offered by the university, but through trial and error, I learnt how they worked in practice and how they influenced the productivity of research. In essence, I transitioned from knowing the science to knowing the business of science, and I was promoted to the role of research manager as a result. Despite my earlier reservations about moving from science to research support, I ended up contributing directly and indirectly to hundreds of research projects. I had a pivotal behind-the-scenes role, and while I was not receiving personal academic accolades, my colleagues appreciated my contribution and frequently acknowledged that our group’s achievements relied (at least in part) on the work I did. When the opportunity arose to move to a national eResearch infrastructure organisation as their health and medical data specialist, I was excited by the prospect but nervous; eResearch entails the use of information technology to support

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existing and new forms of research, and this role would draw on knowledge and skills that I had built up informally. Moving from the coalface of research to a national infrastructure role felt like a big leap, but my research experience got me the job. There are many principles and policies around eResearch and open science, but effectively translating these into research practice can be problematic. Those who know only the theory can struggle to understand why researchers are hesitant or simply unable to implement it in daily practice. Understanding the science, the data, and the realities of being a scientist might not be essential for all research support roles, but the more experience you have with these things, the better you can translate between the differing contexts of infrastructure and science in a meaningful and impactful way. The research sector has evolved well beyond the two original tribes of scientists and administrators. The problems with decreased financial support combined with an increased focus on research translation and public benefit means that research as a sector has had to figure out how to operate more efficiently and productively, particularly through digital transformation of traditional processes. The emergence of a third tribe, research support specialists who have a combination of technical and professional skills and research experience, is essential to the evolution of the sector. This is something I know not only in theory, but from nearly two decades of personal experience in both being a researcher and supporting other researchers. The power of evidence-based decision-making is what drew me to science, as did the desire to improve our knowledge of the world and consequently the quality of our lives. However, I have learnt that simply trying to do good science is not enough to produce these outcomes; scientific research is an information-intensive industry like many others, and must function as such.

Mari Elisa Kuusniemi, Helsinki University Library, Finland First, I studied science/organic chemistry. I specialised in bioinformatics. In my master’s thesis, I used bioinformatics methods to predict protein structure for a research group working on drug chemistry. Bioinformatics is a classic example of data science. I have a Master’s degree in Information Science. I specialised in networks and other electronic resources. As a part of this master’s program, I studied computer science at bachelor level. I have also studied educational sciences, but I have not finished the last course of the program; I have used the skills and knowledge a lot, but have not needed the certificate. I got my first position in a library in Finland’s National Public Health Institute. I worked as an information specialist in a small information services unit. The main customers were medical researchers and national health administration staff. I worked with an experienced information specialist from whom I learned most of the important skills needed for research services. I learned the rules of academic research, academic merit, and funding. I developed skills in information retrieval, bibliometrics, communication, and marketing. I also learned project management

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when working in various projects with research groups and the health administration. My last big project was about interoperability of electronic health services. I was responsible for creating data models, ontologies, and terminologies ensuring semantic interoperability. After 8 years, I got a job in the medical library of the University of Helsinki. I worked as an information specialist in the library of the medical faculty, which also serves the University Hospital (mainly nurses and physicians). Part of my role was to develop data management services. Research data management was a new thing at the time. Starting from scratch, in 2012–2013, I organised in-house training and, together with my colleagues (Kuusniemi et al. 2014), started the library’s first data management services, including developing basic guidance and training for doctoral students. Gradually we built up a data management team in the library and started networking with other services around research data management in the university. I worked for a while as a team leader. At the same time, we got national funding for a project where we developed the national requirements for data management plans (DMP), launched a national instance of a DMP tool (the software is DMPonline), and built up a network of data management planning experts. I was the project manager of the DMP project 2015–2017. During 2015, I got an opportunity to work as a visiting scholar in a hospital library at Geelong, Australia for 3 months. The visit gave me the opportunity to reflect on the importance of research librarians in a hospital setting, while also launching new services and working in a new environment. Continuing in my university career, I worked in a data management infrastructure project developing services for all disciplines. I worked as data librarian in data management services, with various tasks including training, consultancy, and marketing. However, the medical field has been the one I feel most at home with, because of my health background. Nowadays, I work mainly in national and international projects and working groups around research data management. At the university, I work primarily with policy-level issues. The main topics are data management planning, research data documentation and metadata (FAIR—Findable, Accessible, Interoperable, Re-usable), data curation and developing new professional skills in research data in libraries. Research data have huge value as information sources in fast moving disciplines. When dealing with current hot topics there is no time to wait until an article is published. Openly available data make global cooperation possible. The potential of open data has not yet been reached, and this area will grow fast in the future. Although it is challenging, I love being part of the movement towards a true digital era. In the transition to digital health, as a specialised health research information professional, I see the need for specialising even further. New professions will be needed and will develop at least in the bigger libraries and research institutes. Traditional skills around networking and cooperation will be even more important in the future, when we need to bridge the gaps that separate the needs of researchers from all different disciplines. Forms of information are changing along with publication channels; health research information specialists need to be part of that transformation too. The US National Library of Medicine sees the library as an important player in medicine and data science (Fridsma 2015) and I hope that other medical libraries join this movement.

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Conclusion There is no single professional degree or certificate that qualifies an individual to practise as a health research information specialist. The roles go by different names: information service manager, library manager, research assistant, research librarian, evidence researcher, research data specialist, and information specialist. As one case study author commented: “The research sector has evolved well beyond the two original tribes of scientists and administrators”. This third tribe, of health research information specialists, is emerging as an essential part of the research team. The case studies in this chapter have shown various pathways, qualifications, and experiences, that can lead to becoming a health research information specialist. The practitioners who work in this field operate in different capacities and levels, and are employed by different types of organisations. Their work permeates and facilitates the research lifecycle. Whatever the area of health research—clinical, medical, pharmaceutical, bioinformatics, or evidence research—the common element demonstrated in the case studies is that they all have an interest and a passion for research-based knowledge and the potential for knowledge to be used to good effect.

References Australian Library and Information Association/Health Libraries Australia. ALIA HLA competencies. 2018. https://www.alia.org.au/sites/default/files/HLA%20Competencies_0.pdf. Accessed 1 Jun 2020. Fridsma DB.  A new vision for the National Library of Medicine. J Am Med Inform Assoc. 2015;22:1111. Grossetta Nardini HK, Batten J, Funaro MC, Garcia-Milian R, Nyhan K, Spak JM, et al. Librarians as methodological peer reviewers for systematic reviews: results of an online survey. Res Integr Peer Rev. 2019;4:23. Guise JM, Savitz LA, Friedman CP.  Mind the gap: putting evidence into practice in the era of learning health systems. J Gen Intern Med. 2018;33(12):2237–9. Kuusniemi ME, Heino T, Larmo K. How to get started with research data management training services for the academic library? Helsinki University Library. 2014. http://old.iss.it/binary/ eahi/cont/76_Mari_Elisa_Kuusniemi_Full_text.pdf. Accessed 12 May 2020. Watters DA. Skull trepanation in the Bismarck archipelago. P N G Med J. 2007;50(1–2):20–4.

Chapter 21

Working as an Allied Health Informatician Mark Merolli, Kirsty Maunder, Dawn Choo, Khye Davey, and Yasmine Probst

Abstract  Allied health professionals are fast recognising the need to adopt digital health technologies in their service delivery models. Reasons driving this need include greater digitisation of healthcare systems, such as electronic medical records, and pressures on healthcare systems to rapidly deploy digital models of care, such as telehealth consultations. Despite a growing need to support the digital allied health workforce, there has been much volatility in formal educational opportunities, career development pathways, and leadership roles. The future of allied health informatics faces challenges, including its heterogeneity of professions, siloed tools and technologies, and ongoing issues surrounding change management, for example. However, striving to increase the profile and provide support for the advancement of digital health and informatics amongst this group of professions will help foster success; success in terms of improved patient outcomes, health system performance, and job satisfaction (to name a few). With greater attention to educating the emerging and existing workforce, mapping career progression, and lobbying for leadership opportunities, we envisage that a clearer profile of the digital allied health information workforce will emerge. Keywords  Case study · Allied health · Dietitians · Physiotherapists · Competencies M. Merolli (*) · D. Choo Centre for Digital Transformation of Health, The University of Melbourne, Parkville, VIC, Australia e-mail: [email protected]; [email protected] K. Maunder · Y. Probst School of Medicine, University of Wollongong, Wollongong, NSW, Australia e-mail: [email protected]; [email protected] K. Davey East Metropolitan Health Service, Perth, WA, Australia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_21

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Introduction Allied health professionals (AHPs) are a diverse mix of health practitioners who are trained in complementary clinical disciplines outside of medicine and nursing. Examples include, but are not limited to: audiologists, dietitians, occupational therapists, optometrists, physiotherapists, psychologists, speech pathologists, and social workers. AHPs can be found in varying roles (i.e. clinician, educator, researcher, policymaker), across a range of healthcare settings (i.e. acute or tertiary public and private hospitals, aged care facilities, private practice, community and in-home care), and in colleges and universities, industry, and consulting. It is fast becoming recognised that there is a need for AHPs to embrace digital health—including electronic medical records (EMR), telehealth, mobile applications, online resources, analytics dashboards and/or ubiquitous care—for (1) the ability to capture patient data in a more complete, systematic, accurate, and efficient manner to improve healthcare recording accuracy; (2) adapting and mobilising digital models of care in light of unprecedented challenges to healthcare (e.g. pandemics); (3) evaluating and improving the quality and safety of care; and (4) collecting and using health data to support the transformation of care, research and/or funding decisions. There are also ever-increasing patient expectations about digital empowerment and a more observable focus on the continuum of care from the hospital/clinic to the home/ community environment (NHS 2019; Philip 2015; Maunder et al. 2018; Greenhalgh et al. 2020). The digital health workforce across the varied allied health disciplines needs to include technologically informed and competent clinicians with diverse specialised clinical skills and also AHPs in information and communication technology (ICT) specialist roles. The types of digital health and clinical informatics competencies which different healthcare professionals must possess have been proposed over the years and mapped, with relevance to the allied health workforce (Coiera 2013; Gray et  al. 2015; Fridsma 2018, 2019; Butler-Henderson et  al. 2020). Such workforce development is an initiative with global support, for example in international scientific forums (Maunder et  al. 2018; Houston et  al. 2018), in UK National Health Service policy documents (NHS 2017, 2018, 2019). Key underpinning themes are (NHS 2019): being digitally ready (strategic, leadership, digital literacy, and governance), being digitally mature (effective use, interoperable, and transformative), and being data-enabled (safety and quality, outcome-driven, and sustainable). Despite a clear argument for the role which AHPs play in the delivery, planning, and coordinating of healthcare, designated leadership roles in digital health continue to elude these professions. While clearly defined roles exist for Chief Medical and Nursing Information Officers (i.e. CMIO/CNIO), support and understanding for the leadership role that AHPs can play in senior executive health teams (has lagged in Australia and internationally; only a handful of Chief Allied Health Information Officer (CAHIO) positions exist. Author Maunder, has worked to promote the importance of such leadership in dietetics (Maunder et al. 2019), and the Australasian Institute of Digital Health (AIDH) has produced a whitepaper calling for greater visibility and a future for the role of CAHIO in 2019 (HISA 2019).

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Collectively the authors of this chapter represent experiences across the disciplines of physiotherapy, dietetics, and speech pathology. Our careers span several settings, including but not limited to private/public hospitals, private practice, community-­based, and aged care, industry (IT vendor solutions), consulting, academia, and research. Further, we are actively engaged members of the digital allied health community, keenly involved in digital health initiatives around the world; we have chaired national and international committees in clinical informatics and been actively involved in annual scientific conferences as presenters, chairpersons, and reviewers to advocate for the engagement of allied health professionals in this discipline. Using a pseudo-collaborative autoethnographic approach (Chang 2016), we have individually reflected upon our career journeys, impact, and progression through allied health informatics, meeting afterwards and reflecting iteratively to distil our experiences of working in this sub-community of the HIDDIN (Health Informatics, Digital, Data, Information, and Knowledge) workforce. Here we present the result of analysing our collective career narratives (Benoot and Bilsen 2016) as four major themes (see Table 21.1 for a full list of themes and sub-themes), drawing on thematic analysis in qualitative research (Anderson 2007): preparing for a career in allied health and AHI; building knowledge and skills in digital health; the unique attributes of the digital AHI workforce; predictions for the future of the allied health information workforce. Taking this further, we have also identified particular themes that are inherently related to our professional training as an AHP and those seen to be purely related to informatics. It is at this intersection of informatics and AHP that we see the unique attributes of an allied health informatician. Our themes are accompanied by short excerpts or quotes from our individual narratives. To further contextualise this chapter, Table 21.2 offers two case studies that depict sample career paths in digital allied health informatics.

Table 21.1  Allied health informatician career themes and sub-themes summary Themes 1. Educational background

2. Career history

3. Unique attributes of the digital allied health informatician 4. A future view of the allied health informatics workforce

Sub-themes 1.1 Interest in the sciences 1.2 Desire to help people 1.3 Big picture thinking 1.4 Pursuing further education and credentialing in informatics 2.1 Careers paralleling advances in digital health 2.2 Experience with digital health quality improvement and service redesign projects 3.1 Personal advancement and life-long learning 3.2 Analytical curiosity 4.1 Opportunity to advance and transform practice supported by digital health technologies 4.2 Essential role of the allied health professional (leadership and engagement) in digital health 4.3 Change management 4.4 Maintaining a patient-centred focus

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In the educational backgrounds of AHPs who move into the field of digital health, there are consistent threads. One is interested in the sciences. AHPs curiosity about the STEM disciplines (science, technology, engineering, mathematics) goes as far back as high school, especially biology, chemistry, physics, and psychology, with some also interested then in information-communication technology (ICT) and information systems. “Science and technology have been constant gravity wells.” (Author KD). Another such thread is desire to help people. We collectively identified a common theme that spurred all of us to pursue our careers, the interpersonal nature of being a practicing AHP. This was sometimes augmented by an AHP having prior experience with receiving healthcare treatment or with sharing in the health journey of a loved one. At the intersection of health informatics and AHPs are “big picture” thinkers, a macro-level view of health and wellbeing. We all agreed that positive change requires an appreciation for the role of the patient in their own care (participatory health) and across their continuum of care and a willingness to embrace evidence-­ based practice grounded in sound research. “Insights…about the performance of health systems…helped me to recognise the value of big picture thinking in advancing health beyond one-to-one clinical care.” (Author DC). While much of AHPs’ journey into digital health is work-based or experiential, we found that people interested in this area will take it upon themselves to pursue further education opportunities related to digital health and informatics—continuing professional development modules, graduate coursework, doctoral studies. “I pursued my PhD… This was a way for me to combine my knowledge of clinical healthcare, with an interest and foundational knowledge of ICT from my earlier years.” (Author MM) In the absence of a formal clinical informatics training pathway, some proceed to gain credentials such as the Certified Health Informatician Australasia (CHIA) or international equivalents; some apply to have their years of experience and education recognised in Fellowship status in a professional college in Health Informatics (MM, KM and YP are Founding Fellows of the Australasian Institute of Digital Health). “…There were no academic or formal education channels to prepare me for my work in this field.” (Author KM). The career development of AHPs who move into the field of digital health parallels the advances in digital health and involves project-based experiences. A common theme in our personal journeys in AHI is our evolution alongside that of digital health. We have noted a paradigm shift from traditional patriarchal healthcare structures towards participatory healthcare, in part driven by increasing ubiquitous computing and digital connectedness (i.e. Internet and online information availability, rising prevalence of smartphones, mobile health and remote monitoring). For those of us in the vanguard, digital health practice meant being early adopters and willing to embrace innovation to support how we work; for those only just entering the digital workforce now, it means an opportunity to drive forward cutting edge AHP practice utilising advances in ICT. In career journeys in allied health, AHPs past and present may recall personal involvement with service- or quality-improvement projects. Whether working in a

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Table 21.2  Two case studies of careers in digital allied health informatics Case study #1 Physiotherapist clinician; clinical lead for EMR implementation; large metropolitan hospital Domain – Interest in science and technology Education background and growing up preparation for – Undergraduate studies in marine and biological sciences allied (digital) – Pursued degree in physiotherapy to health capture the opportunity to combine interest in life sciences with communication and people skills, critical thinking, and ability to add value to people’s lives – Career in public tertiary and Work history: role(s), why it is rehabilitation hospitals interesting, the – Started career in paper-based clinical records systems; technology was siloed how/what of and scarcely used, for results look-up building knowledge and and email – Worked in various clinical teams over skills in their the years as technology outside of the area hospital rapidly evolved; web-based clinical applications slowly began to emerge, allowing better access to patient information and requests, plus it created the opportunity for digitally literate clinicians to start innovating and improving systems – Expansion of role into clinical quality improvement projects (continued use of clinical skills and learning new skills; i.e. project management, problem-solving, system-level thinking, clinical workflows, process mapping, data analysis, human-factors) – Developed into role as an allied health educator; teaching a range of allied health professionals about clinical information systems and health ICT – Eventual role as clinical lead in an EMR implementation, a newly created role

Case study #2 Dietitian; clinical background, now in academic role – Graduated as a Dietitian – Six tertiary degrees; bachelor degree, two graduate certificates, two masters degrees, PhD

– While working as a dietitian and doing doctoral research, I wanted to challenge the key premise about how dietitians worked; this led to upskilling in informatics – Research focused on informatics in dietetics to bridge the gap between clinical care and information science/ICT) – Career experience in hospitals, primary care, the food industry, consulting, and universities – Long research career in clinical and translational studies – Very few informaticians working in the nutrition field (unique skillset in clinical healthcare and informatics provided a pivotal bridge between both areas) – Created a virtual centre for nutrition informatics research – Also experience as a patient (lived experience and merging of personal and professional research interests) – Keen interest and observations of how health professionals document their notes; spurred desire to trial digital systems, data capture forms, and electronic documentation in own practice – Much of early journey was self-directed; including forming collaborative networks with like-minded professionals – Own journey paralleled advancement of digital technology into allied health systems (continued)

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Table 21.2 (continued) Case study #1 Physiotherapist clinician; clinical lead for EMR implementation; large metropolitan hospital Domain Future of allied – A real opportunity to take more leading roles in informatics and digital health health, particularly in hospitals. informatics: Driving factors behind this include; where do you many of us work in interprofessional see the discipline going teams with a good understanding of the roles and skills different professions in the future? bring, expertise in person-centred care, and career paths for allied health are generally more constrained; giving rise to branching out and expanding scope.

Other thoughts

– For a long time, digital health and informatics for allied health professionals has been challenging to get involved in (partly workplace and/ or network dependent) – Career paths for allied health professionals in informatics have not been clear – The digital health community would be well advised to bring greater attention to this area to allied health professionals – The allied health workforce is an untapped resource in digital health

Case study #2 Dietitian; clinical background, now in academic role – Informatics will not hinder, but rather improve efficiencies within the various professions to allow us to use our health training and communication skills more effectively – Expect to see health informatics become more ingrained in practice from the beginning of the professional career journey – The time frame of these cycles of acceptance will begin to decrease; in part due to the parallel changes occurring within society (i.e. with our patients/clients) – We should continue to challenge ourselves and take the next step – Early fear was transformed to passion and a need to advocate for something I continue to feel very strongly about

public hospital system, community care setting or private practice, because of our intimate knowledge of the patient journey, care planning and coordination, AHPs are frequently called upon to take leading roles in major projects to transform practice such as EMR implementations, development of portals and dashboards, telehealth service redesign. “While the electronic systems worked, in some cases the fall back of paper methods were still heavily ingrained in the hospital environment.” (Author YP). We are able to pinpoint systems or processes that are fragmented or working inefficiently and are able to accurately identify areas for improvement, e.g. “ability to prioritise at risk patients accurately was dependent on subjective and often inaccurate information.” (Author KM). As a result of our roles in major projects, we are often well-placed to demonstrate the benefit of digital tools and services and act as a conduit between clinical colleagues, ICT teams, and senior executives in helping to ensure major digital health projects are a success. “These skills honed along my career have allowed me to add value to multiple clinical change programs and projects.” (Author KD).

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The digital allied health informatician has unique attributes, the first of which is personal advancement. AHPs share an intellectual curiosity for progress and self-­ determination; this includes a personal desire to excel. We identified an “Aha moment,” when AHPs discover and pursue digital health and informatics as an area of fascination, historically down a path less trodden. “I found that we were speaking two different languages which at times slowed progress... my focus was to learn the ‘language’ of informatics.” (Author YP) It runs through to a desire to stay ahead of technological advancements that can improve care outcomes and clinical efficiencies (e.g. telehealth solutions and remote monitoring for improved access to care). We have pursued greenfield opportunities beyond the realm of traditional allied health practices whilst balancing a diverse portfolio of work in the clinic, research, teaching, health system administration and industry. Analytical curiosity occurs in conjunction with this desire to embrace solutions at the cutting edge of practice, AHPs are by nature analytically curious about how to support evidence-based practice. AHPs may naturally gravitate towards the digital health informatics sciences because of the innate ability of technologies to support intelligence gathering and processing remotely, which is frequently in the scope of work of an AHP. For example we want to use analytics techniques and the growing field of artificial intelligence on big data that is collected away from the clinic to inform patient progress and care plans. “…continue to leverage technologies...to inform patient progress and clinical metrics outside of the traditional clinical environment and how technology can assist to analyse big data to predict patient outcomes” (Author MM). The future for the Allied Health Informatics workforce is influential and important. The benefits of incorporating informatics and digital initiatives into usual care practices continue to compound and resonate across the allied health disciplines to improve the efficiencies in the way that we currently work. Access to electronic patient data, for example has enabled AHPs to tap into data analytics capabilities for prioritising caseloads, managing workflow, reviewing intervention outcomes as well as to allow for clinical audits to be conducted rapidly to inform service improvements. AHPs have a key role in influencing the integration of digital health and ensuring that the utility and delivery of system support standards and terminologies align with our professional practice. Furthermore, there is a need to ensure that the AHP workforce is sufficiently equipped to best utilise the electronic solutions that are available or drive the development of electronic solutions to support best practice. “Allied health has a key role to play in the integration of eHealth, ensuring the delivery of these systems support standards and terminologies to their professional practice” (Author KM). In teaching and training future clinicians, we observe that student willingness to engage with artificial intelligence, sensor, mobile, and even ubiquitous methods of assessment has been high. There is a shared view that the transformation of care practices supported by digital health technologies could be further bolstered by the infusion of digital health curricula into standard clinical teaching. We can see an essential leadership and engagement role for allied health professionals in digital health. There is a well-known ceiling effect for conventional career

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trajectories for AHPs practicing within their specialised clinical field. This could be the impetus in the future for experienced AHPs—given our exposure to working in interprofessional teams and our common understanding of the roles and skills which different care providers bring to the clinical management of patients—to take leadership opportunities in furthering the digitisation of health within the wider arena of the healthcare system. AHPs can lead all the way from the design and development of digital and informatics solutions through to the generation of research evidence to support implementation in clinical practice. Our observations about change management suggest that the evolution of the digital health informatics workforce within allied health has arguably occurred as cycles of acceptance rather than a linear progression towards our current state. The adoption of technology in healthcare was initially met with resistance and could be attributed to a fear of the unknown or fear of losing jobs to technology. We have found that the number of early adopters has tended to increase with the diffusion of knowledge and information about digital technology use amongst the clinical community; this, in turn, has activated a growing sense of urgency to integrate informatics into practice. The momentum of these cycles of acceptance has continued to build over the years, ultimately leading to the formal recognition of health informatics as a discipline in its own right. “Acceptance generally appears to be driven by urgency while the number of early adopter groups were evidently increasing in number and size.” (Author YP). There is an expectation that these cycles of acceptance and change might occur more rapidly throughout healthcare as AHPs acclimatise to the complementary role that digital health technology has in standard clinical practice. In addition to technological acceptance, we recognise that life-long learning can ensure a smoother transition as new ways of working continue to arise with new technological innovations and new digital information flows. Thus not only is there a need to assimilate digital health and informatics within standard university curricula to prepare the future health informatics workforce but as well as it is a priority to continue to provide professional development initiatives for AHPs in the current workforce, “to the point that we essentially stop referring to the ‘digital’ in ‘health’.” (Author MM). Maintaining a patient-centred focus in the fast-evolving age of digital technology accompanies the growing expectation that individuals will engage with and manage their own health with information technologies across a range of mediums and scenarios. AHPs are generally invested in the life course of individuals and hence need to be adaptable in different digital environments across the continuum of care and over the patient journey. From our perspective as healthcare providers, however, it is folly for technology to overshadow the clinician-patient interaction or to allow it to diminish the patient experience. Patient-centred care, being respectful of an individual’s preferences, needs, and values, has been instilled in AHPs as a cornerstone of practice. In the participatory health paradigm, the clinician strives to uphold patients as the focus of healthcare by engaging with them as equal partners in the process of shared decision-making towards managing their own health. The growing shift towards participatory health also provides an avenue for patients to influence the design of digital tools and solutions; we see that these, in turn, could strengthen patient engagement with their own care as well as improve the patient

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experience. “Basic premise of care for the patient should always remain at the cornerstone of our interactions.” (Author YP).

Implications and Recommendations The themes uncovered and discussed in this chapter (education steps, career pathways, unique attributes, and future perspectives) speak to the existing and future allied health workforce and provide them with a compass to navigate their careers in an increasingly digitised world. The field of allied health poses challenges due to its heterogeneity while at the same time it affords unique opportunities to drive the digital information workforce forward. The advancement of the digital allied health informatics workforce is driven from the desire for the digital enablement of better data collection and synthesis to improve health care and reporting. Success will require addressing the substantial data and information silos created across the various allied health disciplines, as well as greater attention to interoperability challenges created by multiple discrete connected devices and systems. This workforce can bring about change from the push to provide evidence of outcomes to support funding of healthcare and to incorporate digital into models of care to improve access and empower clients to engage in their own care. Coordinated and concerted efforts need to go into advancing both the formal and informal channels for education in digital health information for allied health professionals. Also required is attention to the development of formal career pathways that included leadership roles for AHPs in digital health information. For this work to progress, further research needs to be undertaken to understand better and manage resistance to change in this space. Developing and harnessing a digital allied health information workforce will deliver benefits for clients in improved health outcomes, satisfaction with care, participatory health, and safety of care. It will deliver benefits for allied health practitioners regarding better access to data, data quality improvements, workflow efficiencies, career, and leadership opportunities. A stronger allied health information workforce is a stronger basis for sound evidence for health funding, and sound health system performance, and a culture of innovation in care.

References Anderson R.  Thematic Content Analysis (TCA): descriptive presentation of qualitative data. Institute of Transpersonal Psychology: Palo Alto, CA; 2007. Benoot C, Bilsen J.  An auto-ethnographic study of the disembodied experience of a novice researcher doing qualitative cancer research. Qual Health Res. 2016;26(4):482–9. Butler-Henderson K, Dalton L, Probst Y, Maunder K, Merolli M. A meta-synthesis of competency standards suggest allied health are not preparing for a digital health future. Int J Med Inform. 2020;144:104296.

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Chang H. Autoethnography in health research: growing pains? Qual Health Res. 2016;26(4):443–51. Coiera E. Why e-health is so hard. Med J Australia. 2013;198(4):178–9. Fridsma DB.  Health informatics: a required skill for 21st century clinicians. BMJ (Clinical research ed). 2018;362:k3043. Fridsma DB. Strengthening our profession by defining clinical and health informatics practice. J Am Med Inform Assoc: JAMIA. 2019;26(7):585. Gray K, Choo D, Butler-Henderson K, Whetton S, Maeder A. Health informatics and e-health curriculum for clinical health profession degrees. Stud Health Technol Inform. 2015;214:68–73. Greenhalgh T, Wherton J, Shaw S, Morrison C. Video consultations for COVID-19. BMJ (Clinical research ed). 2020;368:m998. Health Informatics Society of Australia (HISA). Allied health professionals: the untapped potential in digital health – Position Statement. Melbourne; 2019. Houston ML, Yu AP, Martin DA, Probst DY. Defining and developing a generic framework for monitoring data quality in clinical research. AMIA Annu Symp Proc. 2018;2018:1300–9. Maunder K, Walton K, Williams P, Ferguson M, Beck E. A framework for eHealth readiness of dietitians. Int J Med Inform. 2018;115:43–52. Maunder K, Walton K, Williams P, Ferguson M, Beck E.  Strategic leadership will be essential for dietitian eHealth readiness: a qualitative study exploring dietitian perspectives of eHealth readiness. Nutr Diet. 2019;76(4):373–81. NHS. Allied health professions into action: using Allied Health Professionals to transform health, care and wellbeing. England: NHS; 2017. NHS. Clinical informatics and digital delivery in health and care: a career framework for nurses and allied health professionals. England: NHS; 2018. NHS. A digital framework for allied health professionals. England: NHS; 2019. Philip K.  Allied health: untapped potential in the Australian health system. Aust Health Rev. 2015;39(3):244–7.

Chapter 22

Working as a Medical Informatician Daniel Capurro, Rebecca Grainger, and Daniel Luna

Abstract  Medical professionals frequently have to forge their own paths to achieve proficiency and learn the core components of clinical informatics, understood as the “application of informatics and information technology to deliver healthcare services” [American Medical Informatics Association (Clinical informatics. 2021. https://www.amia.org/applications-­informatics/clinical-­informatics)]. In this chapter, we present the personal stories of three medical doctors, from different parts of the world, and what motivated, guided, and inspired their journey. Several common themes emerge. The first is self-directed learning, strong mentorship, and the need for the on-going building of communities of practices to share experiences and knowledge. Secondly, there is strong desire to formalise learning during our careers, and we found different ways to achieve this. Finally, health informatics is more about health, clinicians, healthcare and patients and less about informatics, and clinical informatics sits at the core of clinical care. Although the routes and timing of these journeys varied, resonating throughout is a deep desire to impact patients’ lives and improve the ways we provide healthcare. Keywords  Case study · Clinical informatics · Chief Medical Information Officer · Mentorship · Community of practice

D. Capurro (*) University of Melbourne, Melbourne, Australia e-mail: [email protected] R. Grainger University of Otago, Wellington, New Zealand e-mail: [email protected] D. Luna Hospital Italiano de Buenos Aires, Buenos Aires, Argentina e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_22

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Daniel Luna: Hospital Italiano, Buenos Aires I am currently the Chief Information Officer (CIO) of a private hospital in Buenos Aires, Argentina. Hospital Italiano de Buenos Aires (HIBA) is a non-profit medical academic centre established in 1853. It has a network of 2 hospitals with 750 beds, 41 operating rooms, 800 home care beds, 25 outpatient clinics and 150 associated private practices located in Buenos Aires City and its suburban area. Since 1998, HIBA has been running an internally developed health information system, which encompasses both clinical and administrative data. Recently, we achieved the Health and Information Management System Society (HIMSS) Electronic Medical Record Adoption Model (EMRAM) certification at Level 7, the highest level possible. When I was 17, my father suggested that there were no physicians in the family, so I decided to become a doctor. I went to Buenos Aires University. During my time in medical school I held different jobs, including in a public library, as an electrical technician, even as a DJ in a famous discotheque, which I loved. Having two or three jobs allowed me to be completely independent of my parents, which was pretty exciting at the time. I always thought that I would have certainly found passion in many other activities too. After graduating from university, I decided to become a practising clinician. Someone recommended me to train at HIBA; I have been there ever since. I have worked as a clinician for more than 20 years and, as a primary care physician, I have had more than 900 patients under my care. In daily practice, I could experience first-­hand the doctor’s information needs in the decision-making process. I also realised that data integration for public health was a complex fundamental essential requirement. During my internal medicine residency, we were required to prepare training resources for our colleagues. Usually, I could use slide presentations as graphic resources, but I felt that this was not enough. For me, one of the biggest challenges was how to treat diabetic ketoacidosis. At that time, I created a spread sheet containing formulas for the standardised and systematic treatment of these patients. That was my first contact with informatics tools. It was also during my residence when I was asked to join a team of local researchers. That is when I met Doctor Fernán Quiros, a real mentor, who has had the most significant professional influence on me. Among the many projects we were engaged in, the most complex one was addressing the need for an electronic medical record. I thought of this as a personal challenge. After evaluating different commercial products worldwide, we proposed an in-house-development project. It was then when I started to study the field of medical informatics on my own. I often searched for articles related to implementation experiences, participated in conferences, and began publishing scientific papers on the topic. I spent many summer vacations reading the proceedings of the American Medical Informatics Association (AMIA) Annual Symposium while at the beach. We began to slowly design an electronic health record based on our daily work. While working as a clinician, I would think of possible functionalities, and I would ask the system

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engineer to develop them. I became a tester and a bug reporter. In this way, I actively participated in the iterative cycle of software development. After years of working for free and teaching myself about medical informatics, the Department of Health Informatics was finally established at HIBA in 2000. I was responsible for leading the development and implementation of the institution-­ wide Clinical Information System and served as Chief Medical Information Officer for 10 years. We created and coordinated the Medical Informatics Residency at HIBA to train clinicians in the discipline. To date, it is the only one of its kind in Latin America. Soon, I realised that I needed formal training, as I was missing the latest knowledge—on security and infrastructure issues for example. I began a master’s degree and a PhD. This was a significant challenge since, at that time I was married, and we had three children. I completed a Master of Science in Engineering of Information Systems at Universidad Tecnológica Nacional in 2016. The following year I obtained my PhD in Informatics Engineering at the Technological Institute of Buenos Aires. At that time, I became a HIMSS Certified Professional in Health Information Management Systems, and in 2020 I was named an Independent Researcher of the National Scientific and Technical Research Council (CONICET) of Argentina. As a Chief Information Officer my constant challenge is to nurture an innovative, high-performance team of professionals who can develop local and regional leadership. I consider that a big part of that has been achieved. Today, after 20 years of working in the health informatics department, 10 years as a CIO, and visiting 38 different countries around the world because of my professional activities, there are two key lessons I have learned. First, health informatics is not only about computers, monitors or mobile devices. It involves information systems: information workflow in health organisations, and information needs of both healthcare providers and patients. Devices may change over time, but the information system will always exist and always be the necessary foundation. Second, health informatics is not different to any other clinical specialty; it is an opportunity to change a patient’s life. In this case, the discipline has allowed me to change hundreds and maybe thousands of patients’ lives at the same time.

 ebecca Grainger: Hutt Hospital and University of Otago, R New Zealand My formal journey in health informatics started with an informal conversation in a corridor. I was known by my clinical rheumatology colleagues in a New Zealand public hospital and academic colleagues at a university as an early adopter of technology. Our patients and other stakeholders were increasingly wondering why the “health system” did not adopt more flexible and user-friendly ways of interacting— mobile technology and web-based. One lunchtime a colleague asked me “Rebecca, you are into technology, would an app be useful in patient-led management of

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rheumatoid arthritis?” I was intrigued by the idea but being “into technology” did not extend much past Twitter, Evernote and a few useful apps on my phone. I had no formal, or even informal, training or education in anything that seemed remotely related to the emerging field of digital health. This suddenly seemed untenable in a world where digital was the direction of travel for all business and social aspects of life. Thus began a new direction in my career—into health informatics. Now, 5 years later, much of my research focuses on aspects of health informatics and I contribute to the local information and communications infrastructure development in my hospital. After that conversation I looked for every opportunity to learn and upskill in health informatics. Knowing myself and theories of adult learning, I found communities of practice where I could learn with and from others. Joining Health Informatics New Zealand and attending every seminar, workshop and conference provided insights into the current status of, challenges to, and trends in the digital health landscape in New Zealand. This also provided some base knowledge in the principles and practice of health informatics as a discipline. However, I was aware my knowledge was patchy. The health informatics competencies framework developed for the Certified Health Informatics Australasia (CHIA) (2013) certification provided me a comprehensive and regionally relevant resource to understand the knowledge and skills necessary for competent practice. My undergraduate and postgraduate medical education had already provided ample training in relevant aspects of scientific skills, health science and human and social factors but little in aspects of health informatics, information and communication technology, information science or management science. I, therefore, directed my learning to these areas, using recommended readings from the CHIA certification and any in-person or online learning that I could access. This included completing several MOOCs—massive online open courses—from top international institutions. This organic and self-­directed learning may not be ideal, and I would recommend that others explore formal tertiary offerings in health informatics, however with significant work and family commitments, formal studies were not an option for me. After my knowledge increased, I registered for the CHIA certification and successfully passed the rigorous examination. Using the CHIA competencies, I established an on-going professional development plan in health informatics which meant I was able to recertify after 3 years. My research activities, mainly focusing on technology in supporting self-management in chronic conditions and medical education, keep my knowledge up to date, along with reading key health informatics journals regularly and attending regional health informatics conferences. A key motivator of my health informatics research theme is to provide increased knowledge about how technology can enhance and improve health outcomes, without assuming that a product will always be the answer. A key theme of my research has been a focus on the people and processes around the product—these are often overlooked. It has been possible to apply research skills developed in biomedical and health sciences to health informatics, however research methods are sometimes less well defined and challenges, such as the ethical considerations of research in social media, abound. My clinical work has been influenced by my health informatics journey too. For health professionals in 2020, the use of information and communication technology

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is an everyday part of clinical care. Within my clinical service, I have contributed to the development of information, communication and technology (ICT) tools for management of team processes and patient care. Within my District Health Board (providers of government-funded health and disability services for a local population in New Zealand) I contribute to committees which address practical and governance aspects of ICT developments. I have found that the multidisciplinary nature of these committees continues to enhance my understanding of how interdependent different parts of the health system are, which is highly evident when focusing on shared electronic tools, patient care and staff workflows. Most recently, my clinical, academic and health informatics roles and skills provided a unique and meaningful way to contribute to the global response to COVID-19. In early March 2020, one of my online communities of practice, the Rheumatology community on Twitter, identified that the COVID-19 pandemic was likely to have a particular impact on the patients we serve. This potentially included increased vulnerability to infection with or outcomes from COVID-19, and reduced access to pharmacological treatments, which were being disrupted by a rush to use hydroxychloroquine for COVID-19. This Twitter community responded by forming a virtual team collaborating online, the COVID-19 Global Rheumatology Alliance, who rapidly developed a global disease registry and an international patient registry, facilitated via social media. This international initiative leveraged knowledge and skills across all six competency streams of health informatics, showing that in 2020 health informatics is really just about health.

Daniel Capurro: University of Melbourne As happens with many clinical informaticists, I stumbled upon this career through chance; the path has been winding and mentorship has been fundamental in understanding a field that was still defining itself. I trained as a medical doctor (MD) in Chile, where medical education is a 7-year program that includes clinical internships. Halfway through my training—in 1998—I met a senior clinician who, after a quick chat, suggested I read Learning Clinical Reasoning, a book by a former Chief Editor of the New England Journal of Medicine (Kassirer and Kopelman 1991). Being able to understand the diagnostic process explicitly, and quantify how each symptom, physical finding, and laboratory test could contribute to structured reasoning about the probabilities of disease, was transformational. Immediately, it became apparent that we, clinicians, were making clinical decisions with uncertain information; we were using data about what we remember, about what we had experienced in the past, subject to our individual biases. There had to be a better way. Those were the early days of the Internet, Amazon was already around, and I ordered a copy of Ted Shortliffe’s Medical Informatics (1991). After graduating with my MD, it was time to decide my next steps, but there weren’t many people to ask about medical informatics (as we called the field back

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then). It was hard then, as it is still today, to explain what a health informatician is or does; a few academics suggested talking to the School of Medicine’s webmaster; that conversation didn’t go very far. My interest in quantitative decision-making pushed me towards Evidence-Based Medicine (EBM) where I learned how health informatics, the biomedical literature, and—overall—the whole problem of knowledge translation are deeply connected. With a significant proportion of our time as clinicians spent searching, organising and generating information, healthcare has become an information discipline. I finally decided to complete my medical training in internal medicine, where the spectrum of clinical decision-making would be fully expressed, and I would obtain a broad perspective on how clinical care really worked. During the residency years, I met lifelong mentors from whom I learned about patient care and rigorous decision-­making. My understanding of health informatics was still peripheral. With no training programs in Chile, I moved to the USA, supported by a Fulbright Fellowship and the Chilean Ministry of Education. I enrolled in a PhD in Biomedical and Health Informatics at the University of Washington. The PhD years were spent learning about the shortcomings of clinical informatics and the need for sound methods to improve our ability to reuse routinely collected clinical data for secondary purposes such as quality improvement and research. After the PhD, I returned to Chile and began working at a university healthcare network consisting of two hospitals (approximately 600 beds) and 11 outpatient clinics. I became an advisor to the clinician who was leading the implementation of a new electronic medical record. Shortly, I was asked to step in as the network’s first Chief Medical Information Officer. Only then I realised how much I did not know about clinical informatics. Juggling my research on digital phenotyping in parallel, I became forcefully exposed to concepts I never learned during my medical training nor the PhD. Project management, change management, clinician engagement, software quality assurance, stress tests, development environments, contract management, 24/7 on-call duties and so on. I found some comfort in reading “All systems down”, an article describing a major system crash that happened at Beth Israel Deaconess, in Boston, back in 2002 (Berinato 2003). If you work in clinical informatics and haven’t read it, you should. But even more relevant than all the IT stuff I learned during those years, the most significant learning came from working with nurses. None of them had a formal education in clinical informatics—they had all learned a lot by doing— but they all had a deep understanding of clinical workflow, patient-centred care, and the clinical risks of incorporating technologies into clinical care. Just as nurses’ perception of a patient’s gravity can be a powerful predictor of adverse events (Romero-Brufau et al. 2019), I experienced first-hand their ability to detect risks imposed by new technologies. It was a humbling and productive period, certainly not pain-free. We were able to create the Clinical Informatics Unit and make it part of the organisational chart and slowly started to incorporate clinical informatics into crucial decision-making processes inside the organisation. Together we learned about the perils and consequences of embarking on implementing a new electronic medical record when it is not an explicit part of the strategy. Before spending so

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much energy and resources, clinical organisations must ensure that it perfectly aligns with its mission, vision and values. Return on investment is not a useful metric to assess such an endeavour. I spent almost 4 years as CMIO before moving to Australia to pursue a full-time involvement in health informatics research. During those years, I had the privilege of working with colleagues in other Chilean universities and, with them, founded the National Centre for Health Information Systems. I recently joined the University of Melbourne School of Computing and Information Systems; I believe I am the only medical doctor among the academic staff of that School. I feel that the impostor syndrome has reached new heights as a result of me being among people who know so much about things of which I know so little. On the other hand, I believe that my combined 15 years of clinical experience and the time I spent as CMIO helps provide a different perspective and understanding to those who conduct research in digital health. There is still a long way to go. I still struggle to explain what I do to people unfamiliar with health informatics, but it feels like we are now at a turning point. The COVID-19 epidemic forced everyone, including patients, to rethink the way we seek and deliver healthcare. Health data analytics and predictive models are in the news daily. It is time to embrace that healthcare is an information discipline and health informatics is at its core.

References American Medical Informatics Association. Clinical informatics. 2021. https://www.amia.org/ applications-­informatics/clinical-­informatics. Accessed 17 Feb 2021. Berinato S.  All systems down. Computerworld. 2003. https://www.computerworld.com/article/2581420/all-­systems-­down.html. Accessed 29 Jul 2020. CHIA.  Health informatics competencies framework. 2013. https://www.healthinformaticscertification.com/wp-­content/uploads/2016/02/CHIA-­competencies-­Framework_FINAL.pdf. Accessed 28 Jul 2020. Kassirer JP, Kopelman RI. Learning clinical reasoning. Baltimore: Williams & Wilkins; 1991. Romero-Brufau S, Gaines K, Nicolas CT, Johnson MG, Hickman J, Huddleston JM.  The fifth vital sign? Nurse worry predicts inpatient deterioration within 24 hours. JAMIA Open. 2019;2:465–70.

Chapter 23

Working as a Nursing and Midwifery Informatician Karen Day, Sally Britnell, Lisa Livingstone, Abin Chacko, and Karen Blake

Abstract  Information is integral to the work of nurses and midwives, as they gather, analyse and use data to perform clinical activities, keep private information confidential, and support the healing of those for whom they care. Evidence-based care is fundamental to their practice, and information management is part of their training, to varying degrees. Once they have completed their training, some nurses and midwives augment their professional profile and become information specialists. Nurses and midwives are the largest component of the clinical workforce, so possibly nursing and midwifery information specialists are the largest invisible subgroup in the health information specialist workforce. In this chapter we explore the history of nursing and midwifery informatics, how it has been incorporated in training, and what nurses are doing now. Five narratives from practitioners in Australasian health care systems describe their identity, the impact of their work (now and yet to

K. Day (*) School of Population Health, University of Auckland, Auckland, New Zealand e-mail: [email protected] S. Britnell Nursing Department, School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand e-mail: [email protected] L. Livingstone Nelson Marlborough District Health Board, Nelson, New Zealand e-mail: [email protected] A. Chacko Waitemata District Health Board, Auckland, New Zealand e-mail: [email protected] K. Blake Office of the CCIO, healthAlliance NZ Ltd, Auckland, New Zealand e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_23

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be seen) and their role in creating and implementing innovations to improve the care they and their colleagues deliver. Keywords  Case study · Nursing · Midwifery · Clinical informatics · Nursing informatics

Introduction Nursing and midwifery take on many forms, but this field is essentially about care, advocacy and assisting people through life and health transitions (Meleis et  al. 2000). A great deal of this caring relies on a broad range of skills and activities that are based on co-ordination with other clinicians (from the multidisciplinary team), safe processes that enhance health outcomes, and reliable and trustworthy information systems to support the information needs of those both providing and consuming nursing and midwifery services (Nursing Council of New Zealand 2017). The nursing workforce accounts for 59% of the total health professional workforce globally (World Health organization 2020). If nursing is the largest group of the health workforce, then nursing and midwifery informaticians are the most invisible subgroup amongst the Health Informatics, Digital, Data Information and kNowledge (HIDDIN) specialist workforce. This is despite the fact that nursing informatics competencies have been clearly documented over the years (Hübner et al. 2018), especially since the inception of Technology Informatics Guiding Education Reform (TIGER) in 2004. Florence Nightingale, a statistician, a trained nurse, and famous for her work in the Crimean War, is the first known nursing information specialist (Brixey et al. 2020). Her own claim that her work with data would not become the norm in nursing for another 150 years was prescient. The earliest signs of digital nursing informatics as a specialisation appeared in the late 1960s when electronic health records were just beginning to emerge from the medico-legal records of clinicians (Honey et al. 2020). Paradoxically, the development of information systems in the nursing and midwifery workplace has been slower than for medicine. Nursing information needs are in some ways different from, and in other ways similar to, those of other clinicians, e.g. laboratory results are shared by the whole interdisciplinary healthcare team, but nurses need nursing-specific data (e.g. to calculate patient acuity in order to map nursing resources appropriately), the automation of certain activities (e.g. vital signs measurements), and support for evidence-based nursing care. A survey during the 2020 International Year of the Nurse and the Midwife documents dynamic changes in nursing information specialist roles in the development, implementation, and optimisation of electronic medical/health records, nursing clinical documentation, point-of-care clinical decision support, and computerised practitioner order entry (HIMSS 2020). The case studies in this chapter describe the career journeys of five nursing information specialists in New Zealand and Australia. They outline how their early nursing or midwifery identities

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were expanded or defined by information specialisation, and what they were able to achieve in terms of impact and innovation.

Sally Britnell, Auckland University of Technology, New Zealand The theme that runs through my narrative is improving patient care by doing a ‘defrag’. All my life I have been known as someone curious who strived to help others to be the best that they could be. After doing a post-secondary overseas experience year and completing a Diploma in Recreation Management, I started my journey to becoming a registered nurse as a mature student in 1994. I completed my degree at a time when nursing informatics was just beginning to emerge, but as a new graduate nurse, my focus was on learning to become a nurse, which meant that innovation and incorporation of informatics did not appear on my radar immediately. I was privileged to marry a man who was a researcher and developer in a successful software company, which reignited my passion for data and technology. I coupled this with my keen interest in improving what we do through streamlining processes and I was always tinkering with things that made my job as a registered nurse more effective and agile. What I ran up against in the healthcare system was a series of solutions that solved parts of problems, but these were not united, and although this tendency has improved, the siloed approach is still evident in practice today. At that time, the nursing workforce in New Zealand was not using health data or technology on an everyday basis to improve patient care. In the late 1990s, I worked as a Practice Nurse in a low socioeconomic status area. The immunisation statistics at the practice showed that 6.6% of eligible children were immunised. It seemed hard to believe that the families of the remaining 93.4% of children chose not to immunise. The MS-DOS-based computer system used at the time included a recall system via printing letters and making phone calls. I found that patient data was missing or incorrect in many places, coupled with many households having low health literacy and no access to a phone, which made follow-up challenging. It was this experience that showed me the importance of accurate data in providing optimal healthcare. I made it my mission to collect accurate data and implement strategies such as spending time in the waiting room talking with families and getting to know their challenges, thus allowing more open communication, all the while updating patient records using the information I found out, which led to an immunisation rate of over 60% after 1 year. These challenges prompted me to study Computer Science at the University of Auckland while working part-time as a practice nurse. Although I didn’t finish a formal qualification at this time, I learned what was possible. I used my new skills to develop software for tracking medical staff for a volunteer organisation and other smaller projects. These included assisting with an Installfest (an event where experienced users help others install a computer operating system) for the Engineering Department at the University of Auckland, speaking at a conference (giving an

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introduction to Linux and promoting Linux at a Computerworld event) and assisting in running a Quake Server (an online multi-player game) at an Armageddon Expo. In 2004 while working as a registered nurse in a large District Health Board (DHB) in Auckland I again ran into siloed systems and a culture of very controlled use of technology. For example, nurses across a department were not allowed email addresses or a computer login of their own; instead, they shared a department login to look up patient information, lab results, and X-rays. A DHB could not digitally share much information, such as X-rays, blood results, and patient alerts, making patient care fragmented on a national level. It was at this point that I began to question why the systems and technology were not fit for purpose. I knew what was possible using data and technology but was unable to implement this in the role I had. I tried to discover why processes and systems were siloed and whether it was the healthcare system, or the DHB, or the advancement of technology, that was making integration impossible. I changed roles to become an infection control nurse specialist, where I enjoyed and valued the use of data to improve patient care. In 2011 I moved into academia, allowing me to research, educate future nurses, and ultimately change and streamline their practice. I gained a Master of Health Science including a thesis, completing many small projects along the way in nursing informatics and online learning. I became known for innovation and investigating emerging technologies and became a part of the health informatics community in New Zealand and internationally. In 2015 I began my PhD in Computing and Mathematics, where I could finally combine my nursing knowledge and passion for streamlining processes using technology. My PhD project modelled the New Zealand Health Survey data and used this in the design and development of a mobile application for clinical decision making in paediatric resuscitation (MOH NZ 2020; Britnell 2020). My informatics journey continues through doing research, inspiring future nurses, and working in a team developing informatics guidelines and resources to support nursing informatics in New Zealand. It has taken 25 years to reach an expert position where I can pass on my passions, curiosity, innovation, technology, and nursing to improve patient care. I plan to continue challenging the status quo to allow us to work smarter in providing healthcare for New Zealanders.

 isa Livingstone, Nelson Marlborough District Health Board, L New Zealand Advocating for clinician-friendly information systems is the theme of my narrative. Nursing is in my blood, the ‘family business’, although I fought the call for a long time. After being accepted into training and turning it down in 1990 I finally graduated in 2002. By this time, I had been very successful in my chosen career of hospitality and I suddenly went from expert to novice overnight. Curiosity has been a strong theme in my nursing career to date; I always questioned why and was never satisfied with the answer, ‘Because that’s how it is done’.

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My constant need to see improvements in healthcare, along with a strong management background, led to my career move into nursing management in 2008. This was where my interest in processes and informatics really began. I could see that clinical staff were struggling to achieve outcomes, not due to clinical abilities, but rather because the information systems they were using to collect data were not set up in a way that supported them. In theory the systems were in place to improve patient flow and support the clinical team to identify barriers, but in fact the systems themselves were the barriers. I worked on these systems, being involved in clinical projects and looking at ways we could work with the technology provided to support and guide the work being done. I never once thought about the fact that I was becoming involved in health informatics; I am not sure whether I even knew then that it was a field in which I possibly could work. Over the next 10 years I developed my nursing career, moving around a number of organisations, always questioning how the clinical informatics systems that we were using worked for the clinician: Were we capturing the right data at the right time and then presenting this to the right people? One organisation where I worked was building a new hospital that aimed to be paperless. I left the organisation before the hospital opened; when I later visited with their newly appointed Chief Nursing Informatics Officer I learned that they did not move into the building paperless, but they were paper-light. We both reflected on the challenges of enabling enough clinical voices to be heard, to ensure change that works for clinicians—but not so many voices that you end up trying to ‘boil the ocean’ and never see any change. In 2018 I moved to where I now work full time in clinical informatics within a large DHB. Initially I was seconded for 12 months to be the clinical lead on a project to implement an electronic observation and assessment system. I finally had an opportunity to work with vendors and clinicians on making sure that the system worked for the clinician not the other way around. I learnt so much during that first 12 months and at the end of the project, with the system successfully introduced into the DHB, I accepted a permanent role within the informatics team. My role is ever evolving which in itself has challenges at times. I am a project manager, sometimes a clinical advocate, and often an interpreter between the clinical and informatics teams. My role is at a strategic level looking at opportunities and working others to set the organisation’s health informatics direction; it also entails working alongside clinicians to bring ideas to life in the organisation. What has not changed in my purpose, and in fact has been cemented, is the need for health informatics to be data driven, patient driven, and clinician-friendly. Too often I have seen clinical applications that silo information, and in turn create silos within clinical teams, and thus create patient safety issues. Another barrier I see within many organisations is the impact of low digital literacy on clinicians’ engagement with the informatics team. Having experienced going from expert to novice several times in my life, I know that unless you approach change with curiosity and willingness you create barriers to learning. I believe that my work has brought down some of these barriers and given clinical teams and informatics teams a better way to communicate with each other. There is still a long way to go, but we are now at least on the same page and talking.

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Abin Chacko, Waitemata District Health Board, New Zealand The theme of my career is walking the floor with digital implementations. In 2009 I migrated from India to New Zealand as a registered nurse. The main difference I noticed in my new ward at a New Zealand metropolitan hospital was the presence of an electronic medication dispensing system called Pyxis. There were a few computers available in the ward, and as a nurse, I accessed the computers in the ward only when required, e.g., to review blood results and radiology reports. The desktop computers were usually occupied by doctors reviewing results or completing discharge summaries. Soon after I arrived, we received the exciting news that the DHB was introducing an electronic workload management system where you could predict a patient’s acuity, analyse the requirement of nursing hours, and document shift notes electronically for handover. Before that, the nursing shift handover was a manual process where you waited for your turn to scribble down notes against your patient’s name on a piece of paper, so that the coordinators could hand over to the incoming shift. The time-consuming handover process involved referring to these notes and ran the risk of missing essential things that may not have been written down on the piece of paper. The new technology was exciting, and I took the opportunity to be a super-user, providing support to new users as part of the implementation team. Post-­ implementation, I noticed a dramatic change in our clinical work: electronic prescribing, smart messaging, electronic bedside patient monitoring and other aspects of care were poured into the nursing workflow. During this time, I completed a Master’s degree in nursing, with a project focus on the nursing leadership role in managing chronic post-surgical pain and incorporating electronic solutions in assessing a patient’s pain. In 2017, I took a role as a clinical coach in a project team managing the implementation of an electronic bedside patient monitoring system where nurses can record vital signs and most of their nursing assessments electronically. I was a bit nervous during the initial days as the workspace was entirely different from what I was used to, and I started hearing new technical terminologies. Many times, it felt like this was not for me; I could not see myself enjoying work away from caring at the patient’s bedside. Going back to the floor of the ward in a different role was not initially comfortable, but the move eventually helped me to listen and understand many things which I had never imagined that my nursing colleagues felt about digital changes. Most nurses felt very optimistic about the digital changes but, at the same time, some nurses shared their concerns in terms of computer skills, time management, and increasing complexity in the nursing workload. I realised that I needed to learn more about leadership and coaching to be more efficient in my role, so I completed a UK National Health Service online leadership course as well as other leadership training locally. The organisation where I work was very supportive and provided training for clinical leaders managing clinical workflow changes. I took the opportunity to be in the first cohort of a Digital Academy for clinical leaders run by the DHB.

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I stepped up to a clinical nurse specialist role from the clinical coach role in late 2019. I enjoy meeting various clinical experts, understanding their needs and workflow and assisting them with a digital solution wherever possible. It is a great pleasure consulting about the clinical needs with a vendor so that they can translate it into a technical functionality. I believe change management is still a great challenge, but I can lead change effectively by listening to end-users, by supporting, directing and guiding them regularly, and by responding to their queries. I am very optimistic about the digital future of nursing. Being a nurse is a highly rewarding job where you receive an opportunity to touch many lives, and ultimately, we want to provide safer care to patients. Digital solutions have the potential to enhance a nurse’s decision making and the nursing care process in order to provide better patient care, faster and smarter. Chasing after clinical notes, time-consuming searches for relevant data, one-way pager communication and duplicated documentation—among other things—will soon be history in many hospitals. Digital solutions will enable nurses to enjoy improved access to health records, more coordinated and streamlined care planning, the ability to schedule assessments with automated reminders, and effective communication with the healthcare team. However the nursing profession, including those in teaching, policy-making and clinical practice, faces a massive responsibility to prepare the current and future nursing workforce to use technology effectively. With the increasing use of technology in nursing care, strategies to maintain patient privacy and to reduce the risk of negative impact on nurses’ critical thinking abilities and patient outcomes are paramount. Constant monitoring and robust research are required to assess the impact of digital solutions on nursing practice and patient outcomes.

Karen Blake, healthAlliance NZ Ltd, New Zealand ‘Learning and leading, leading and learning’ is the story of how I approach my work. When I first trained as a direct-entry midwife (that is, becoming a credentialed midwife without first becoming a nurse) in the late 1990s in New Zealand the career options seemed threefold—work as a clinical midwife, work in management, or work in education—and during my 20 years as a practicing midwife I have done all those things. I have practised clinically in New Zealand and in Australia, from maternal foetal medicine in large tertiary hospitals to water births and home births in the community. I was the first direct-entry midwife in Australia to become a Director of Nursing and Midwifery, managing a sub-acute and day surgery facility. I am passionate about education, working both as a clinical educator and university lecturer. I have also discovered new career options as a senior policy advisor in Australian state government, learning to understand the machinery of government and the development and implementation of policy. An enterprise Electronic Medical Record (EMR) project first propelled me into clinical informatics. I had worked as a clinical super-user of an EMR, had done project management and service redesign, and was generally known as someone

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who was ‘good with computers’. During a coffee break with the Executive Director of Nursing and Midwifery at the Melbourne hospital where I worked, I was asked to apply for a newly created role to support this EMR project: Chief Nursing and Midwifery Information Officer (CNMIO). I tried to appear knowledgeable, enthusiastic and excited about the opportunity; however, the first thing I did after going back to my office was to Google, ‘What is a CNMIO?’ and (because I was curious), ‘What does a CNMIO earn?’. I wholeheartedly embraced this opportunity and experience, finding my feet relatively quickly and working with the newly appointed Chief Medical Information Officer (CMIO) to create a team of clinical subject matter experts. I loved working alongside the EMR project team too, and learning all about a large EMR implementation. This role became a strong foundation to build the next steps in my career in clinical informatics. Following a move back to New Zealand—having already accumulated a wealth of qualifications from health care leadership to project management, data standards, complex obstetrics and informatics—I embarked on more postgraduate study in health informatics. I also took on a new role at healthAlliance, a shared service provider that provides the Information and Communications Technology (ICT) services for the four northern region DHBs. As Head of Clinical Informatics, I work to ensure that everything we provide and implement meets the requirements of the clinicians working across the region and the patients they care for. In order to do the breadth or work required, I have collaborated with the Chief Clinical Information Officer (CCIO) to build a team of specialist informaticians, including nursing, midwifery, pharmacy, and medicine. I believe that in order to fully understand health and how best to develop and implement ICT, we need the collective wisdom of a multidisciplinary team with a wide range of clinical experience underpinned by a theoretical knowledge base. What continues to resonate with me and forms a large part of my approach to my work as a clinical informatician is being able to learn and critically evaluate new information, and bring that into my work, becoming a more effective advisor with every opportunity. I have an in-depth understanding about the healthcare system and the impacts of technology on clinical staff, patients and consumers, and health operations. I also am able to learn about technology, processes and systems—distributed denial of service attacks, and containerisation to support a cloud migration strategy, and application programming interface gateways, and identity and access management. It is this on going quest for learning that contributes to success in my role and makes my work so rewarding.

Karen Day, University of Auckland, New Zealand Changing how we work is the theme of my narrative. It was 1980 and I was training for my Diploma in General Nursing and Midwifery. In the half-kilometre-long building that was the new Johannesburg Hospital, we sat before computers and

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learned how to search for patients, complete the midnight report, and admit, transfer and discharge patients electronically. It was exciting and scary in the belly of this immense 2000 bed hospital, where we had to walk through the ‘tunnel’ and past the mortuary to get to the computer department. My dream was to influence how health care was organised, managed and delivered on a national level, so I went on to complete a Bachelor of Arts, majoring in public health nursing and health service management. By then I was nursing in a small urban hospital rehabilitation unit for people recovering from brain injuries and spinal cord injuries and associated complications. There were no computers. When the Nursing Process was introduced to standardise care, in the late 1980s, I felt overwhelmed by the documentation and I wondered how a computer could support this work. By 1995 I was half way through my Master of Arts, using data on 100,000 members of a medical insurance scheme to apply a managed care intervention in an effort to create a market advantage for my employer. My nursing skills, coupled with latterly acquired insurance underwriting skills, were leveraged to manage a prescription benefit for people with long-term health issues. Fourteen per cent of the membership base had long-term health issues, and we applied a managed care structure and rules to their use of the benefit. This was the basis of my Master’s thesis. A year later this work became the reason for a start-up insurance scheme in a gold mining house west of Johannesburg, to employ me to establish its claims and managed care divisions. When the pre-selected information system failed, we recruited a software company to co-design and develop a bespoke claims system that included managed care processes. As the claims data grew in the information system, I analysed the practice profiles of the services and clinicians such as general practitioners (GPs) to detect variance in clinical care and resource use. I had no idea that the work I was doing was informatics. By the time I arrived in New Zealand in 1999 I was comfortable at the computer, and had other skills such as data analysis in spread sheets, information system design and implementation, data driven staff management skills, and the ability to use routinely collected claims data to assess the quality of care delivered by clinicians in primary and secondary care. My first job in New Zealand was to build a decision support system for prescribing in software designed for use by primary care clinicians—although I did not understand what I was doing until years later, in these terms. At the time I was called a ‘knowledge advisor’ and asked to source information about medications available in New Zealand and set up a system to support GPs as they wrote prescriptions. When I moved on and joined a project team as a change manager in a large urban District Health Board in Auckland, it felt like this was the kind of work I wanted to do for the rest of my life, this blend of nursing, midwifery, information systems, innovation and bringing about changes to improve how we do health care. The Chief Information Officer recommended that I enrol in a PhD in information systems. Once again, my studies were based on my job, inextricably blended into the work I was doing through action research. At that point I discovered that I was doing health informatics and it became part of my identity.

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With a PhD I became an academic—learning, teaching, and researching health informatics. As a supervisor of research students, I relate to clinicians, their environment and the learning they do that will enrich their roles. My experience in hosting hackathons, leading and teaching postgraduate and undergraduate informatics, conducting research in telehealth, consumer informatics, evaluation of information systems, and change management, have left their mark on me. I deeply identify with health informatics as seen in my teaching and workforce research—it means a lot to me to influence the next generation of the HIDDIN workforce, and to have farreaching positive impacts on the way people do their jobs.

Discussion and Conclusion Our original identities as nurses and midwives have been enriched with understanding how the whole health system works. We are successors of Florence Nightingale, in this respect; 200 years after her birth we are as baffled as she was by the institutional and processual silos that interfere with clinical workflow, continuity of care and quality of care. All of us have extended our clinical roles to reach into the possibilities associated with digital health. To varying degrees, we learnt something about clinical informatics in our basic training and augmented that with information specialist learning to solve problems and advocate for change—to ensure that clinical information systems meet nurses’ and midwives’ needs as well as those of other clinicians. We are prepared to keep advocating for change through leadership and support for those who need to learn and adapt to new digital health ways of working. The impact of informatics was dramatic in Nightingale’s time, when she developed visualisations of patient data for the first time in history so that funders and administrators were able to see at a glance the effects of hygiene, appropriate staff training and management, and environmental conditions on the morbidity and mortality of hospital patients. We take this for granted now and we have the advantage of digital data management and associated informatics initiatives. Innovation is at our fingertips, as described by Sally and Karen Day. We become the bridges between information systems designers and implementers, and the clinicians who use those systems, as described by Lisa, Sally and Karen Blake. We are the voice of leadership to advocate for improved clinical care, as described by Lisa, Karen Blake and Karen Day. And because we are nurses and midwives, we provide support for those who need our help, whether as our patients or as our colleagues who are learning new ways of working, as described by Abin. All of us solve problems that require our information specialist skills, which lie in the sweet spot between what it means to be a nurse and/or midwife and to be an information specialist. Like Nightingale, we may not see the full impact of our work in our lifetime, but we work to ensure that one day we will no longer be the most invisible subgroup of the HIDDIN workforce.

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References Britnell S. Weight estimation without waiting: design, development and testing of a mobile application to measure the length and estimate the weight of New Zealand children for advanced paediatric resuscitation. Auckland University of Technology; 2020. Brixey J, Salyer P, Simmons D. Nightingale power: the advent of nursing informatics. Nurs Manage. 2020;51(7):51–3. HIMSS.  Nursing informatics workforce survey. 2020. https://www.himss.org/sites/hde/files/ media/file/2020/05/15/himss_nursinginformaticssurvey2020_v4.pdf. Accessed 15 May 2021. Honey M, Collins E, Britnell S. Education into policy: embedding health informatics to prepare future nurses – New Zealand case study. JMIR Nursing. 2020;3(1):e16186. Hübner U, Shaw T, Thye J, Egbert N, de Fatima MH, Chang P, et  al. Technology Informatics Guiding Education Reform–TIGER. Meth Inf Med. 2018;57(S01):e30–42. Meleis AI, Sawyer LM, Im E-O, Messias DKH, Schumacher K.  Experiencing transitions: an emerging middle-range theory. Adv Nurs Sci. 2000;23(1):12–28. MOH NZ.  New Zealand Health Survey. Wellington: Ministry of Health New Zealand; 2020. https://www.health.govt.nz/nz-­health-­statistics/national-­collections-­and-­surveys/surveys/new-­ zealand-­health-­survey. Accessed 22 Oct 2020. Nursing Council of New Zealand. Trends in the New Zealand Nursing workforce: 2012–2016. Wellington; 2017. World Health Organization. State of the World’s Nursing Report. 2020. https://www.who.int/ publications/i/item/9789240003279. Accessed 15 May 2021.

Chapter 24

Working as a Public Health Informatician Karen Day, Robyn Whittaker, Vicki Bennett, Vanessa Selak, and Brian Stokes

Abstract  Public health realises its potential when information specialisation is part of the job of protecting and improving the health of populations. With the benefit of information specialisation, practitioners are able to tap into evidence, leverage the value that data adds and improve services at individual, local, regional, national and global levels. This chapter contains four narratives of careers of public health information specialists in which they describe their career journeys, the innovations they have spearheaded, and the impact of their work on the health of populations. We then relate their narratives to identity, impact and innovation to illustrate how diverse career pathways converge on public health information specialisation, enable innovation and define far-reaching impacts. Keywords  Case study · Public health · Population health · Global health · Community health

K. Day (*) ∙ V. Selak School of Population Health, University of Auckland, Auckland, New Zealand e-mail: [email protected]; [email protected] R. Whittaker Institute for Innovation and Improvement, Waitemata District Health Board, Auckland, New Zealand e-mail: [email protected] V. Bennett Metadata and METeOR Unit, Australian Institute of Health and Welfare, Canberra, ACT, Australia e-mail: [email protected] B. Stokes Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_24

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Introduction Public health focuses on the community. The community has many faces, e.g. local communities, populations and nations. Within each of these communities, public health practitioners work to prevent illness, promote health and address health issues from a population and infrastructure perspective that results in improved health outcomes simultaneously for individuals and groups of people. Public health typically involves monitoring, diagnosing and investigating health issues in communities; informing and educating people about health issues and identifying ways for them to access services that are out of their reach; mobilising communities and developing policies to address health issues that affect communities and policing policies and regulations; evaluating the impact of public health policies and implementations; researching insights and innovations to take public health to the next level, and assuring an appropriately qualified and skilled workforce to provide public health services for individuals and communities (Magnuson and Dixon 2020). The information needs of public health practitioners include statistics, guidelines, research, government reports and finding data (Barr-Walker 2017). Electronic data collections, such as immunisation registers, are a valued source for public health. Creating, maintaining and making best use of these databases requires public health information specialists. The inclusion of digital health and information science in public health is a small step, since public health work so obviously relies on information management. Public health informatics is defined as ‘the systematic application of information, computer science and technology in areas of public health, including surveillance, prevention, preparedness, and health promotion’ (Aziz 2017: 78). The context and scope of public health informatics is as broad as it is wide. Its identity lies within this scope; with a focus on illness prevention and health promotion that ranges from interventions that affect the environment to interventions aimed at individuals within their communities. This reach benefits from digital technologies and associated innovations that take public health interventions into the homes of individuals. The impact of such a reach is woven into everyday lives, e.g. by targeting a population (such as people with diabetes) on their mobile phones with a diabetes monitoring and management program via texts (Dobson et  al. 2018). The digital and information challenges faced by the public health workforce stem from new health issues too, such as the COVID-19 pandemic. This poses opportunities for the public health workforce to reposition itself (Meng 2020). The ability to create large databases raises opportunities to create innovative public health interventions, but to make the most of the data, the workforce requires new skills in big data analytics and the use of artificial intelligence in making sense of the data. However, many public health databases remain manual or based on localised use of Excel spread sheets—thus are not readily connected with other useful databases, e.g. a cancer registry separated from electronic health records. The pandemic juxtaposed the lack of inter-regional laboratory data sharing to manage epidemics with the need to share data globally with other countries and with the World Health Organisation. The

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public health workforce must expand its information skills to address new and old problems. This chapter examines the careers of four people who have done so.

 obyn Whittaker, Waitemata District Health Board, R New Zealand Mobilising public health has been a key theme in my career. I am a public health physician—a doctor specialised in public health, population health, epidemiology, health impact assessment and health services planning. My first placements as a trainee were in health informatics—in regional health services planning and then in a hospital assessing clinical indicators. I went on to do more traditional public health service roles but returned to digital health initially through an academic role. I was employed at the University of Auckland to help with the dissemination and implementation of the first health intervention delivered solely by text messages, proven effective in a randomised controlled trial (Rodgers et al. 2005). This started my interest in using mobile communications to deliver healthy behaviour change and self-management support programmes directly to people. The mobile phone was fast becoming ubiquitous and outstripping any previous technology in terms of the speed of global spread and penetration into vulnerable communities. New Zealand was at the forefront of mobile health (mHealth) research and development, cementing my role and the topic of my PhD. This has led to a body of mHealth interventions, co-designed with communities and clinicians, tested in real-world randomised trials, and several implemented in practice across a variety of contexts and countries (Whittaker et al. 2012a–c). My international networks in mHealth were given a huge boost through a Harkness Fellowship in Healthcare Policy and Practice and 12 months working in the US federal health system (Whittaker et al. 2012a). Upon return to New Zealand, I took up a role at Waitematā District Health Board while continuing my academic career. This role led to a strategic programme of work in digitising the hospital system and implementing innovations in the health service as part of establishing an Institute for Innovation and Improvement. My interest in making cool things happen has led to dabbling in a variety of different specific fields including mHealth, health IT, telehealth, wearables, big data and AI. I think that the public health perspective brings big picture thinking and the population perspective to all this work. Conversely, I think an interest in informatics and digital brings an openness to new ways of doing things to the public health sector. Learning from others and particularly other countries has always been a large part of my work. I enjoy working internationally, on mHealth trials and implementations, as well as with the World Health Organization (WHO) as part of their Digital Health Technical Advisory Group and ‘Be Healthy Be Mobile’ global initiative (WHO 2020). Recently I have been involved in developing an in-house course for clinicians with an interest in digital, causing me to reflect on the paths my colleagues have taken to get to the roles they now occupy. These pathways are varied

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and were not often planned in advance—mine certainly wasn’t. Those who have taken circuitous routes to digital health often have a breadth of experience and passion for improvement in general that adds substantially to what they bring. A clinical background or deep experience of the health sector is invaluable, and works well alongside the fresh perspective and new approaches brought by those completing health informatics and related courses.

 icki Bennett, Australian Institute of Health V and Welfare, Australia My career has taken me from Health Information Management (HIM) to population health. When I finished high school, I started an accounting degree, but quickly worked out that I had too much personality for that! So, I enrolled in a Bachelor of Applied Science (HIM) at the University of Sydney. Not only was the varied content intellectually appealing, but I was told that there were lots of jobs available, including part-time. I knew that I wanted to have a family, so work flexibility was important. I was pregnant when I finished my degree, so the ensuing years included lots of part-time and contract roles including working as a data manager in a cancer unit (arguably my first foray as a Public Health Information Manager (PHIM), teaching IT units in the HIM degree program, and developing a database for the first set of Australian Clinical Coding Standards. Then, with two toddlers in tow, I moved to a village in the catchment region of the national capital, Canberra; over the next 5 years, I worked at Yass Hospital 1 day a week looking after all things health information- and data-related, and had another two children. During this time, I also continued to stay strongly committed to my professional identity as a HIM and often self-funded my attendance at professional development events. I then moved into Canberra and I worked at a private hospital as the Administration Manager; I led the implementation of a new patient information system managing a team of 40 staff, and completed a Master of Science (Health Informatics) at the University of Sydney. I was offered the opportunity to work as a HIM for 2 months on an Australian aid funded program in Vanuatu, due to a relationship between my hospital and one there. My CEO asked if I wanted to go, following up with ‘but you probably can’t because you have four kids’; notwithstanding, living and working in Vanuatu turned into an experience for my whole family, and was the true beginning of my appreciation of public health information and the vital role it plays in improving health outcomes. When I returned, I took on a position as the Manager of the Health Information Section at Medicare Australia, responsible for a team who provided publicly subsidised medical and pharmaceutical services data (MBS and PBS) to internal and external requestors. This was a new and interesting area involving the use of national health data to inform health policy, planning and research, including the establishment of a number of important major cohort studies. My heart was still in the Pacific Islands, so over the next few years I developed

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a health data dictionary and national health indicators for Tonga, then became the national HIM for the Ministry of Health in Fiji, again moving my whole family there. When I returned, I started work at the Australian Institute of Health and Welfare (AIHW) where I have held a range of roles managing national health and welfare data assets and developing national health indicators, including running the My Health Record Data Unit, the Expenditure and Workforce Unit, and now the Metadata and Classifications Unit. In 2009, I took on the role of Project Manager of the Health Information Systems Knowledge Hub for Health at the School of Population Health at the University of Queensland. I worked extensively with the WHO and other international agencies and made ten international trips for work in 2010. I have since returned to work at the AIHW where I am able to continue to work in an area that allows me to fulfil my passion for public health and for using evidence to inform decision making in health continues. As well I remain committed to Pacific Island nations—I am now the official advisor to the Pacific Health Information Network Board. I could not be happier with the career I have had and I hope to continue to make a positive impact on public health outcomes for many years to come.

Vanessa Selak, University of Auckland, New Zealand Using routinely collected electronic data to support improvements in health service quality has increasingly fascinated me during my career, as clinician and a health researcher. I completed training in medicine in 1998, specialist training in public health medicine in 2007, and a PhD in cardiovascular epidemiology in 2015. I have 20 years’ experience working within the health sector, in roles spanning clinical medicine, planning, funding and quality improvement. In my most recent health sector role, in quality improvement, my job was to work with clinical directors of hospital specialty departments to help them to develop clinical quality indicators that could be reported on automatically and displayed in near real-time on specialty-­ specific dashboards using routinely collected electronic data. A critical aspect of the role was in being able to effectively translate between clinicians and their clinical priorities, and the analysts and the available electronic data. A particularly fruitful collaboration emerged when working with a rheumatologist who wanted to develop an indicator of the proportion of patients with rheumatoid arthritis who were receiving disease-modifying anti-rheumatic drugs (DMARDs). Rheumatoid arthritis is primarily managed by rheumatologists in the outpatient department in the hospital, and medication is dispensed in community pharmacies. While we could identify all patients attending rheumatology outpatient clinics (and could determine whether this was for a first or subsequent specialist assessment) and all the medications that had been dispensed in the community (including DMARDs), we had no way of knowing which of the rheumatology patients had rheumatoid arthritis without manually looking up the records of each patient. This was because, although coding of clinical diagnoses is undertaken

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routinely for admissions (by administrators), it is not done for outpatient appointments at that hospital. There was no funding to support extension of coding of clinical diagnoses by administrators to include outpatient appointments, so the rheumatologist and I, supported by a quality improvement team, developed and implemented a sustainable system for integrating the coding of clinical diagnoses within the existing workflow of the rheumatologists for all outpatient clinic appointments. Initially we modified an existing paper-based process but were then able to progress to an electronic system. Not only was the rheumatologist able to receive automated reporting on the proportion of rheumatology patients receiving a DMARD, but for the first time they were able to automatically generate reports of their patient population by clinical diagnosis. Since completing my PhD, which was based on data from randomised controlled trials, my research has increasingly focused on observational methods which use routinely collected electronic data. Undertaking such research is extremely attractive for a public health physician in New Zealand, for a number of reasons. We have a unique national health identifier (NHI), which enables us with ethics approval to link data for individuals across different data sets. As researchers we don’t need to know who these individuals are, just that their data are linked, so we are able to maximise the privacy of included individuals by using encrypted, rather than live, NHIs. Our Ministry of Health collates and maintains a comprehensive selection of national datasets, including for mortality, hospitalisation, primary health organisation enrolment, community dispensing and laboratory test requests. Using these data collections, we are able to create national (or subnational) cohorts of health-­ contact populations. This means that research can be conducted at a truly population level at a manageable cost because data collection leverages existing data sources. We are currently restricted primarily to structured data, but efforts are underway to unpack the potential of unstructured data (such as from specialist letters and hospital discharge summaries) using methods such as natural language processing. Combining unstructured with structured data will increase our ability to identify and characterise clinically relevant cohorts of people from a population perspective, for example in order to assess the extent to which evidence-based interventions are being implemented for these cohorts. Further, we are also able to characterise these clinical cohorts according to important demographic factors such as ethnicity and socioeconomic status, which will increasingly enable us to assess the equity of the implementation of such interventions.

Brian Stokes, University of Tasmania, Australia The undeniable value of data in public health is the theme running throughout my working life. I have always worked with data in differing forms. Earlier in my career, I worked in public sector tourism as a technology specialist with responsibility for data management and reporting. With a primary interest in using data to evidence and inform government and public policy, I completed a Bachelor of Arts degree

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with majors in political science and public policy, and with a focus on the emerging role of information technology in supporting data management. With the emergence of digital health in the early 2000s, I found myself drawn to the enormous opportunities presented through the growing use of technologies in the healthcare sector. After a period working in central government in policy and project roles, in 2006 I joined the Mental Health and State-wide Services areas of the Tasmanian Department of Health, with responsibility for technology, data and information management. In this demanding but rewarding role, I found myself drawn to strategies centred on improving data quality to support state and national reporting across the mental health and alcohol and drug sectors. A key area of my responsibility was providing data annually to the Australian Institute of Health and Welfare across multiple national minimum datasets. At that time, data was collected manually by clinicians across acute inpatient, ambulatory and residential settings with unit record data coded manually according to the International Classification of Diseases (ICD) system and entered into a standalone database. The timeliness, accuracy, completeness and overall quality of data was considered low and presented problems at multiple levels. Nationally, when compared to other jurisdictions, certain key performance indicators suggested Tasmania was underperforming. Locally, with incomplete data and significant delays in coding of patient episodes of care, the absence of high quality data made resource and service planning challenging—especially when developing policy specific to requests for increased funding and service expansion, from consumers, carers, staff and the broader mental health and alcohol and drug sector. For the past 9 years, I have been employed as Manager of the Tasmanian Cancer Registry (TCR) and the Tasmanian Data Linkage Unit (TDLU) of the Menzies Institute for Medical Research, University of Tasmania. In the former, data is collected specific to the incidence and mortality of malignant neoplasms in Tasmania. Cancer registration is required by law under the Public Health Act Tasmania 1997, with data used in a multiplicity of ways including those of previous roles—to inform policy direction, strategic planning and service provision. Data is coded centrally by TCR staff according to the ICD Version 3.1 classification system and reported at State and local levels. In the TDLU, a range of health and related datasets, collected and classified according to agreed standards, are used to support de-identified, Human Research Ethics Committee approved, whole of population research in Tasmania and Australia-wide. Today, as when I commenced working with health data earlier in my career, my unwavering focus remains on the collection, availability, reporting and quality of healthcare data and the innovative use of technology and systems that underpin timely data collection and reporting.

Discussion and Conclusion Few people wake up in the morning and say, ‘Today I want to start a career as a public health information specialist’ and follow that up with an inquiry into the training they would need to complete or an appropriate career path to enter. Public

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health informatics is a logical progression from public health (Aziz 2017), but there is no equivalent to public health icon John Snow, to inspire future public health information specialists—this is still in the stage of ‘becoming’ an identifiable role. The identities of public health information specialists derive from an associated identity: as a public health physician, a health information manager or a public health policy maker and implementer, all with an interest in how information technologies influence the impact of public health. They have converged on the public health information specialisation from different trajectories, rather than identifying with it at the beginning of their career. While Robyn took what some would say is a traditional journey as a public health physician and career path into policy, Brian started with policy. Vanessa flipped clinical reliance on randomised controlled trials, to explore routinely collected data as a means to improve the quality of care of a population of patients with rheumatoid arthritis. In contrast to Robyn’s and Vanessa’s journeys, Vicki was first an HIM and then became a public health information specialist. In different ways their work has had an impact on the health of their communities (local, regional, national and beyond) demonstrating the breadth and depth of public health and the potential of information specialisation. Both Vicki and Robyn describe working within the global community of the World Health Organisation, to contribute to public health outside their own countries. All our case study authors have been innovative, though not limited to information technology innovation. All have used the evidence derived from information systems to design and implement novel solutions to new and old problems. Innovative thinking and design are at the heart of solving the problems faced by public health information specialists; here, we have shown solutions in the form of an mHealth intervention, a data dictionary, a cancer registry and a creative use for routinely collected patient data. Increasingly, people in these roles need to know their way around the associated information systems, build and use evidence, and influence the health of populations, in ways that make sense and scale up in digitally enabled health systems.

References Aziz HA. A review of the role of public health informatics in healthcare. J Taibah Univ Med Sci. 2017;12(1):78–81. Barr-Walker J. Evidence-based information needs of public health workers: a systematized review. J Med Libr Assoc: JMLA. 2017;105(1):69. Dobson R, Whittaker R, Jiang Y, Maddison R, Shepherd M, McNamara C, et al. Effectiveness of text message based, diabetes self management support programme (SMS4BG): two arm, parallel randomised controlled trial. BMJ. 2018;361:k1959. Magnuson J, Dixon BE. Public health informatics: an introduction. In: Public health informatics and information systems. Springer; 2020. p. 3–16. Meng X-L. COVID-19: a massive stress test with many unexpected opportunities (for data science). Harv Data Sci Rev. 2020.

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Rodgers A, Corbett T, Bramley D, Riddell T, Wills M, Lin RB, Jones M. Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging. Tobacco Control. 2005;14(4):255–61. Whittaker R, Matoff-Stepp S, Kendrick J, Jordan E, Stange P, Cash A, et al. Text4baby: development and implementation of a National Text Messaging Health Information Service. Am J Public Health. 2012a;102(12):2207–13. Whittaker R, McRobbie H, Bullen C, Borland R, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrane Database of Systematic Reviews. 2012b;(11 Art No.: CD006611). Whittaker R, Merry SN, Stasiak K, McDowell H, Shepherd M, Dorey E, et al. MEMO–a mobile phone depression prevention intervention for adolescents: development process and postprogram findings on acceptability from a randomised controlled trial. J Med Internet Res. 2012c;14(1):e13. WHO. Addressing mobile health. WHO; 2020. https://www.who.int/activities/Addressing-­mobile-­ health. Accessed 16 Oct 2020.

Chapter 25

Journeys into Becoming a Digital Health Specialist Urooj R. Khan, Leanna Woods, Gerardo Luis C. Dimaguila, Mohamed Khalifa, Elizabeth Schoff, Greig Russell, and Saswata Ray

Abstract  Digital health specialists are by definition a pluralistic heterogeneous professional group; their career pathways are neither straight nor smooth. This chapter contains seven narratives of professionals, their journeys into becoming digital health specialists, their aspirations, and their career prospects. These narratives speak of identity, impact, and innovation and illustrate how diverse career pathways out of clinical and technological careers have converged to define roles in digital health. These narratives have important implications for how this specialised workforce needs to be trained, identified, and retained, to meet the growing needs of digital health in patient care, health system planning, and policy making. Keywords  Case study · Doctoral degree · Fellowship · Training · Mentorship U. R. Khan (*) La Trobe University, Bundoora, VIC, Australia e-mail: [email protected] L. Woods The University of Queensland, Brisbane, QLD, Australia e-mail: [email protected] G. L. C. Dimaguila Murdoch Children’s Research Institute, Parkville, VIC, Australia e-mail: [email protected] M. Khalifa Macquarie University, Sydney, NSW, Australia e-mail: [email protected] E. Schoff · S. Ray University of Auckland, Auckland, New Zealand e-mail: [email protected]; [email protected] G. Russell Massey University, Palmerston North, New Zealand e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0_25

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Introduction The potential of digital technologies to benefit healthcare has been evident in recent years, but implementation has encountered challenges at various levels; the effort required to deliver on significant digital health investments is often underestimated and the workforce required is often invisible (Gray et al. 2019). Technology is not a panacea, and theories of technology adoption put the focus on workflow, process, task-fitness, and other user-related factors that indicate the cultural shift needed to use technologies effectively in complex and sensitive healthcare environments (Raza Khan et  al. 2019). Such use of digital technologies requires a multidisciplinary approach (Smith et al. 2011), specifically the development of a specialist workforce that understands both the technical and clinical aspects of a digital health program (Parry et al. 2013; Whetton 2005). Digital health specialists are critical in the digital transformation of health (Butler-Henderson and Gray 2019), in two key roles: creating and managing clinical information from raw patient data and delivering it to the point of care; and acting as the bridge between the work cultures of clinical and technical teams across the health organisation. Technical, clinical, and other communities within the broad health environment often resemble tribes, looking within to define who they are to themselves and others (Mannion and Davies 2018). But digital health specialists exist to enable the binding and integrating of health workforce tribes. Some of them choose to keep one foot in their tribe of origin, and one foot in a self-defined digital health tribe (Dave et al. 2008), and maybe this is why they are hard to perceive distinctly. Some reports indicate that the current specialist workforce is ageing and consequently the future productivity of the health sector is at risk (Butler-Henderson and Gray 2018); the COVID-19 pandemic resulted in rapid adoption of virtual care and other related digital health technologies, indicating a rising need for such specialised professionals (Sarbadhikari et al. 2020). However digital health specialist career structures are unclear—how someone lands into this space, gains professional recognition, finds pathways forward. Building capacity— through workforce planning, developing specialist qualifications, training programs, and career pathways—is essential to satisfy the growing demand for such roles (Butler-Henderson et al. 2020). This chapter explores the stories of seven clinical and technology professionals to trace common threads in their profiles, their motivations and trajectory in becoming digital health specialists, and to relate their reflections to the concepts of workforce identity, impact, and innovation.

 rooj Raza Khan: “I wanted my health records anywhere U anytime.” Sometimes a moment can change your life aims. It was such a moment for me in August 2012, lying in the operating theatre, nearly unconscious after going through immense pain from a ruptured ectopic pregnancy, thinking that I am in a technology

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era in a developed country that has a world-class health system… there must be a way to inform these clinicians, when I can’t speak, about my chronic illness, sensitivities and its effects on me....before I could think anything else, I was asleep. The background to this incident is that I relocated from the city to the countryside in 2012 and was trying to transfer my health records from a general practice in Melbourne to a new one in regional Victoria; after the initial consultation with my new GP, I kept my next check-ups on hold for some time, thinking I would follow up once my health records were transferred. Thus I came out of hospital frustrated, sad, and angry, wondering how it is possible in today’s world that I am unable to inform clinicians about my condition in an emergency or how can take so long to transfer GP records? My career as a technologist with 12 years of experience in IT business systems implementation and management of various electronic record systems, eGovernment and eCommerce solutions, made it impossible for me to comprehend the situation in the health sector. Dealing with post-natal depression, I was encouraged to use my disappointment and energy positively, so I started investigating any technological solutions that could ease health records transfer among clinicians and enable health record accessibility by clinicians when one is unable to speak. My mantra was “there has to be a better way!”. I learnt there was such a solution, which could store my health records and make them accessible to clinicians and myself easily, Australia’s Personally Controlled Electronic Health Record (PCEHR). I conducted a Master of Science research project investigating PCEHR adoption in regional Victoria and realised its teething issues. Not satisfied with my findings, I pursued doctoral research about MyHealthRecord (the new name for PCEHR) adoption in general practices. I started with curiosity, thinking I was exploring technology issues. Soon I learnt that there were process- and people-related socio-technical challenges, limited provisions for change management and cultural shift, significantly delaying system adoption. I investigated the problems with ten general practices and developed a framework for improving the integration of the MyHealthRecord system into their workflow. After one of my industry supervisors directed me to the field of health informatics, I was accepted in a fellowship training program that extended my professional development into digital health. During my associated work placement, I was involved in planning and initiating various innovative digital health projects. I discovered the real-world challenges of fragmented health data and its silos. I also learnt not only that digital health is surrounded by numerous challenges related to data, technology, organisations and people, but also that technology implementation is handled very differently by various disciplines. Even when people are motivated to progress in digital health with all good intentions, their journey is protracted as they learn new dimensions of each other’s knowledge and communication conventions. To date, I am unable to find a satisfactory answer to the question, why is it so difficult to have our health records accessible anytime, anywhere? The legacy of my own hospital emergency episode now seems like a lifetime reminder; it has given me a purpose to do more so that no one else should have to go through what I have suffered. It leads me on to explore my opportunities to make an impact in digital health through projects, education and research: in 2020, I joined La Trobe

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University as a senior lecturer. In this role, I am involved to coordinate, develop, and teach postgraduate courses in digital health. I am also leading a community of practice to mentor students and interns who are conducting various virtual care related industry research projects (in collaboration with Australia’s Digital Health Cooperative Research Centre). I feel this is my calling now, to make a difference and make a contribution to a better healthcare system. I believe my journey entails much more learning about digital health, and many further opportunities to find answers. Bring it on!

 eanna (Lee) Woods: “Administration should not absorb one L third of my time as a nurse.” It was 2 o’clock in the morning at the nurses’ station, when I realised something was dramatically inefficient with the way healthcare was delivered. With 8 years of clinical practice behind me, I had seen advances in biomedical science—so how could it be that I was writing my new patient’s admission weight, with pen on paper, four times, across the various admission documents? Tired and frustrated, I placed multiple admission paperwork pages for a single patient end-to-end on the floor, lay down alongside these, and measured their length, amused at its relation to my own. Administration would absorb some 30% of my time as a nurse, pulling me away from the bedside where my patients lay afraid, confused or in pain. I made the decision to get qualified, be heard and make a difference on a larger scale than six patients I cared for in a single shift. I knew it was time to make my contribution to the digital transformation of healthcare. Six years later, I have a two post graduate certificates, a research honours degree and Doctor of Philosophy which span the fields of clinical nursing, research, and digital health. My doctorate investigated clinician-led innovation, mobile health, and patient empowerment. Following this I undertook the requirements of the Fellowship by Training program with the Australasian Institute of Digital Health, affording me the opportunity to complete coursework in digital health topics and learn from experts across industry, health, and research. The highlight was contributing to national policy development in a 12-month work placement with a federal government department. Digital health offers powerful ways to connect information to people when and where it is needed, to keep the patient at the centre of their health. Unless digital health is used to its full potential, there is a missed opportunity to better health outcomes both at an individual and population level. Nurses are innovative, adaptable, and committed to people, yet the data we generate, correct, and use is stored in paper records in vaults under hospitals. As the largest workforce in health, nurses advocate for safe, quality patient care across a great number of settings, however nursing leadership in senior positions in the health, education and government sector is less than proportionate to the numbers in the workforce. I now have a seat at

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the table to make positive change, because of the pre- and post-nominals I have earned and the professional network I am developing. My becoming a specialist has been based on my mission to focus on the humans behind the technology, to enable the realisation of digital health technologies in practice. My pursuit of learning and impact has continued with my return to academic research, in collaboration with a State health system, to advance the connection of patient information across the healthcare journey. Digital health specialists are fearless optimists from a variety of backgrounds working on the same goal but from different angles.

 erardo Luis (Ikee) Dimaguila: “…passionate about patient G empowerment and bridging healthcare gaps through technological innovation.” When I finished my bachelor’s degree in Computer Science in the Philippines, I didn’t know what to do next. I loved my degree, but I didn’t want to work as a full-­ time programmer. I was driven by curiosity and the desire to contribute positively, and in the university, I spent a significant amount of time constantly finding ways to be involved in volunteering activities and community organisations. I was fortunate to be accepted as the consultant and designer of a project to develop the first national registry of child neurological diseases. In my role, I had to understand and implement data documentation and reporting using the ICD-10 standard. I was not even aware of any health standard at the time! I had to learn about the standard so we could integrate it into the database. I also had my first taste of digital health challenges, such as how some terminologies used by neurology practices may not be directly mappable to the ICD codes; and how data collection processes and resources available at different clinics across the country could vary. Through successive consultations, my team established processes for nationwide neurological data collection and management, and a feedback loop so varying terminologies could be flagged, and consensus reached. My first dip into digital health was a relative success, but I felt that there were many things I could have done better—but I was not sure what, or how. Serendipitously, a new Master of IT degree with a health specialisation launched at the University of Melbourne around the same time as the registry project ended. I enrolled in this degree and secured an Australian government Endeavour scholarship. I learned that the challenges I had encountered in designing and developing the registry were quite common, and that digital health frameworks, principles, and methods could have guided me then. I became passionate about patient empowerment and bridging healthcare gaps through technological innovation and digital health expertise, and I researched and published an evaluation of the possible challenges facing mobile health technologies in under-resourced settings. To merge my digital health interests, I decided to pursue a PhD in digital health. I developed a novel framework enabling people to report their health effects and outcomes, when they use the data

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that they generate from engaging with health information technologies, and I disseminated my research through six publications in high-quality journals. In digital health one does not have to look far to find meaningful work. In our State’s vaccine safety service, I work closely with clinicians and epidemiologists so data and information guide government policies on Covid vaccine roll outs. I also hope to learn and look for ways to bridge healthcare access gaps, especially in under-resourced settings, through critical and thoughtful use of digital health design and evaluation frameworks and principles. I am excited to be in a field that continually challenges its practitioners and experts to ask: What can I do best, and better; where are my skills needed now and in the future?

 ohamed Khalifa: “I could add greater value in population M health through informatics, as compared to treating individual patients.” My childhood was spent closely connected to computers, however, I studied medicine and graduated in 2001 as a medical doctor. Based on the slowness and inefficiency of healthcare processes that I experienced shortly thereafter, I decided to use my computer skills to improve the clinical outcomes of patients. I believed technology could save time and provide more accurate results. In 2002 I had the opportunity to implement a health information system to run the oncology medical centre where I worked, which resulted in improvements in our services to patients. This experience changed my mindset: I realised that I could add a greater value in population health through informatics, as compared to treating individual patients. I took a full-time job as a medical IT consultant, developing and implementing health information systems. For my professional development in this field, I studied healthcare management at the American University in Cairo. It brought me an opportunity to teaching health informatics to doctors. I continued my studies from 2009 to 2012  in a Master of Science in Health Informatics at the University of Edinburgh, and I became a member of the Royal College of Surgeons of Edinburgh. I also became a HIMSS Certified Professional in Healthcare Information & Management Systems. In 2012, I moved to a major tertiary care hospital to lead the Health Informatics and Performance Improvement departments. Over 5 years, I led diverse teams of healthcare and IT professionals to use health informatics in improving patient safety, effectiveness, efficiency, and timeliness of healthcare services. I worked on reducing waiting times, improving productivity, streamlining discharge processes, and reducing unnecessary lab tests. I also worked on reducing avoidable hospital admissions and frequent non-urgent emergency visits. I developed key performance indicators, operational dashboards, and strategic scorecards to improve services. I published over 30 papers documenting my projects and sharing my experience with colleagues worldwide.

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I moved to Australia in 2017, as a distinguished talent permanent resident, and joined the PhD in Health Innovation at Macquarie University, after I had been awarded a Commonwealth government funded PhD scholarship. I developed an evidence-based framework for grading and assessment of clinical predictive tools— the GRASP framework. In 2018, I was granted my first innovation patent from IP Australia for my framework. In 2020, my core published PhD paper was selected by the International Medical Informatics Association as best paper worldwide in medical informatics, and appeared in the IMIA 2020 Yearbook. Over 20 years I have developed a global career as a consultant and director in health informatics, business intelligence, and digital health, and I have engaged from three main perspectives: business development and implementation, professional hospital operation and utilisation, and academic research and training. Going forward, I am interested in investing in my skills, knowledge, and experience to contribute to the continuing digital transformation of the Australian healthcare system.

Greig Russell: “The daylight is slowly creeping in.” Describing myself as a health informaticist and getting the newly created sole full-­ time role in my hospital felt akin to telling people something socially stigmatising about myself. My friends said they had known all along but did not want to say anything. I was instantly demoted to a second-class citizen in the professional pecking order in the hospital, but I was happy! Paying my way through medical school as a software developer for actuarial software might have given some clues. My honours thesis was in health informatics, rebadged of course. Still, once I became a doctor, everyone let me put my sordid IT past behind me and start again: my mother was so proud of her son the real doctor. So my double life started, working as a clinician by day, and at night using health information to improve clinical outcomes. This study evolved into studying extramurally at the local university, but always on the quiet. Many colleagues looked the other way, though a few were incredibly supportive. Slowly my health informatics skillset grew, in computer science, statistics, clinical coding and classification systems, as well as health systems theory. I moved from solving a specific problem through ad hoc learning to the broader joined-up knowledge base, one course, one book, one video at a time, with lots of mistakes and many blind alleys. Slowly, various doors started to open as I worked on shared problems with new colleagues, particularly from the local university. The statisticians and philosophers in particular welcomed me into the most fascinating and exhilarating conversations of my life. Transactional medicine was getting in the way of my secret academic life; my desire grew to use health informatics to contribute to population health outcomes and optimisation of health system management to support clinicians. However, when I seized the chance for my dream job, as a health informaticist, this triggered new challenges. In my mind, I am still a clinician, but I just use different tools and have a population health focus; the snag is that there is no recognised

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health informatics speciality in medicine. Growing as a practitioner is no problem, thanks to the internet, Amazon and the fantastic Rstats community around the world, but getting this learning recognised as clinical activity is not straightforward. I have included my health informatics study into my clinical speciality continuing medical education system, and hoped for the best, and so far everyone has looked the other way, and it has worked out—but a vehicle for professional recognition is badly needed. I quickly discovered that my academic knowledge and practical experience were not the reason I was hired for my dream job, nor are these valued greatly in the digital health business culture of personalities and events—I have a presence in that world though still not a much of a voice. I hope and believe that a new day is dawning, however slowly, on professional recognition and respect for the role of the health informaticist.

 lizabeth (Liz) Schoff: “Technology has changed, but people E are still key to leveraging its value.” My journey into the world of health informatics started from a completely unrelated life situation and I had no idea where it would lead me. I’m a techie—not the geeky kind, but instead the people kind. I love working with people who are having trouble understanding when technology can help and how. I’m also the first person to wave a banner when technology is not the answer. My journey into health informatics started while I supported my mother in her final years. As we travelled from doctor to doctor, I would listen to her recount her medical history to each newly acquired specialist, gently correcting and prompting her when she left out major medical events or conditions, or confused dates and people. I tried to help by creating (what I now know!) her medical record—a spread sheet of her medications and medication history and a chronological (e.g. longitudinal) health record. Fast forward a couple of years, I had relocated from California to New Zealand, and was looking for a job that would allow me to stay. Because of my technology and management skills, I landed a job heading Northern Region Professional Services at the healthcare software vendor, Orion Health. During my interviews, I leaned on my personal experience creating my mother’s medical record. Working at Orion Health and with the Northern Region healthcare providers, I realised I had fallen into a world that fit me—people who wanted to help people, but who didn’t have the right fit, or sometimes any fit, for the technology that would make a difference. This was an opportunity to build into systems, the constant need to balance people, process and technology—a three-legged stool that would surely topple if one leg grew too long. I needed more training. I signed up for a Master of Science degree at the University of Auckland’s School of Population Health, focusing on health informatics. I was often the only techie in classes of nurses, doctors, and researchers. Listening to the discussions of those who had come from the clinical world really

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broadened my empathy for the challenges that technology has brought to healthcare. I wanted more involvement. I joined Health Informatics New Zealand (HINZ), then joined the Executive Committee, and moved into a lead role to restructure the HINZ organisation. This time, I was working to balance the perspectives of clinical, academic, and commercial stakeholders; again, it was all about the balance. Through all this, I have continued to work in hospitals and healthcare related entities. Technology has changed, as has our cultural acceptance of technology, but people are still key to leveraging its value. Now I focus on cybersecurity, where we look at how people, process, and technology work together to keep our electronic health information under our own control, secure and private, as it should be.

 aswata (Sas) Ray: “Some beautiful paths can’t be discovered S without getting lost.” (Erol Ozan) I certainly don’t want to get lost on my career path, but I feel this quote has something to do with my career decisions, maybe something to do with my subconscious performing silent tricks on my conscious, before my conscious being typed a search into Google, ‘digital health’. I was in Mumbai, India, working for the number one healthcare service provider in the country. I had worked in clinical operations, medical writing, pharmacovigilance, and clinical research data management over a span of 8 years. Life was sorted out almost perfectly, but I felt a push from inside to do something that was not written in a ‘protocol’ and that would allow me to leave a digital footprint (by writing a thesis). Thus I landed in Aotearoa, New Zealand, to do a PhD at the University of Auckland. My PhD is exploring how social media may influence food decisions for young adults. The urge to find a research topic outside clinical research grew after I wrote a review paper on social media and clinical research. As a professional I was attracted to research that had the potential to influence our daily lives in health related ways, and I became fascinated with the power of the virtual world to affect us in so many ways. I am excited that my PhD topic is the first national study of its kind at this point, and this helps me to keep going. Moving on from clinical trials to embark on a new path was not an easy choice, but when I made the decision, I banked on my skills being transferable in an informatics role. Working in all domains of clinical research had given me a positive mindset about being able to traverse new domains, to rise to a challenge and to shift from one stream of healthcare research to another. In addition I imagined the fun and satisfaction in contributing to a project that could improve patient outcomes. As I gain more exposure to the field of health informatics I am feeling more confident to meet other professionals and have conversations about fascinating topics, and I am getting to know more about different roles and requirements. Given an opportunity, I would like to work as a clinical business analyst who knows what happens in the clinical field and is able to translate the needs to a software developer.

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Discussion and Conclusion The case studies in this chapter tell of passionate professionals with various information sciences and health sciences backgrounds and pivotal life and work experiences. Urooj, Liz, and Ikee worked through multiple roles in the technology industry whereas Khalifa, Lee, Greig, and Sas worked in clinical positions. They observed and experienced problems in patient care, health records inaccessibility and fragmentation, technology adoption resistance, and systems inefficiencies. They realised how digital technologies can help the healthcare industry deal with these challenges and became determined to explore how to contribute to improve efficiency, effectiveness, and safety of health technology design, development, and evaluation. A personal drive sustained the momentum in each of them, as they proceeded through the indeterminate nature of the digital health profession. Their stories illustrate the lack of a formal pathway to enter and advance in the field. Greig and Liz were able to find their way into digital health through years of work experience. Others followed PhD pathways, and some undertook a fellowship training program as well. All navigated different routes to come to identify themselves as digital health specialists. Coming through such individualised experiences has made them resilient; in them, the digital health workforce has dedicated emotionally intelligent real-­ world problem solvers, likely to have significant impact through work in academia, in applied research, and in the digital health industry. They will view challenges through diverse lenses and develop multidisciplinary solutions using outside-the-­ box thinking and evidence-based approaches. Each one brings something unique to the evolution of the health care system. The healthcare industry is often tribal (Dave et al. 2008), and such a culture can be the source of harm and conflict, but it is also possible that culture can be a source of remedy, as can technology. Health information ideally is a force multiplier, reducing the stress on staff and improving both patient experiences and clinical outcomes. Digital health specialists are a pluralistic heterogeneous professional group that builds information and communication bridges between the technical and the clinical tribes. Their work is translational and holistic; more than merely having expertise in dual disciplines, the whole of their skillset is greater than the sum of the parts, and they can make key contributions once they find their voice and assert their ability. Imagine how much more powerful these individuals’ contributions could be if they had a firm base of professional recognition from which to build! In concluding, this chapter shares their experiences to frame a call for the health sector: to raise awareness of the exciting possibilities to work as a digital health specialist; to provide high-quality specialist learning and training options; to encourage consistent specialist identification and affiliation; and to formally recognise the value of this specialist workforce. Innovations in these aspects of workforce culture will widen and deepen understanding of the need for change in healthcare and of the positive transformation that is possible through digital health.

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References Butler-Henderson K, Gray K. Australia’s Health Information Workforce: Census Summary Report. Launceston, Australia: University of Tasmania; 2018. Butler-Henderson K, Gray K. A Glimpse at the Australian Health Information Workforce: findings from the First Australian Census. Stud Health Technol Inform. 2019;264:1145–9. Butler-Henderson K, Gray K, Day K, Grainger R, editors. Defining the Health Information Technology discipline: results from the 2018 Australian and New Zealand censuses. Proceedings of the Australasian Computer Science Week Multiconference. 2020. Dave L, John K, Halee F-W. Tribal leadership. Aurora: HR.COM; 2008. 4 p Gray K, Gilbert C, Butler-Henderson K, Day K, Pritchard S. Ghosts in the machine: identifying the digital health information workforce. Stud Health Technol Inform. 2019;257:146–51. Mannion R, Davies H. Understanding organisational culture for healthcare quality improvement. BMJ. 2018;363:k4907. Parry D, Hunter I, Honey M, Holt A, Day K, Kirk R, Cullen R.  Building an educated health informatics workforce–the New Zealand experience. In: Grain H, Schaper L, editors. Health informatics: digital health service delivery-the future is now!: Selected Papers from the 21st Australian National Health Informatics Conference (HIC 2013). IOS Press; 2013. Raza Khan U, Zia T, Perera K, Pearce C. User acceptance of MyHealthRecord system in general practices. Int J Cyber-Physical Syst IJCPS. 2019;1(1). Sarbadhikari SN, Pradhan KBJS, Work Ha. The need for developing technology-enabled, safe, and ethical workforce for healthcare delivery. 2020. Smith SE, Drake LE, Harris J-GB, Watson K, Pohlner PG. Clinical informatics: a workforce priority for 21st century healthcare. JAHR. 2011;35(2):130–5. Whetton S, editor. Health informatics workforce skills: technology is king, time for a consort? HINZ: Proceedings. 2005.

Index

A Above-the-line (ATL), 142 Accreditation, 79–96 Accredited Record Technician (ART), 278 Actor network theory (ANT), 60 Advanced Analytics for Health Care Strategists, 101 Advanced Certified Health Informatics Professional (ACHIP), 101 Advanced Health Informatics Certification (AHIC), 101 Allied Health Informatician capture patient data, 310 career development of, 312 career themes and sub-themes, 311 case-studies, 313–314 cycles of acceptance, 316 digital allied health community, 311 EMR, 310 evidence-based practice, 315 implications and recommendations, 317 language of informatics, 315 leadership and engagement role, 315 patient-centred care, 316 service- or quality-improvement projects, 312 STEM disciplines, 312 teaching and training, 315 American Academy of Professional Coders (AAPC), 106, 107 American Association of Professional Coders (AAPC), 104, 108, 109

American Board of Medical Specialties (ABMS), 102 American Board of Preventive Medicine (ABPM), 102 American Board of Pathology (ABP), 102 American Health Information Management Association (AHIMA), 84, 103–106, 274 American Journal of Pharmaceutical Education 2016–2017, 29 American Medical Informatics Association (AMIA), 101, 109 American Nurses Credentialing Center (ANCC), 102 American Society of Health Informatics Managers (ASHIM), 104 Application programming interface (API) security, 227 Artificial intelligence (AI), 116, 129–131, 296 computer vision, 133 data science, 132, 133 deep learning, 130 machine learning analytics, 133 natural language processing, 133 professional learning, 131–135 Associate/Fellow of the Australasian Institute of Digital Health (AFAIDH/ FAIDH), 102 Australian Digital Health Agency, 203, 204 Australian Health Review 2009–2016, 29 Australian Library and Information Association (ALIA), 84, 107, 298

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Butler-Henderson et al. (eds.), The Health Information Workforce, Health Informatics, https://doi.org/10.1007/978-3-030-81850-0

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362 B Barnes, Cameron, 269–280 Baxter, Helen, 281–294 Below-the-line (BTL), 142 Bennett, Vicki, 339–342, 344–346 bioRxiv, 287 Blake, Karen, 327–336 BMC Medical Informatics & Decision Making 2009–2016, 29 Board-Certified Clinical Informatician (BC), 102 Board-Certified Informatics Nursing Registered Nurse (RN-BC), 102 Bolan, Christopher, 225–235 Britnell, Sally, 327–336 Bromiley, Paul A., 247–266 Burns, Mandy, 269–280 Butler-Henderson, Kerryn, 3–17, 71–77, 79–93, 115–123, 269–280 C Canadian College of Health Information Management (CCHIM), 103, 104 Canadian Health Information Management Association (CHIMA), 109 Capurro, Daniel, 319–325 CareSearch palliative care project, 285 Centre for Digital Transformation of Health (CDTH), 300 Certifications, HIDDIN workforce, 97–114 Certified Associate/Professional in Healthcare Information and Management Systems (CAHIMS/CPHIMS), 103 Certified Classification and Coding Specialist (CCCS), 103 Certified Clinical Data Manager (CCDM), 103 Certified Clinical Documentation Improvement Specialist (CCDIS), 103 Certified Coding Associate (CCA), 103 Certified Coding Specialist/-Physician-based (CCS/CCS-P), 103 Certified Documentation Expert Outpatient (CDEO), 104 Certified Documentation Improvement Practitioner (CDIP), 104 Certified Healthcare Chief Information Officer (CHCIO), 105 Certified Healthcare Information Security Leader (CHISL), 105 Certified Healthcare Simulation Educator/ Advanced (CHSE/CSHE-A), 105

Index Certified Healthcare Simulation Operations Specialist (CHSOS), 105 Certified Health Data Analyst (CHDA), 104 Certified Health Informatician Australasia (CHIA) certification, 104, 207, 312 Certified Health Informatics Systems Professional (CHISP), 104 Certified Imaging Informatics Professional (CIIP), 106 Certified in Healthcare Privacy and Security (CHPS), 105 Certified in Health Information Management (CHIM), 104, 105 Certified Inpatient Coder (CIC), 106 Certified Outpatient Coder (COC), 106 Certified Physician in BioMedical Informatics (CPBMI), 106 Certified Professional Coder (CPC), 107 Certified Professional in Electronic Health Records (CPEHR), 107 Certified Professional in Healthcare Information and Management Systems–Canada (CPHIMS-CA), 108 Certified Professional in Healthcare Quality (CPHQ), 108 Certified Professional in Health Informatics (CPHI), 107 Certified Professional in Information Exchange (CPHIE), 108 Certified Professional in Operating Rules Administration (CPORA), 108 Certified Professional Medical Auditor (CPMA), 108 Certified Specialist Health Interpreter (CSHI), 109 Certified Risk Adjustment Coder (CRC), 109 Certified Terminology Standards Specialist (CTSS), 109 Chacko, Abin, 327–336 Chief Allied Health Information Officer (CAHIO), 310 Chief Clinical Information Officer (CCIO), 219 Chief Digital Health Officer (CDHO), 219 Chief Information Officers (CIOs) challenges and directions for, 222 emergence of, 218–220 functions of, 220–221 seeking post-graduate qualifications, 221 traditional technology career path, 222 Chief Nursing Information Officer (CNIO), 219

Index Choo, Dawn, 309–317 College of Healthcare Information Management Executives (CHIME), 105 Competency, 80, 83 Competency-based education, 86–88 Competency frameworks, 83, 84, 86 Constructive alignment, 116 Consumer-centric, 148 Consumer health information specialist (CHIS), 140, 143, 144 Consumer information above-the-line activity, 142 actionable, 148 below-the-line activity, 142 digestible, 148 equitable, 148 graphic designers, 141 healthcare, 140, 146, 147 healthcare information specialists, 140 high quality, 148 marketing agencies, 141 pathways, 144, 145 relevance, 148 trustable, 148 Continuing Professional Development (CPD), 81 Cooper, Paul, 129–136 COVID-19, 116, 121, 147, 152, 153, 226, 285, 298 Cowley, Simon, 225–235 Crawford, Joseph, 115–123 Cybersecurity specialist Bolan, Christopher, 230 challenge of, 227–228 COVID-19 pandemic, 226 Cowley, Simon, 228–230 Fowle, Ken, 230–231 real-time access, 226 social engineering, 226 Staynings, Richard, 231–233 Williams, Trish, 233–235 D Davey, Khye, 309–317 Davies, Alan, 247–266 Davies, Angela C., 247–266 Davies-Tuck, Miranda, 237–245 Day, Karen, 3–17, 327–336, 339–346 Deep learning (DL), 130 de Sain, Rachel, 139–149 Digital Health Canada (2019), 85

363 Digital health technologies, 10, 284 building cohesive workforce, 206–207 COVID-19 outbreak, 206 education program or strategy, 204 Global Digital Health Partnership, 210 improving transparency, 209 risk management, 207–208 operating virtual clinic, 209–210 Digital Marketing Strategy in Healthcare, 109 Dimaguila, Gerardo Luis C., 349–358 DistillerSR, 303 DMPonline, 306 Donnolley, Natasha, 237–245 Dua, Prerna, 71–77 E Electronic health records (EHRs), 203, 275 Electronic medical records (EMRs), 219, 310 Eleftheriou, Iliada, 247–266 Embase, 287 EndNote sharing, 303 Entrustable professional activities (EPAs), 87 eResearch infrastructure, 304 Evidence Based Library & Information Practice 2010–2011, 29 Evidence-based practice (EBP), 283, 302 F Fellow of the American Medical Informatics Association (FAMIA), 109 Fellow of the Health Information Management Association of Australia (FHIMAA), 109 Fenton, Susan H, 71–77, 79–93, 201–212 Fernandes, Lorraine, 269–280 Fitzgerald, Oisin, 237–245 Fowle, Ken, 225–235 G Gee, Brendan Loo, 201–212 Gilbert, Cecily, 23–40, 295–307 Global Digital Health Partnership, 204, 210, 218 Global Health Workforce Council, 85 Globalisation COVID-19, 153, 154 economic interdependence, 151 health information work, 154–163 HIDDIN, 152, 153, 163, 164 infodemic, 152

Index

364 Globalisation (Cont.) internet, 151, 152, 164 knowledge management, 164 open coding, 154 public health, 152 transborder work, 154 Grainger, Rebecca, 319–325 Gray, Kathleen, 3–17, 23–42, 97–113, 151–165 Gupta, Meena, 281–294 H Hatta, Gemala, 277–278 Hatta, Oknam, 269–280 Hayman, Sarah, 281–294 Healthcare Information and Management Systems Society (HIMSS), 103 Health CIO, 217–224 Health data scientist, 237–246 Health informatics (HI), 9, 10, 84–86 Health Informatics, Digital, Data, Information and kNowledge (HIDDIN) workforce, 64–67, 80, 88, 91, 219 Health information and communications technologists (HICT), 85, 86 Health Information Management Association of Australia, 84 Health information management (HIM) professionals, 84, 86 Barnes, Cameron, 272–273 Burns, Mandy, 275–276 Fernandes, Lorraine, 271–272 Hatta, Gemala, 277–278 Marshall, Deneice, 273–275 Kim, Oknam, 278–279 Mandapam, Sabu Karakka, 276–277 Health librarianship, 5, 84, 86 EBP, 283 evidence-based decision-making, 284 face-to-face interactions, 282 Hayman, Sarah, 284–286 Baxter, Helen, 289–290 informationists, 283 innovative digital solutions, 283 Kelly, Blair, 292–293 Lawton, Aoife, 286–288 Gupta, Meena, 290–292 principles of good governance, 284 print-based transactions, 282 HITCOMP (2015), 85 Human Resources in Health 2013–2015, 29 Human resources management (HRM), 118

I Implementation Science 2013–2016, 29 Information Governance Toolkit, 275 Institute of Health Records and Information Management (IHRIM) Foundation Qualification, 275 International Federation of Health Record Administrator Organisations (IFHRO), 270 International Standard Classification of Occupations (ISCO), 73 Internet of Things (IoT), 116 J Journal of Healthcare Information Management 2004, 29 Journal of Health Communication 2012, 29 Journal of Hospital Librarianship 2005–2007, 29 Journal of Nursing Administration 2011–2017, 29 Journal of Physical Therapy Education 2004–2010, 29 Journal of Public Health Management & Practice 2015–2016, 29 Journal of the American Society for Information Science 1987–1988, 29 Journal of the Canadian Health Libraries Association 2008–2017, 29 K Kang, Kristan, 295–307 Kelly, Blair, 281–294 Kemp, Trixie, 269–280 Khalifa, Mohamed, 349–358 Khan, Urooj R., 349–358 Kim, Oknam, 269–280 Korea Medical Records Association (KMRA), 278 Korean Society of Medical Informatics (KOSMI), 106 KRMA-led ART education, 278 Kuusniemi, Mari Elisa, 295–307 L Lalani, Karima, 71–77 Lawton, Aoife, 281–294 Learning health systems (LHS), 302 Learning networks (Network based Learning Health Systems), 204

Index Leung, Tiffany I., 171–181 Lewis, Suzanne, 295–307 Library and information science (LIS), 299 Living documents, 290 Livingstone, Lisa, 327–336 Luna, Daniel, 319–325 M Machine learning, 296 Machine learning analytics (MLA), 133 Makeham, Meredith, 201–212, 217–222 Mandapam, Sabu Karakka, 269–280 Marc, David T., 71–77 Marshall, Deneice, 269–280 Mather, Carey A., 55–67 Maunder, Kirsty, 309–317 McCall, Terika, 171–181 McDonald, Steve, 295–307 McNeil, Keith, 217–222 Medical informatician, 319–326 Medical Library Association (2007), 84 Medical Record Librarian Licence System, 278 Medical records and management (MRM), 276 medRxiv, 287 Merolli, Mark, 309–317 N National Vocation Qualification (NVQ), 275 Natural language processing (NLP), 133 Network-based learning health systems, 210 Non-governmental organisations (NGO), 147 Not-for-profit organisations (NFP), 147 Nursing and midwifery informatician, 327–338 O Occupational classification categories, 74 competencies, 75, 76 HIDDIN, 72–74 internationally, 72, 73 jobs, 74 roles, 72 Online Journal of Public Health Informatics 2012–2014, 29 OpenNotes, 204, 209

365 P Patient administration system (PAS), 275 Pringle, Catherine, 247–266 Pritchard, Simone, 23–40 PRISMA flowchart, 303 Probst, Yasmine, 309–317 Professional development, 117 competency framework, 121 digital health, 120 empirical evidence, 120 HIDDIN, 120 Professional learning, 118 evaluating options, 121, 122 evidence-based strategies, 118–120 HIDDIN workforce, 120 learner, 122 Public Health Informatics Institute (2016), 84 PubMed Reminer, 303 Q Queensland Digital Academy (QDA), 205, 207 R Ray, Saswata, 349–358 Rayyan, 303 Reddy, Sandeep, 129–136 Registered Records Administrator (RRA), 278 Research information specialist Gilbert, Cecily, 300–302 implementing search strategies, 296 Kang, Kristan, 304–305 Kuusniemi, Mari Elisa, 305–306 Lewis, Suzanne, 298–300 McDonald, Steve, 297–298 open science model, 296 research lifecycle, 296 Solomons, Terena, 302–304 Ritchie, Ann, 79–93, 281–307 Rudd, Lachlan, 237–245 Russell, Greig, 349–358 Ryan, Angela, 201–212, 217–222 S Sabu Karakka Mandapam, 276–277 Safety Assurance Factors for EHR Resilience (SAFER), 205 Saeed, Haroon, 247–266 Sambrooks, Lawrence, 185–197 SCADA systems, 232 Schoff, Elizabeth, 349–358

Index

366 Selak, Vanessa, 339–346 Shillabeer, Anna G., 185–197 Shillabeer, Aydan C., 185–197 Siemensma, Gemma, 79–93, 281–294 Society for Health Care Strategy & Market Development (SHSMD), 101, 109 Sociomateriality, 60 Socio-technical system analysis, 58 design, 56, 58 digital health, 62, 63 HIDDIN workforce, 58 humanistic values, 55 mid-twentieth century, 56 models, 59 open systems, 59 primary objective, 56 principles, 55, 62–64 theories, 57, 59 Solomons, Terena, 295–307 Sprivulis, Peter, 217–222 Staynings, Richard, 225–235 Stokes, Brian, 339–346 Sullivan, Clair, 217–222 T Taggart, Richard, 217–222 Technology acceptance model (TAM), 60 Telemedicine Journal & E-Health 2012, 29

U UK National Health Service policy, 310 Unified Theory of Acceptance and Use of Technology (UTAUT), 61 US Institute of Medicine, 202 V van Merode, Frits, 171–181 Virtual care, 234 Virtual health rotation at the University of Minnesota, 204 W Wang, Karen H., 171–181 Western Australian Group for Evidence Informed Healthcare Practice (WAGEIHP), 302 Whetton, Sue, 55–67 Whittaker, Robyn, 339–346 Williams, Patricia A. H., 225–235 Wilson, Anthony, 247–266 Woods, Leanna, 349–358 Workforce and Education Roadmap, 204 Y Yale MeSH analyser, 303