Current and Future Trends in Health and Medical Informatics (Studies in Computational Intelligence, 1112) 3031421116, 9783031421112

Table of contents :
Preface
Acknowledgements
Contents
About the Editors
Medical Imaging and 3D/4D Surgical Visualization
Analysis of Brain Subregions by Segmentation of MRIs Using Improved BAT Optimization
1 Introduction
2 Related Work
3 Methods and Materials
3.1 Dataset
3.2 Image Preprocessing
3.3 Segmentation
3.4 Classification
4 Results and Discussion
5 Conclusions
References
Enhancing Medical Imaging with Computational Modeling for Aortic Valve Disease Intervention Planning
1 Introduction
1.1 Cardiovascular Disease
1.2 Aortic Valve Stenosis and Associated Cardiovascular Pathologies
2 Cardiovascular Imaging Techniques for Diagnosis and Intervention Planning
2.1 Echocardiography
2.2 Computed Tomography (CT)
2.3 Magnetic Resonance Imaging (MRI)
3 Intervention Planning and Post Operation Complications
3.1 Surgical and Minimally Invasive Options for Aortic Valve Intervention
3.2 Managing Complications Post-intervention
4 Computational Modeling
4.1 Cardiovascular Hemodynamic Modelling
4.2 Image Analysis, Geometry Reconstruction and Meshing
4.3 Personalized Computational Modelling
4.4 Artificial Intelligence and Machine Learning Application in Cardiovascular Pathophysiology
5 Challenges and Limitations of Computational Modeling
5.1 Accuracy and Validation Challenges
5.2 Limitations of Current Computational Models
6 Future Perspectives and Emerging Technologies
References
Construction of an Algorithm for Three-Dimensional Bone Segmentation from Images Obtained by Computational Tomography
1 Introduction
2 Background
3 Materials
4 Methodology, Results and Discussion
4.1 Morphological Study
4.2 Ground Truth Determination
4.3 Validation Metrics
4.4 Segmentation Based on Morphological Filters
4.5 Segmentation by Active Contour Methods
4.6 3D Model
5 Conclusions
References
Healthcare/Medical Information Systems Supporting Patients and the Public
Point-of-Care Devices in Healthcare: A Public Health Perspective
1 Introduction
2 Public Health Implications of POC
2.1 Patient Engagement and Health Literacy
2.2 Health Inequity
2.3 Detecting Undetected Conditions
2.4 Big Data Mining and Knowledge Discovery
2.5 Impact on Healthcare Systems and Health Promotion
3 Challenges and Limitations
3.1 Data Management and Integration
3.2 Transfer Protocols and Connectivity
3.3 Performance and Calibration
4 A Framework for POC Device Regulation
4.1 POC Assessment Factors
4.2 Lack of a Global Repository
4.3 Proposed Framework for POC Device Regulations
5 Conclusion
References
Digital Platforms to Support Feedback Processing in Aged Care Homes: Friend or Foe?
1 Introduction
2 Background
3 Methodology
4 Data Analysis
4.1 Participants
4.2 Applying Thematic Analysis
5 Discussion
6 Conclusion
References
State of Digital Health Communication Infrastructure in LMICs: Theory, Standards and Factors Affecting Their Implementation
1 Introduction
2 Literature Review
2.1 Current Practice of Standardisation by the Country’s National Standardisation Bodies
2.2 Theory and Frameworks for Standardisation
3 Methods
4 Results
4.1 Demographics of Respondents
4.2 Standardisation Practice in Uganda
4.3 Factors Affecting Implementation of DHCI Standards in Uganda
5 Discussion
5.1 Practice of DHCI Standardisation in Uganda, an Example of Resource-Constrained Setting
5.2 Factors Affecting Implementation of DHCI Standards in Uganda
6 Conclusion
References
Management of Healthcare and Medical Information Systems
Unpacking Privacy Calculus and Interplay of Data Privacy and Healthcare: Paths Towards Safeguarding Patient Empowerment
1 Introduction
2 Understanding Patient Data and Its’ Uses
2.1 Clinical Uses of Patient Data
2.2 Consumer Uses of Patient Data
2.3 Uses of Patient Data in Research and Analytics
3 Data Privacy Implications Centered Around Current Modern Healthcare Landscape
3.1 Impact of Big Data in Patient Empowerment and Improving Healthcare Management efficacy in Healthcare Ecosystem
3.2 Burgeoning Growth of Data Comes with Serious Privacy Threats
3.3 Major Data Privacy Implications Centered Around Current Modern Healthcare Landscape
4 Privacy Calculus: Impacts on Patient Empowerment and Healthcare Management
4.1 Privacy Calculus in Current Modern Healthcare Landscape
4.2 Privacy Calculus and Healthcare Management Efficacy
5 Healthcare Regulations to Address Privacy Calculus
5.1 Common Healthcare Regulations& Its Objectives
5.2 Impact of Healthcare Regulations in Addressing Privacy Calculus
6 Conclusion
References
Effects of Caregiver Support in the Adoption of Assistive Technologies for Online Patient Health Self-management
1 Introduction
2 Materials and Methods
2.1 Chronic Disease Self-Management
2.2 Model and Background Theory
2.3 Data Collection and Common Method Bias
3 Results
3.1 Internet-Panel Study Results
3.2 In-Person Study Results
3.3 Data Grouping
3.4 Individual Item and Construct Reliability Test
3.5 Model Analysis
4 Discussion
4.1 Analysis
4.2 Overall Model Assessment
4.3 Explanations
4.4 Conclusions
4.5 Future Research
References
Design and Analysis of Health/Medical Records
Standards for Structure in Clinical Therapy
1 Case Conceptualisation
2 A Clinical Approach
2.1 Conceptual Foundations
3 Dynamic Modelling
4 Application of Methods
5 Discussion
6 Conclusion
References
Obstetric Ultrasound Modelling and Analysis with Fractal Interpolation Methods
1 Introduction
2 Iterated Function System
3 Fractal Interpolation Functions
3.1 Affine Fractal Interpolation Functions
3.2 Piecewise Affine Fractal Interpolation Functions
3.3 Affine Fractal Interpolation Curves
3.4 The Fractal Dimension of an AFIC
4 Application to Medical Imaging
5 Conclusions
References
Healthcare/Medical Networking and Care Sharing
Predicting the Relationship Between Meal Frequency and Type 2 Diabetes: Empirical Study Using Machine and Deep Learning
1 Introduction
2 Literature Review
2.1 Relationship Between Meal Frequency and Diabetes
2.2 Machine Learning Model Selection
2.3 Predictor Selection
3 Method
3.1 Data Collection
3.2 Data Pre-possessing
3.3 Feature Selection
3.4 Data Cleaning
3.5 Model Algorithm Selection
3.6 Evaluation
4 Result and Discussion
4.1 Baseline Characteristic
4.2 Model Accuracy
4.3 Importance of Meal Frequency
4.4 Meal Frequency with Late-night-dinner Eating
5 Limitations
6 Conclusion and Future Works
References
Healthcare/Medical Data Representation and Analysis
Non-stationary Intrinsic Feature Assessment of Health/Medical Data Representation – Blood Pulse Signal for Example
1 Background
2 Morphology Assessment
3 Intrinsic Feature Representation
4 Spectral Assessment
5 Adaptive Spectral Assessment
6 Multi-Dimensional Assessment
7 Conclusion
References
Federated Learning: An Alternative Approach to Improving Medical Data Privacy and Security
1 Introduction
2 Literature Review
2.1 Medical Data: Definition, Sources, and Stakeholders in Data-Sharing
2.2 Challenges to Accessing Medical Data for Predictive Modelling
2.3 Existing Data Protection Approaches
3 Emerging AI-Based Solution: Federated Learning
3.1 Applications in Healthcare
3.2 Benefits of Federated Learning
3.3 Challenges of Federated Learning
3.4 Types of Federated Learning Algorithms
3.5 Federated Learning Literature Review Summary
4 Conclusion
References
Simulation and Modelling in Healthcare
Analysis and Application of Regression Models to ICU Patient Monitoring
1 Introduction
2 Related Work
3 Materials
4 Methods
4.1 Research Framework
4.2 Preprocesing
4.3 Regression Models
4.4 Results
5 Experimental Methodology
6 Results and Discussion
6.1 Results
6.2 Discussion
7 Conclusions
References
Total Hip Arthroplasty Modelling and Load Simulation, in COMSOL Multiphysics
1 Introduction
1.1 Anatomy and Biomechanics of the Hip Joint
1.2 Uncemented Prosthesis
1.3 Distribution of Forces on the Femur
1.4 Biomaterials
1.5 Finite Element Method—COMSOL Multiphysics
2 Methodology
2.1 Geometry
2.2 Material Properties
2.3 Physical Interface—Solid Mechanics
2.4 Computational Mesh
2.5 Studies Performed
3 Results and Discussion
3.1 Stationary Stress Studies
3.2 Stationary Strain and Deformation Studies
3.3 Dynamic Studies
4 Conclusion
References
Health and Medical Informatics Education
Work Disability Risk Prediction Using Machine Learning
1 Introduction
2 What Are the Stakeholders in the Work Disability Risk Prediction?
3 How Work Disability Risk Can Be Predicted Using Machine Learning?
3.1 Data
3.2 MHealth
3.3 MPension
3.4 Comparison of MHealth and MPension
4 What Are the Aspects of Ethical AI in the Work Disability Risk Prediction?
5 How Explainable Are the ML Methods for Work Disability Risk Prediction?
6 Discussion
7 Conclusions
References
Exploring Aerospace Health Informatics Core Competence: A Grounded Theory Perspective
1 Introduction and Context
2 The Conceptual and Theoretical Dimension
2.1 Information Systems and Grounded Theory
2.2 Mapping the Research Question to Methodology
2.3 Analysing the Interconnected Factors
3 The Transdisciplinary Dimension of the Underlying Foundation for Aerospace Health Informatics Core Competency
3.1 Literature Map of Information Systems Theory, Health Informatics, and Its Competency Area
3.2 The AIDH Competency Framework
3.3 Aerospace Medicine Competency
3.4 The Critical Factors of Aerospace Health Informatics Competency
4 Conclusion: The Aerospace Health Informatics Core Competency
References

Citation preview

Studies in Computational Intelligence 1112

Kevin Daimi Abeer Alsadoon Sara Seabra Dos Reis   Editors

Current and Future Trends in Health and Medical Informatics

Studies in Computational Intelligence Volume 1112

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, selforganizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

Kevin Daimi · Abeer Alsadoon · Sara Seabra Dos Reis Editors

Current and Future Trends in Health and Medical Informatics

Editors Kevin Daimi University of Detroit Mercy Detroit, MI, USA

Abeer Alsadoon Asia Pacific International College Sydney, NSW, Australia

Sara Seabra Dos Reis Polytechnique Porto Porto, Portugal

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-031-42111-2 ISBN 978-3-031-42112-9 (eBook) https://doi.org/10.1007/978-3-031-42112-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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 Paper in this product is recyclable.

Preface

Healthcare organizations are producing huge data. Such data is constantly increasing. Health and medical data need to be saved, retrieved, and processed to facilitate decision-making by medical professionals and contribute to health problem-solving. Health and medical informatics emerged as a multi-disciplinary field involving healthcare leaders, health professionals, physicians, and computing professionals. To this extent, Health and Medical Informatics focuses on computing technology to constructively improve the relationship between patients and health professionals and physicians through better understanding of health data and evidence-based critical decisions. The main goal of Health and Medical Informatics is to improve the quality and cost of healthcare. This book is organized into eight parts. The first part deals with Medical Imaging and 3D/4D Surgical Visualization, second part introduces Healthcare/Medical Information Systems Supporting Patients and the Public, third part presents Management of Healthcare and Medical Information Systems, fourth part covers Design and Analysis of Health/Medical Records, fifth part depicts Healthcare/Medical Networking and Care Sharing, sixth part introduces Healthcare/Medical Data Representation and Analysis, seventh part deals with Simulation and Modelling in Healthcare, and eighth part covers Health and Medical Informatics Education. The first part of the book presents the analysis and findings related to the early identification of dementia using the improved BAT optimization algorithm for segmenting brain subregions and deep learning for classification. It then focuses on the integration of medical imaging with computational modelling, which will allow for faster modelling, improved data accuracy, and earlier detection of cardiovascular and valvular anomalies. It further moves to proposing a tool that extracts data from computational tomography (CT) scans of long bones, applies filters to allow a distinction between cortical and cancellous tissue, and converts the tissues into a three-dimensional (3D) model that can be used to generate finite element meshes. In the second part, discussion of the public health benefits of POC devices in terms of patient engagement, health inequality, and health promotion, and their impact on healthcare systems is studied. This is followed by assessment of the efficacy of a digital platform in collecting feedback in ACH settings, identifying key features of the v

vi

Preface

platform, and examining the quality-of-care improvements achieved through its use. Finally, state of digital health communication infrastructure in LMICs with theory, standards, and factors affecting their implementation is presented with examples on international standardization in Uganda. The third part examines the data privacy implications of the contemporary healthcare environment, identifies seven significant challenges, and investigates the idea of privacy calculus and how it impedes patient empowerment and healthcare management effectiveness in the healthcare ecosystem. Furthermore, it investigates how to help make informed decisions through support from a caregiver and how this can motivate patients to adopt and use such assistive technologies. The fourth part shows how the implementation of a uniquely designed standardized process and training, embedded with health and medical informatics within template design could provide new insights needed to close the gaps in the current knowledge base. In addition, it focuses on modelling and analysis of obstetric ultrasound by using fractal interpolation. Firstly, a new way of representing ultrasounds achieving remarkable compression ratios while maintaining the quality of the original images is presented. Then, based on this representation, the possibility of grouping ultrasounds as well as the automatic detection of points of interest in them with the aim of diagnosis is examined. In the fifth part, a diabetes predictive model is built using machine learning methods. The relationship between meal frequency and the incidence rate of type 2 diabetes using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2014 is investigated. Seven machine learning and deep learning algorithms were employed to verify the hypothesis. A brief introduction of the blood pulse signal representation using assessment techniques and illustration of the results of the nonstationary intrinsic feature representations, followed by how to make medical data more accessible for medical research whilst addressing the ethical and technical issues around data privacy and data-sharing are the topics of the sixth part. The seventh part concentrates on a novel approach to tackle the issue of missing medical data in ICU patients through enabling the retrieval of values from multiple medical tests simultaneously with evaluation of the performance of the proposed model using several metrics, including RMSE, MAPE, and MAE. It then emphasizes performing stress and strain studies under different load conditions for the titanium and cobalt-chromium alloys in cementless prostheses. Finally, the eighth part introduces how machine Learning methods are efficient and economical in the screening of potential work disability risk cases. It then deals with the conceptual, theoretical, and underlying foundation for core competency of aerospace health informatics practice emphasizing the Australasian Institute of

Preface

vii

Digital Health’s health informatics new competency framework that ensures the dynamic nature of digital health and the challenges and opportunities it presents. Detroit, USA Sydney, Australia Porto, Portugal

Kevin Daimi Abeer Alsadoon Sara Seabra Dos Reis

Acknowledgements

The Current and Future Trends in Health and Medical Informatics book would not have been doable without the teamwork, inspiration, and collaboration of many people. We would like to first acknowledge the authors of all chapters in this book, who contributed their knowledge and expertise in Health and Medical Informatics. The Editors are also appreciative to the hard work of all chapter reviewers, who are listed below. Finally, we would like to express our gratitude to Dr. Thomas Ditzinger, the Editorial Director in charge of the Springer book series: Lecture Notes in Networks and Systems (LNNS) for his kindness, courtesy, professionalism, and support. We would also like to thank Saranya Sakkarapani, the project coordinator of Current and Future Trends in Health and Medical Informatics book, for his help. Asai Asaithambi, University of North Florida, USA Marta Barbosa, University of Porto, Portugal Vikraman Baskaran, Mercer University, USA Sergio Celada-Bernal, University of Las Palmas de Gran Canária, Spain João Seabra Castelhano. Universidade de Coimbra, Portugal Chia-Chi Joseph Chang, National Yang Ming Chiao Tung University, Taiwan Li-jing Arthur Chang, Jackson State University, USA Dillon Chrimes, University of Victoria, Canada Michal Chlebiej, Nicolaus Copernicus University, Poland Luís Pinto Coelho, Instituto Superior de Engenharia—Politécnico do Porto, Portugal Rosemeyre Amaral Cordeiro, University of Coimbra, Portugal Nectarios Costadopoulos, Charles Sturt University, Australia Sammy Danso, The University of Edinburgh Medical School, UK Vasileios Drakopoulos, University of Thessaly, Greece ix

x

Acknowledgements

Farnaz Farid, Western Sydney University, Australia Tzu-Chien Hsiao, National Yang Ming Chiao Tung University, Taiwan Zahra Keshavarz-Motamed, McMaster University, Canada William Klement, Ottawa University, Canada Irene Kopaliani, Princeton University, USA Yasuhiro Kotera, University of Nottingham, UK Oleg Reva, University of Pretoria, South Africa Alessandro Ruggiero, University of Salerno, Italy Jorge Luís Lopes Zeredo, University of Brasília, Brazil

Contents

Medical Imaging and 3D/4D Surgical Visualization Analysis of Brain Subregions by Segmentation of MRIs Using Improved BAT Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Chitradevi, S. Prabha, and A. Asaithambi

3

Enhancing Medical Imaging with Computational Modeling for Aortic Valve Disease Intervention Planning . . . . . . . . . . . . . . . . . . . . . . . Seyedvahid Khodaei and Zahra Keshavarz-Motamed

19

Construction of an Algorithm for Three-Dimensional Bone Segmentation from Images Obtained by Computational Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marta Barbosa, Francesco Renna, Nuno Dourado, and Rúben Costa

47

Healthcare/Medical Information Systems Supporting Patients and the Public Point-of-Care Devices in Healthcare: A Public Health Perspective . . . . . . Armita Zarnegar Digital Platforms to Support Feedback Processing in Aged Care Homes: Friend or Foe? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tanya Linden and Rosemary Fisher

75

93

State of Digital Health Communication Infrastructure in LMICs: Theory, Standards and Factors Affecting Their Implementation . . . . . . . 109 Andrew Egwar Alunyu, Mercy Rebekah Amiyo, and Josephine Nabukenya

xi

xii

Contents

Management of Healthcare and Medical Information Systems Unpacking Privacy Calculus and Interplay of Data Privacy and Healthcare: Paths Towards Safeguarding Patient Empowerment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Nazmus Sakib, Hari Sai Jogesh Veeramalla, Nisarga Allu Raghu Naidu, Fahim Islam Anik, Lakshman Reddy Vanga, Maria Valero, and Eklas Hossain Effects of Caregiver Support in the Adoption of Assistive Technologies for Online Patient Health Self-management . . . . . . . . . . . . . . 173 Reza Aria, Norm Archer, Vikraman Baskaran, and Bharat Shah Design and Analysis of Health/Medical Records Standards for Structure in Clinical Therapy . . . . . . . . . . . . . . . . . . . . . . . . . 201 Lucie-May Golbourn-King and Yasuhiro Kotera Obstetric Ultrasound Modelling and Analysis with Fractal Interpolation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Vasileios Drakopoulos and Polychronis Manousopoulos Healthcare/Medical Networking and Care Sharing Predicting the Relationship Between Meal Frequency and Type 2 Diabetes: Empirical Study Using Machine and Deep Learning . . . . . . . . . 235 Yiman Hunag, Farnaz Farid, and Basem Suleiman Healthcare/Medical Data Representation and Analysis Non-stationary Intrinsic Feature Assessment of Health/Medical Data Representation – Blood Pulse Signal for Example . . . . . . . . . . . . . . . . 261 Chia-Chi Joseph Chang Federated Learning: An Alternative Approach to Improving Medical Data Privacy and Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Joyce Chen, Farnaz Farid, and Mohammad Polash Simulation and Modelling in Healthcare Analysis and Application of Regression Models to ICU Patient Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Sergio Celada-Bernal, Carlos M. Travieso-González, Guillermo Pérez-Acosta, José Blanco-López, and Luciano Santana-Cabrera Total Hip Arthroplasty Modelling and Load Simulation, in COMSOL Multiphysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Nuno Gueiral and Elisabete Nogueira

Contents

xiii

Health and Medical Informatics Education Work Disability Risk Prediction Using Machine Learning . . . . . . . . . . . . . 345 Katja Saarela, Vili Huhta-Koivisto, Kai-Kristian Kemell, and Jukka K. Nurminen Exploring Aerospace Health Informatics Core Competence: A Grounded Theory Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Edi Nuryatno, Arwin Sumari, Astika Ayuningtyas, Anggraini Kusumaningrum, Asih Pujiastuti, and Hero Wintolo

About the Editors

Kevin Daimi received his Ph.D. from the University of Cranfield, England. He has a long academic and industry experience. His research interests include Computer and Network Security with emphasis on vehicle network security, Software Engineering, Data Science, Computational Intelligence, and Computer Science and Software Engineering Education. He has published several papers on vehicle security. He is the editor of seven books in cybersecurity and data science; Computer and Network Security Essentials, Innovation in Cybersecurity Education, Advances in Cybersecurity Management, Principles of Data Science, Breakthroughs in Digital Biometrics and Forensics, Principles and Practice of Blockchains, and Emerging Trends in Cybersecurity Applications, which were published by Springer. He is also the editor of the Proceedings of the ICR’22 and ICR’23 International Conference on Innovations in Computing Research book published by Springer Book Series: Advances in Intelligent Systems and Computing, and Lecture Notes in Networks and Systems, and ACR’23 International Conference on Advances in Computing Research book published by Springer Book Series: Lecture Notes in Networks and Systems. He has been chairing the annual International Conference on Security and Management (SAM) since 2012. He is also Program Chair of the annual International Conference on Innovations in Computing Research for the years 2022– 2024, and the annual International Conference on Advances in Computing Research for the years 2023– 2024. Kevin is a Fellow of the British Computer xv

xvi

About the Editors

Society (BCS), a Senior Member of the Association for Computing Machinery (ACM), and a Senior Member of the Institute of Electrical and Electronic Engineers (IEEE). He is the recipient of the Outstanding Achievement Award from the 2010 World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP’10) in Recognition and Appreciation of his Leadership, Service and Research Contributions to the Field of Network Security. He is currently Professor Emeritus of Computer Science and Software Engineering at the University of Detroit Mercy. Abeer Alsadoon received her Ph.D. from the University of Technology, Baghdad. She published more than 130 papers in A and B ranking journals and has more than 100 conference papers published in different peer review IEEE conferences, and she has received four (4) best paper awards. She edited an Emerging Trends in Cybersecurity Applications book published by Springer. Abeer received twenty (20) awards for teaching and research excellence from different Australian Universities. She has been recognized nationally as one out of five finalists for the prestigious 2019 Australian Women’s Agenda Leadership Awards in the category of Emerging Female Leader in the Government and Public Sector. She chaired the 2022 International Conference on Health Informatics and Medical System (HIMS’22), Las Vegas, USA. She is the conference chair of HIMS’23 conference too. She is the Program Chair of the 2022 International Conference on Innovations in Computing Research (ICR’22), Athens, Greece, the 2023 International Conference on Advances in Computing Research (ACR’23), Orlando, Florida, USA, the ICR’23, Madrid, Spain, and the ACR’24, Madrid, Spain. Abeer has been chairing the Program Committee of the annual IEEE International Conference on Innovative Technologies in Intelligent Systems & Industrial Application (CITISIA’20), Sydney, since 2020. She also chaired special sessions at different conferences. Abeer is a Senior Member of the Institute of Electrical and Electronic Engineers (IEEE). She has many years of academic experience. Abeer is currently an Associate Professor at the Education Centre of Australia (ECA)-Asia Pacific International College (APIC). She was a Dean of Scholarship and Research at

About the Editors

xvii

Kent Institute Australia. In addition, Dr. Alsadoon is an Adjunct Associate Professor at Charles Sturt University (CSU), Australia. Sara Seabra Dos Reis received her Ph.D. from the Catholic University, Portugal. She is an assistant professor at the School of Engineering, Polytechnic of Porto, Portugal. Sara is currently the Co-director of the Biomedical Engineering program. She has advised many students in their projects in the field of Health and Medical Informatics. Her resesarch interests include Medical Informatics, Innovation and Management in Healthcare, and Artificial Intelligence Ethics. She has received a national award in the drugs innovation field from the Portuguese National Pharmacies Association. She has published several papers on the field of medical informatics and its applications to healthcare. She is currently the Sessions/Workshops Chair of the 2023 International Conference on Health Informatics and Medical Systems (HIMS’23) and was the Posters Chair of HIMS’22, in addition to being a Program Committee member for both HIMS’22 and HIMs’23. Furthermore, she is a Program Committee member of the 2023 International Conference on Innovations in Computing Research (ICR’23)—Health Informatics and Medical Imaging Track, Madrid, Spain, and ICR’22, Athens, Greece. Sara is editor of the Emerging Advancements for Virtual and Augmented Reality in Healthcare book published by IGI Global, and a guest editor of the special edition of the Bioengineering Journal—Bioengineering Techniques and Applications Against COVID-19, which was published by MPDI.

Medical Imaging and 3D/4D Surgical Visualization

Analysis of Brain Subregions by Segmentation of MRIs Using Improved BAT Optimization D. Chitradevi, S. Prabha, and A. Asaithambi

1 Introduction Dementia is normally characterized by memory loss, diminished perception, and forgetfulness [1]. There are several types of dementia such as frontotemporal dementia, Alzheimer’s Disease (AD), vascular dementia, and others. AD is the most prevalent kind of dementia around the world among older persons. AD is a neuro-pathological impairment/disorder that is continuous, irreversible, and it damages memory skills, cognitive abilities, the ability to carry out daily tasks, and organizational functions over time. The goal of AD pathology is to alter or harm the tissues of the brain. This illness is thought to be one of the major contributors to the high death rates around the globe. The onset of AD is thought to be around 20 years or longer before symptoms actually appear, with minute changes in the brain that are invisible to the person experiencing it. After a few years, a person’s brain begins to alter, as seen by symptoms like memory loss and language difficulties. This means that neurons in the areas of the brain that entail cognitive functions and impairment in thinking and learning have shown symptoms. Symptoms of AD are typically present in people for several years. These symptoms often worsen with time and begin to affect the person’s ability to do daily tasks. The person is said to have dementia in this instance as a result of AD. As the condition progresses, the nerve cells and other parts of the brain are impacted and killed. The brain’s nerve cells and other areas eventually impact routine activities like walking, running, and swallowing [1–6]. A person with D. Chitradevi SRM Institute of Science and Technology, Tiruchirappalli Campus, Trichy, Tamil Nadu, India S. Prabha Saint Johns, FL, USA A. Asaithambi (B) University of North Florida, Jacksonville, FL, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Daimi et al. (eds.), Current and Future Trends in Health and Medical Informatics, Studies in Computational Intelligence 1112, https://doi.org/10.1007/978-3-031-42112-9_1

3

4

D. Chitradevi et al.

advanced AD will be bedridden and require round-the-clock care. AD is fatal in the end [1, 7]. AD is often associated with an array of brain abnormalities because of the accumulation of aberrant tau protein inside the neurons and beta-amyloid protein outside the neurons. Tau tangles interfere with the movement of nutrients and other substances inside the neurons, while beta-amyloid plaques aid synaptic contact between neurons. The aberrant tau tangle can spread throughout the different areas of the brain if the amount of beta-amyloid protein deposition exceeds a certain threshold. Toxic amyloid beta and tau protein trigger the brain’s immune system cells, which are known as microglia. It spreads the debris from dead and dying cells while attempting to eliminate the hazardous proteins [8, 9]. Although shrinkage and brain atrophy are based on cell loss, swelling and atrophy in the brain are produced by aberrant protein dispersion. The loss of cells disrupts typical brain functions and reduces the brain’s capacity to digest glucose. During the early stages of AD, the brain begins to interfere with an individual’s ability to operate, and gets increasingly injured as the brain cell destruction persists, leading to an adverse impact on cognitive performance. The tangles and plaques in this case impact not just the brain’s cognitive function but also other areas of the brain. The injured nerve cells then exhibit behavioral symptoms, such as personality changes, depression, and a loss of interest in activities, as well as cognitive impairment symptoms, including difficulty making decisions or memory loss. Finally, fundamental body processes like swallowing, walking, talking get affected [1–6]. These issues arise due to the breakage of neuronal functions, leading to atrophy. The atrophy starts from the brain regions of Grey Matter (GM), White Matter (WM), Hippocampus (HC) and Corpus Callosum (CC). Generally, all these regions are responsible for the daily activities in terms of connecting the brain with other parts of the body, communication, long-term memory, short-term memory, etc. Hence, segmenting these brain sub-regions in the medical images will be helpful to analyze the changes in the brain. Magnetic Resonance Imaging (MRI) serves as the main imaging tool that helps quantify the changes in the brain. The diminished GM, enlargement of the ventricle, and the shrinkage of HC areas in the Medial Temporal Lobe (MTL) are particularly useful indicators for detecting atrophy. In this study, multi-level thresholding has been used for segmentation as it allows the use of several thresholds to divide the pixels into various groups [2–6, 10]. In relation to this study, we refer to optimization as the process of choosing the best elements for a given set of criteria from a variety of viable options. It has a wide variety of applications in business and scientific research [10]. Optimization techniques are usually based on a framework for modeling that uses an objective function to be optimized [11]. Nature-based optimization algorithms such as fireflies, ants, or bats are commonly used for the purpose of handling biological systems [12]. Such algorithms yield results that are highly competitive, superior at solving complicated scenarios, and are capable of provide better optimal solutions when compared to other optimization strategies [2]. Convolutional Neural Network (CNN) modelling is a type of machine learning technique that can be used to classify and identify target objects from images. The image categorization determines whether an image under investigation has the desired object or disease. CNNs have recently enabled

Analysis of Brain Subregions by Segmentation of MRIs …

5

improvements in the analysis of the AD [13] from segmented brain subregions. CNNs use densely connected layer(s) for classification and convolutional layers for feature extraction. The current study involves image acquisition, preprocessing, and segmentation. The data used in this study has been collected from the Chettinad Health City Hospital, Chennai, India, and preprocessed to enhance contrast and to perform skull stripping. The study uses the BAT and the improved BAT optimization algorithms to segment the brain subregions to help observe the disease manifestation of AD as well as ongoing structural alterations. Similarity and overlapping metrics are used to obtain both qualitative and quantitative results. Then, a newly developed deep learning classifier has been used as a validation tool for an optimization technique to classify normal and AD images. This evaluation method aids in the clinical diagnosis of AD.

2 Related Work In this section, we explore and describe previous works reported in the literature that are related to the analysis of AD. Alzheimer’s disease (AD) is the most prevalent type of dementia, and a progressive neurological illness most frequently characterized by initial memory loss and cognitive decline that can eventually impact behavior, speech, visuospatial orientation, and the motor system. The only reliable way to diagnose clinical AD dementia is by post-mortem neuropathological analysis, but research centers that can measure the amyloid and tau burden in patients while they are still alive are challenging this long-held belief. Moreover, AD has a protracted asymptomatic preclinical stage, and even people with cognitively normal abilities might develop the condition [1, 7–9]. On the basis of the visualization of the brain structures, numerous investigations including AD biomarkers are recommended to analyze the atrophy of the brain’s sub regions. The functions of all recognized hallmarks allow for a precise diagnosis of AD, to determine the prognosis of AD, to track the stages of AD, and to assess the changes induced by medication. A perfect hallmark would be able to identify the precise pathophysiological characteristics of AD that are absent in a healthy state. The surfaces of the GM, WM, HC, ventricle, CSF, and CC can be identified via MRI. The CC, GM, ventricle, WM, and HC brain areas are important indicators of AD, according to the aforementioned literature. In order to analyze AD, and to to evaluate the changes in the brain and recognize Normal Controls (NC) and AD, the brain images need to be segmented to recognize the subregions of GM, WM, ventricle, CC, and HC. Several studies have been carried out to analyze Alzheimer’s disease and find the biomarkers of AD. On the basis of the visualization of the brain structures, numerous investigations including AD biomarkers are recommended to analyze the atrophy of the brain’s subregions [14]. MRI is a major imaging modality to analyze the structural changes of the brain sub regions [2–6]. MRI helps identify the surfaces of the GM, WM, HC, ventricle, CSF and CC. A variety of segmentation techniques that use clustering-based, region-based,

6

D. Chitradevi et al. Chettinad Dataset

PREPROCESSING Otsu Thresholding

SEGMENTATION BAT Optimization Improved BAT

CLASSIFICATION CNN

Fig. 1 Workflow for segmentation and classification of brain MRI

thresholding-based, and graph-based approaches may be used for this purpose. Of these, the thresholding approach is most frequently used. Two types of thresholding techniques exist. The first, known as bi-level thresholding, uses a single threshold to differentiate between two classes (background and object). The second, known as multi-level thresholding allows for the use of multiple thresholds and hence can identify several classes. Bi-level thresholding can be used on regular images, although it is computationally expensive. On the other hand, multi-level thresholding can segment complex images [3]. Multi-level thresholding has been developed to address the shortcomings of bi-level thresholding techniques and to solve difficult situations within the restrictions of time and resources. Real-time optimization problems can lead to a variety of issues. The demand for optimization techniques grows along with complexity. Prior to the development of heuristic optimization techniques like genetic algorithms (GA), mathematical techniques were employed to solve the optimization problems [15]. Particle Swarm Optimization (PSO) was then used to simulate bird behaviour [16]. These two classes of methods have undergone improvements and have been used in various contexts [17, 18]. In order to address a wide range of issues, bio-inspired optimization techniques as the BAT optimization (BAT) [19, 20], Whale Optimization Algorithm (WOA) [21], Moth-Flame Optimization (MFO) [22], and Dragonfly Algorithm (DA) [23] have been used. The workflow (Fig. 1) for the current study begins with data gathering from Chettinad Hospital, Chennai, India. The next step, namely preprocessing, is used to minimize interventions via removal of the skull, skin, and muscle, and to improve the quality of the images. This step is followed using the BAT and the improved BAT (IBAT) approaches to segment the internal regions of the brain, namely the ventricle, GM, HC, WM, and CC. The segmentation performance has been evaluated using a variety of ground truth and segmented area measurements. Finally, the CNN classifier is used to classify the pictures of the subregions as Normal Control (NC) and Alzheimer Disease (AD).

3 Methods and Materials 3.1 Dataset The dataset used in this study has been acquired from Chettinad Hospital, Chennai, India. These images, known as the T2 weighted slices of brain MR images, are taken in the axial, coronal, and sagittal planes. A total of 100 normal (NC) and 100 abnormal

Analysis of Brain Subregions by Segmentation of MRIs …

7

controls (AD) are included in the dataset. The 100 normal images came from 73 male and 27 female patients in the age range of 60–65 years. The 100 abnormal images came from 68 male and 32 female patients in the age range of 67–73 years.

3.2 Image Preprocessing The input image data, as stated previously, is subject to preprocessing with techniques like skull stripping and contrast enhancement. In skull stripping, Otsu’s thresholding is used to remove non-brain tissues from input images, such as fat, scalp, muscle, skull, and skin. For contrast enhancement, histogram equalization has been used. Figure 2 shows a sampling of raw and preprocessed images.

3.3 Segmentation The goal of segmentation in image processing is to divide an image into segments or objects in such a way that the pixels in each segment or object share a common

Plane

Normal Controls Raw Input Preprocessed

Plane

Axial

Axial

Coronal

Coronal

Sagittal

Sagittal

Fig. 2 Raw and preprocessed images in different planes

Alzheimer’s Disease Raw Input Preprocessed

8

D. Chitradevi et al.

property. One of the most commonly used methods for image segmentation is known as thresholding, which uses the image histogram of the gray levels to carry out the segmentation. The thresholding used to segment an image into two classes (like an object and the background), is called bi-level thresholding. When thresholding is used to segment an image into more than two classes, it is called multi-level thresholding. Suppose . I represents the set of gray values of an image with . L gray levels coming from the set .{0, 1, . . . , L − 1}. Then, when .k-level thresholding is carried out, the set . I is partitioned into .k + 1 subsets .{I0 , I1 , . . . , Ik } such that any pixel with gray level . p ∈ I will belong to . I j if . p satisfies the condition .x j ≤ p < x j+1 for . j = 0, 1, 2, . . . , k. Here, .x0 = 0, .xk+1 = L, and .x1 , x2 , . . . , xk ∈ {1, 2, . . . , L − 1} are called thresholds, which are usually determined by optimizing an objective function based on the histogram of the image. Thus, the .k-level thresholding will segment the image into .k + 1 classes. This means that the case .k = 1 corresponds to bi-level thresholding, and in this case the image is segmented into two classes with a single threshold.

3.3.1

The BAT Algorithm

Based on the foraging behavior and highly developed echolocation skills of bats, a swarm intelligence algorithm for global optimization, known as the BAT algorithm, has been developed [19, 20]. For the present study, the BAT algorithm has been adapted to determine the vector .x = (x1 , x2 , . . . , xk )T of optimal thresholds that will maximize an objective function denoted by .g(x), also referred to as the fitting function sometimes. Two objective functions are often reported in the literature. The first one, due to Otsu [24] is based on the between-class variances. The second one, due to Kapur [25] is based on entropy. The search for the optimal thresholds proceeds by considering .n bats positioned initially at .xi = (xi,1 , xi,2 , . . . , xi,k )T for .i = 1, 2, . . . , n, and monitoring the movement of the bats according to their foraging behavior. The optimal threshold will be then identified as .x∗ for which the largest value of the objective function is achieved. When searching for preys, bats generate loud and brief pulse sounds and sense the echo of their pulses when hit any obstacles. Bats have the ability to determine the locations of the obstacles and distinguish between barriers and preys. The bats modify their velocities, sonar frequencies, and the loudness based on the locations of the prey. Thus, the BAT algorithm uses these quantities as its parameters and simulates the BAT movements repeatedly as many times as desired to determine the optimal thresholds.

Analysis of Brain Subregions by Segmentation of MRIs …

9

Algorithm 1 BAT algorithm for (k + 1)-level thresholding with n bats 1: procedure BAT(n, k, Max_Iterations) 2: for i ← 1 to n do 3: Initialize xi0 , vi0 , f i0 , ri0 , Ai0 4:

x∗ ← x0j for which g(x0j ) = max g(xi0 )

5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17:

for t ← 1 to Max_Iterations do for i ← 1 to n do Update f i , vi , xi using Eqs. (1a)–(1c) xold ← x j for some random j ∈ {1, 2, . . . , n} for i ← 1 to n do if rand > rit then Set xinew using Eq. (2) if rand < α Ait−1 and g(xinew ) > g(xit ) then xit ← xinew Ait ← α Ait−1 rit+1 ← ri0 · (1 − e−γ t ) else xit ← xit−1

18:

1≤i≤n

x∗ ← xtj for which g(xtj ) = max g(xit ) 1≤i≤n

Some of the details pertaining to the equations and formulas used in the BAT algorithm are explained here, and a pseudocode for the BAT algorithm is presented. Suppose the .ith bat is initially located at position .xi0 , moving at velocity .vi0 , emit pulses at sonar frequencies . f i in a specified range from . f min to . f max , with loudness in a specified range from . Amin to . A0 , at a pulse rate of .ri0 in the range .[0, 1]. The bat locations are updated by taking discrete time steps indicated by .t, with .t = 0 representing the initial location, according to f = f min + ( f max − f min )β,

. i

t .vi t .xi

= =

vit−1 xit−1

+ +

(xit−1 vit ,

∗

− x ) fi ,

(1a) (1b) (1c)

where .β is a vector of random numbers uniformly distributed in the range .[0, 1], and x∗ is the best solution identified thus far. Initially, .x∗ is the .xi for which the objective function .g(x) is the largest. Remember that each .xi should satisfy the conditions . x i, j < x i, j+1 . Thus, whenever the value of any . x i, j exceeds the allowed limit, then it is set to the allowed limit. Once the bats get close enough to their preys, they adjust their loudness and approach the preys more quietly, and this is referred to as local search. For the local search, each bat is assigned the location .

xnew = xold + ε At ,

. i

(2)

in which .xold is randomly chosen from among the current optimal solutions, . At is the average of the loudness of all .n bats at this time step, and .ε ∈ [−1, 1] is a random number.

10

D. Chitradevi et al.

At this point, it is important to emphasize that the BAT algorithm combines the standard swarm optimization algorithm and a local search which is essentially controlled by the pulse rate and loudness. Thus, the loudness and the pulse rate are also updated if it is evident that the new solutions are improving, or equivalently the bats are moving towards the optimal solution. These updates are made according to .

Ait+1 = α Ait ,

rit+1 = ri0 (1 − e−γ t ),

(3)

where .α, γ are constants that satisfy the conditions .0 < α < 1 and .γ > 0. Note that these conditions ensure that that the loudness progressively decreases and the pulse rate increases as the procedure is iterated (or as .t → ∞). The improved BAT algorithm differs from the original BAT algorithm in how this local search is carried out. This improvement will be further explained in the next subsection.

3.3.2

The Improved BAT Algorithm

The BAT algorithms works well for small values .k, namely, when the number of thresholds is fairly small. When tested with increasing numbers of thresholds, the BAT algorithm failed frequently, especially when Kapur’s objective function [25] is used. An improved version of the BAT algorithm [26] is able to overcome this difficulty by modifying the local search step in the BAT algorithm through a combination of the local search step of the BAT algorithm with a differential evolution step which uses crossover and mutation operators [27]. Algorithm 2 Improved BAT algorithm for (k + 1)-level thresholding with n bats 1: procedure BAT(n, k, Max_Iterations) 2: for i ← 1 to n do 3: Initialize xi0 , vi0 , f i0 , ri0 , Ai0 4:

x∗ ← x0j for which g(x0j ) = max g(xi0 )

5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17:

for t ← 1 to Max_Iterations do for i ← 1 to n do Update f i , vi , xi using Eqs. (1a)–(1c) xold ← x j for some random j ∈ {1, 2, . . . , n} for i ← 1 to n do if rand > rit then > This is where the improvement occurs Set xinew using Eq. (4) if rand < α Ait−1 and g(xinew ) > g(xit ) then xit ← xinew Ait ← α Ait−1 rit+1 ← ri0 · (1 − e−γ t ) else xit ← xit−1

18:

1≤i≤n

x∗ ← xtj for which g(xtj ) = max g(xit ) 1≤i≤n

Analysis of Brain Subregions by Segmentation of MRIs …

11

The improvements are described in this subsection, and a pseudocode for the Improved BAT algorithm, highlighting where the improvement occurs is also presented. This strategy helps the algorithm to avoid getting stuck at local optima to a great extent. Also, in order to increase the diversity of the bats and accomplish both precision and search efficiency, the binomial “DE/rand/1/bin” scheme is utilized, which is a significant improvement. The improved BAT algorithm replaces the local update defined by Eq. (2) with

xnew

. i

⎧ dif ⎪ if rand1 > rit , ⎨xi , = x∗ + ε Ait if g(x∗ ) > g(xit ), ⎪ ⎩ t−1 otherwise, xi

(4)

with dif t t t i, j = xc, j + F(xa, j − xb, j ),

.x

for j = 1, 2, . . . , k,

if (rand2 < C p ) or j = jrand ,

(5) where .a, .b, .c are randomly picked bats, . F the differential weight, .C p the crossover probability, and. jrand is a randomly picked index from.{1, 2, . . . , k}. This modification corresponds to changing the line 11 in the pseudocode when transitioning from the BAT algorithm to the Improved BAT algorithm. Line 11 in the pseudocode for the BAT algorithm uses Eq. (2), whereas Eq. (4) is used in line 11 of the Improved BAT algorithm. Note that any reference to a variable like rand, .rand1 , or .rand2 in the pseudocode or otherwise means a uniformly distributed random number in .[0, 1]. Finally, if any updates to any of the thresholds exceed the limits imposed on them, they are set to the appropriate corresponding limits. Note that in both the BAT and the Improved BAT algorithms, .xinew is discarded if it does not help increase the objective function value (lines 12 through 17 in the pseudocode). More details of the improved BAT algorithm are available in [26].

3.4 Classification With accurately segmented images of brain subregions, the classification can be done more confidently. The classification process entails several steps including feature extraction, feature selection, dimensionality reduction, and feature-based algorithm selection. In the area of extensive and complex medical image analysis, deep learning classifiers take care of these steps. In particular, convolutional neural networks (CNN) produce better outcomes than other types of neural networks for medical image analysis [28]. The main goal of the current study has been segmentation. Therefore, the classification step is used as validation step for the segmentation process. The segmented subregions are used to classify images as belonging to either the class of Normal Controls (NC), or Alzheimer Disease (AD). This is accomplished in

12

D. Chitradevi et al.

two steps, namely feature extraction, and classification. Feature extraction is the most challenging step of extracting the most dominating, appropriate, and correct features from the segmented images that can contribute effectively in the accurate identification of AD patients. A suitable method for automatically selecting and classifying the best features is thus required. The current study uses convolutional neural networks (CNNs), which can learn how to select the precise features for various images. Both the feature extraction and classification phases of CNNs contain layers. The the fundamental structures of the images are discovered using convolutional layers and masking. A subsampling layer is used to reduce the amount of features. These layers are fully linked and can be regarded as a pooling layers or feature extractor layers. The feature extractor approach produces outputs that feed into and are entirely relevant to the classification process. It takes one or more hours to train the datasets and acquire the results from the output layer. The connected layer will eventually acquire the segmented brain sub regions in order to classify the patients with AD and NC. Evaluation metrics of specificity, sensitivity, and accuracy have been used to illustrate how the high-quality segmentation achieved by the BAT and the improved BAT algorithms leads to superior classification.

4 Results and Discussion In this study, the BAT and the improved BAT optimization algorithms have been used with multilevel image thresholding to segment the different regions of the brain. Table 1 shows the vital parameters used. Figure 3 shows the segmented images of the brain subregions of interest when the BAT and the Improved BAT optimization algorithms are used. The BAT and the improved BAT algorithms also use additional control parameters that directly enhance their effectiveness. Table 2 summarizes the values used in this study for these parameters.

Table 1 Parameters used for the BAT and the improved BAT algorithms Parameters Brain region GM WM CC Ventricle

HC

# Bats Max_Iterations .k

25 100 12

25 100 6

25 100 6

25 100 8

25 100 10

Table 2 Additional parameters used for the BAT and the improved BAT algorithms 0 . f max .ri . Ai .β .F Parameters . f min Values

0.2

0.2

0.5

0.99

0.9

0.75

.C p

0.95

Analysis of Brain Subregions by Segmentation of MRIs …

Region

Normal Controls BAT Improved BAT

Region

GM

GM

WM

WM

CC

CC

Ventricle

Ventricle

HC

HC

13 Alzheimer’s Disease BAT Improved BAT

Fig. 3 Brain subregions segmented using the BAT and improved BAT algorithms

The BAT algorithm performs satisfactorily when Otsu’s [24] objective function is used. However, when Kapur’s [25] objective function is used, the BAT algorithm faces difficulties for large values of .k as it appears to get stuck at local optima. On the other hand, the improved BAT algorithm produces excellent results. The segmentation quality of the improved BAT algorithm is better than what is possible with the BAT algorithm, as can be seen from Fig. 3. For both the BAT and the improved BAT algorithms, the segmentation quality is measured in terms of the similarity between the segmented images and the ground truth images with the Structural Similarity Index Measure (SSIM) and the Feature Similarity Index Measure (FSIM).

14

D. Chitradevi et al. CC

WM

GM

Ventricle

HC

Fig. 4 Similarity measures for segmented images

From Fig. 4 it is clear that the SSIM is better than the FSIM for all the brain subregions considered. Also, it is evident that the improved BAT algorithm performs better than the BAT algorithm for all the brain subregions. The best accuracy of 93.4% has been achieved by the improved BAT algorithm. In addition to comparing the performances of the BAT and the improved BAT algorithms, this study also compared the present approach with previously used methods for segmentation of the brain subregions. This is especially important because accurately segmented images are crucial to the study of progression of diseases such as dementia, AD, brain cancer, and epilepsy. As a result, numerous studies have proposed various methods for tracking the progression of brain diseases, including Fuzzy C means, Grey Wolf Optimizer (GWO), Active contour-Boosting-Chan/Vese (ABC), and Wind Driven Optimization (WDO). Table 3 provides a comparison of the present study with some previous studies [29–31], showing that the present study has achieved the highest performance. As stated previously, the quality of segmentation impacts how well images can be classified. Shown Table 4 are the results obtained in the current study when the segmented subregions are classified as NC or AD, using from the BAT algorithm and the improved BAT algorithm for segmentation. In Table 4, the improved BAT algorithm is abbreviated as IBAT. Another validation instrument often used to illustrate the quality of classification is known as Receiver Operating Characteristic (ROC) curve, which is shows the relationship between sensitivity and specificity. Figure 5 shows the ROC curves resulting from the classification using the images segmented by the BAT and the IBAT algorithms. The fact that all of the ROC curves are skewed significantly to the left indicates that both the BAT and the IBAT algorithms for segmentation are able to

Analysis of Brain Subregions by Segmentation of MRIs … Table 3 Comparison of present study with previous studies Dataset Number of images Pang et al. [29] Singh et al. [30] Pham et al. [31] Current

SATA 35 0 database BrainWeb Different slices of an image database BrainWeb Different slices of an image database Real time data 100 100

15

Selected regions

Segmentation accuracy (%)

HC

87.70

GM, WM, CSF GM, WM, CSF GM, WM, CC, HC

91.74 92.90 93.40

Table 4 Classification results for brain subregions Metric

Subregion GM

WM

Ventricle

CC

HC

BAT

IBAT

BAT

IBAT

BAT

IBAT

BAT

IBAT

BAT

IBAT

True Positive Rate (TPR)

0.910

0.913

0.880

0.890

0.910

0.910

0.890

0.900

0.910

0.930

False Positive Rate (FPR)

0.090

0.087

0.120

0.100

0.090

0.090

0.110

0.100

0.090

0.070

False Negative Rate (FNR)

0.090

0.090

0.088

0.085

0.089

0.089

0.089

0.089

0.089

0.091

Precision (%)

91

91

87

89

90

91

88

90

90

92

Recall (%)

90

90

88

85

89

89

89

89

89

91

F1-score (%)

91

91

88

87

89

89

89

89

89

91

GM

CC

WM

Ventricle

Fig. 5 ROC curves for the BAT and the IBAT algorithms

CC

16

D. Chitradevi et al.

Fig. 6 Validation of HC segmentation with MMSE scores

result in superior classification accuracy. Also, between the two, since the maximum values for all the regions achieved by the IBAT algorithm are located more to the left side of the graph when compared to those for the BAT algorithm, it is evident that the IBAT algorithm has performed better than the BAT algorithm. Finally, while segmentation is the main focus of this study, the role of accurate segmentation in classification must be highlighted even further. For the purpose of this study, the effectiveness of the BAT and improved BAT algorithms in segmenting the HC subregion in particular has been validated by examining the relationship between the Mini-Mental State Examination (MMSE) scores of patients and the the pixel densities of their HC regions segmented using the BAT and the improved BAT algorithms. The MMSE scores range from 0 to 30. If the MMSE score is between 26 and 30, the patient is considered to be normal, and if the score is below 18, the patient is considered to be have AD. The pixel densities of the normal HC region images range from 91 to 94, whereas in the images of HC regions of AD patients, the pixel densities range from 81 to 85. Figure 6 confirms this relationship between the pixel densities of the segmented HC regions in the NC and AD classes and the corresponding MMSE scores.

5 Conclusions Shape variations in local and global structures that can be seen in magnetic resonance images (MRI) can be used to define Alzheimer’s disease. Several optimization approaches are used to analyze and quantify these structural changes. The present study has examined some key MRI optimization techniques to assess the anatomical changes in distinct disease locations. Using the information acquired from the Chettinad Health City in Chennai, India, 100 normal and 100 AD-T2 weighted pictures have been taken into consideration. After carrying out preprocessing of these

Analysis of Brain Subregions by Segmentation of MRIs …

17

pictures using Otsu’s threshold and histogram equalization methods, the brain subregions of GM, WM, CC, ventricle, and HC have been segmented using the BAT and the improved BAT optimization algorithms. Quantitative and qualitative validation has been performed with ground truth images. The study finds that the improved BAT algorithm produces better results when compared to the BAT algorithm without the improvement. The relationship between the segmented region’s pixel intensity values and MMSE scores has been used to validate the robustness of the segmentation algorithms. Based on the results of this work, it can be said that the suggested method performs well in determining the BSR’s shape variation, which aids clinicians in making judgements on how best to proceed with patient care.

References 1. Alzheimer’s Association et al., 2014 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 10(2), 47–92 (2014) 2. D. Chitradevi, S. Prabha, Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease. Appl. Soft Comput. 86, 105857 (2020) 3. D. Chitradevi, S. Prabha, A.D. Prabhu, Diagnosis of Alzheimer disease in MR brain images using optimization techniques. Neural Comput. Appl. 33, 223–237 (2021) 4. D. Chitradevi, S. Prabha, Analysis of Alzheimer disease using optimization techniques, in 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII) (IEEE, 2020), pp. 1–5 5. S. Prabha, K. Sakthidasan@ Sankaran, D. Chitradevi, Efficient optimization based thresholding technique for analysis of Alzheimer MRIs. Int. J. Neurosci. 133(2), 201–214 (2023) 6. D. Chitradevi, S. Prabha, MR brain screening using optimization techniques—a survey. Curr. Med. Imaging (2021) 7. B.A. Gordon, T.M. Blazey, Y. Su, A. Hari-Raj, A. Dincer, S. Flores, J. Christensen, E. McDade, G. Wang, C. Xiong et al., Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol. 17(3), 241–250 (2018) 8. R.J. Bateman, C. Xiong, T.L.S. Benzinger, A.M. Fagan, A. Goate, N.C. Fox, D.S. Marcus, N.J. Cairns, X. Xie, T.M. Blazey et al., Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med. 367, 795–804 (2012) 9. N. Puthiyedth, C. Riveros, R. Berretta, P. Moscato, Identification of differentially expressed genes through integrated study of Alzheimer’s disease affected brain regions. PLoS ONE 11(4), e0152342 (2016) 10. C. Ledig, A. Schuh, R. Guerrero, R.A. Heckemann, D. Rueckert, Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Sci. Rep. 8(1), 11258 (2018) 11. N. Bell, B. John Oommen, A novel abstraction for swarm intelligence: particle field optimization. Auton. Agents Multi-Agent Syst. 31, 362–385 (2017) 12. S.-S. Guo, J.-S. Wang, X.-X. Ma, Improved bat algorithm based on multipopulation strategy of island model for solving global function optimization problem. Comput. Intell. Neurosci. 2019 (2019) 13. A. Hossain, M.T. Islam, T. Rahman, M.E.H. Chowdhury, A. Tahir, S. Kiranyaz, K. Mat, G.K. Beng, M.S. Soliman, Brain tumor segmentation and classification from sensor-based portable microwave brain imaging system using lightweight deep learning models. Biosensors 13(3), 302 (2023)

18

D. Chitradevi et al.

14. Y. Zhou, C. Tan, D. Wen, H. Sun, W. Han, Y. Xu, The biomarkers for identifying preclinical Alzheimer’s disease via structural and functional magnetic resonance imaging (2016) 15. J.H. Holland, Genetic Algorithms. Sci. Am. 267(1), 66–73 (1992) 16. J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4 (IEEE, 1995), pp. 1942–1948 17. T.X. Pham, P. Siarry, H. Oulhadj, An improved particle swarm optimization algorithm for MRI image segmentation, in Proceedings of the 13th International Conference on Artificial Evolution, EA 2017 (2017), pp. 277–284 18. X. Cao, J. Miao, Y. Xiao et al., Medical image segmentation of improved genetic algorithm research based on dictionary learning. World J. Eng. Technol. 5(01), 90 (2017) 19. X.-S. Yang, Optimization and metaheuristic algorithms in engineering. Metaheurist. Water Geotech. Transp. Eng. 1, 23 (2013) 20. X.-S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (2010), pp. 65–74 21. S. Mirjalili, A. Lewis, The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016) 22. S. Mirjalili, Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015) 23. S. Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, 1053–1073 (2016) 24. N. Otsu, A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979) 25. J.N. Kapur, P.K. Sahoo, A.K.C. Wong, A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Gr. Image Process. 29(3), 273–285 (1985) 26. A. Alihodzic, M. Tuba, Improved bat algorithm applied to multilevel image thresholding. Sci. World J. 2014 (2014) 27. R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341 (1997) 28. T. Jo, K. Nho, A.J. Saykin, Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front. Aging Neurosci. 11, 220 (2019) 29. S. Pang, J. Jiang, L. Zhentai, X. Li, W. Yang, Yu. Meiyan Huang, Y.F. Zhang, W. Huang, Q. Feng, Hippocampus segmentation based on local linear mapping. Sci. Rep. 7(1), 45501 (2017) 30. C. Singh, A. Bala, A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images. Appl. Soft Comput. 68, 447–457 (2018) 31. T.X. Pham, P. Siarry, H. Oulhadj, Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Appl. Soft Comput. 65, 230–242 (2018)

Enhancing Medical Imaging with Computational Modeling for Aortic Valve Disease Intervention Planning Seyedvahid Khodaei and Zahra Keshavarz-Motamed

1 Introduction 1.1 Cardiovascular Disease Cardiovascular disease is a prevalent worldwide epidemic that has a significant impact on millions of people every year, and it carries a considerable global economic burden. According to statistics, in 2017, cardiovascular diseases were responsible for approximately 17.8 million deaths, which is the primary cause of death globally, and it accounts for 37% of premature deaths from noncommunicable diseases [1, 2]. In Canada, cardiovascular disease causes one out of every four deaths, and it costs the Canadian economy more than $22 billion each year [3]. The United States alone projects that by 2035, annual medical costs and lost productivity due to cardiovascular disease will surpass USD$749 billion and USD$368 billion, respectively, adding up to over USD$1 trillion in total costs [4]. Similarly, in 2017, the European Union economy’s total estimated cost of cardiovascular diseases was e210 billion [5]. According to projections made prior to the COVID-19 outbreak, the mortality rate due to cardiovascular disease was expected to increase each year, with an estimated 23.6 million deaths projected by 2030 [6]. However, studies have since revealed that COVID-19 can cause significant long-term harms to the cardiovascular system [7, 8]. As a result, the mortality rate for patients with cardiovascular disease has increased and is projected to continue rising at a faster pace than previously predicted, both during and after the pandemic [9]. Additionally, cardiovascular disease is responsible for the greatest number of disability-adjusted life years [10] and poses a significant S. Khodaei · Z. Keshavarz-Motamed (B) McMaster University, Hamilton, Canada e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Daimi et al. (eds.), Current and Future Trends in Health and Medical Informatics, Studies in Computational Intelligence 1112, https://doi.org/10.1007/978-3-031-42112-9_2

19

20

S. Khodaei and Z. Keshavarz-Motamed

risk of falls [11] and reduced quality of life for patients [12]. Given the significant burden of this disease, researchers and clinicians are continuously seeking ways to reduce its impacts.

1.2 Aortic Valve Stenosis and Associated Cardiovascular Pathologies Aortic valve stenosis (AS) is a long-term cardiovascular illness that impacts a significant proportion of people globally, mainly resulting from the progressive calcification of the aortic valve [13]. It is characterized by a reduction in the valve’s opening area, resulting in restricted movement [14]. This movement constraint causes the heart to exert more effort, leading to symptoms like chest pain, shortness of breath, and fainting [14]. The most common cause of AS is calcific degeneration, and its incidence increases with age [14]. AS is the most prevalent among all heart valve diseases, affecting up to 7% of individuals over 65 years and up to 10% of patients over the age of 80 [13–15]. If AS is not treated, it can lead to heart failure, and approximately 50% of patients with symptoms will die within two years [16]. AS can manifest in two ways; it can be asymptomatic, which means that the patient may not experience any symptoms even with severe complications [15]. Alternatively, it can be symptomatic, with symptoms such as angina, syncope, and heart failure [15]. Patients suffering from severe AS are recommended to undergo valve replacement only when they exhibit symptoms [17]. However, before severe symptoms set in, the vascular system and the left ventricle experience continuous pressure overload and afterload due to left-ventricular outflow obstruction. This can result in other complications such as hypertrophy, impaired ejection fraction, systolic and diastolic dysfunction, impaired coronary blood-flow reserve, and mitral regurgitation [15, 17]. Indeed in most cases, AS is associated with other cardiovascular diseases, including left ventricle hypertrophy [18], diastolic dysfunction [19], coronary artery disease [20], arrhythmia [21], atrial fibrillation [22] and stroke [23]. Some of these complications can cause irreversible damage to the heart tissue, which may hinder optimal postoperative outcomes [17]. Therefore, pre-existing complications that coexist with AS can hasten heart failure after the onset of severe symptoms [15, 24]. Cardiovascular disease is indeed a general term that encompasses a vast array of illnesses related to the heart and circulatory system. It comprises several conditions that could adversely impact a person’s life, including heart failure, artery stenosis, heart valve complications, irregular heart rhythm, among others [25]. However, one of the most general and fundamentally challenging conditions is the complex valvularvascular-ventricular interactions (C3VI), which involves the interaction of multiple valvular, vascular, and ventricular pathologies [26]. In C3VI, physical phenomena associated with each pathology amplify the effects of others on the cardiovascular system. This intricate interaction could cause mechanical interference between the three systems, leading to complicated clinical outcomes that are often difficult to

Enhancing Medical Imaging with Computational Modeling for Aortic …

21

manage [26, 27]. While a wide range of pharmaceutical options exists for the treatment and management of these diseases, many require invasive or minimally invasive procedures to achieve better clinical outcomes [25]. For instance, in the United States alone, around 500,000 coronary artery bypass grafting (CABG) surgeries are performed each year to help manage coronary artery disease, which is an invasive procedure [28]. In the same way, the occurrence of AS rises from less than 1% in people under the age of 60 to around 10% in those over the age of 75 [29]. Currently, aortic valve replacement is the only known treatment for severe AS [30]. The occurrence of myocardial infarction is a significant disease affecting the ventricles, often accompanied by mitral regurgitation, and frequently present alongside AS [31]. This condition is linked with a higher risk of heart failure and cardiac mortality [31]. The use of transcatheter mitral-valve repair in patients suffering from heart failure and moderate-to-severe or severe secondary mitral regurgitation, who continued to experience symptoms despite maximal doses of guideline-directed medical therapy, led to a reduced rate of hospitalization for heart failure and lower all-cause mortality within a 24-month follow-up period compared to medical therapy alone [32]. Due to the increasing number of cardiovascular interventions being conducted, experts and researchers are exploring innovative technologies to enhance patient outcomes and decrease the economic impact.

2 Cardiovascular Imaging Techniques for Diagnosis and Intervention Planning In the field of cardiovascular surgery, a variety of imaging techniques are employed to effectively handle intricate procedures without leaving out major and crucial information. These techniques employ a variety of technologies to acquire precise images of cardiovascular pathologies. For the purposes of this book chapter, we will concentrate on non or minimally invasive bedside imaging technologies [33] that are widely used (Fig. 1). However, specialized modalities, such as 3D endoscopes, may be necessary for minimally invasive direct coronary artery bypass procedures [33]. Inter-cardiac echocardiography is an emerging technique that is particularly suitable for catheter-based procedures, enabling real-time tracking of catheter locations and early identification of complications [34]. Furthermore, nuclear cardiac imaging technologies, such as single-photon emission computed tomography (SPECT) and positron emission computed tomography (PET), provide high-resolution measurements of biological processes, allowing for the early detection of cardiovascular abnormalities [33, 35].

22

S. Khodaei and Z. Keshavarz-Motamed

Fig. 1 Different widely used imaging techniques for cardiac assessment. a Standard parasternal short-axis view of the left ventricle captured through 2D Doppler echocardiography; b 3D strain analysis using 3D trans-thoracic echocardiography images; c Computed tomography images of aortic stenosis, where non-contrast CT acquisition indicates the extent of calcification in the aortic valve; d Portrays the use of MRI b-SSFP cine sequence for functional evaluation, and the short-axis view allows quantification of ventricular volumes and assessment of global and regional cardiac function (From Kadem et al. [40])

2.1 Echocardiography Echocardiography (ECHO) is the most commonly used imaging technology worldwide to examine the heart’s structure and function [35]. This non-invasive and safe procedure is performed by capturing the reflection of sound waves produced by an ultrasound probe. However, skilled operation is required to accurately visualize the area of interest [36]. The traditional 2D Doppler echocardiography is used to evaluate heart function and structure, but this method relies on images with low spatial and temporal resolutions, which may be sensitive to noise, acquisition

Enhancing Medical Imaging with Computational Modeling for Aortic …

23

parameters, and operator variability [36]. Despite these limitations, ECHO remains the first-line imaging modality for the management of cardiovascular diseases due to its cost-effectiveness and safety. The latest advancements in transducer technology have resulted in the creation of 3D echocardiography methods, including 3D transesophageal and transthoracic echocardiography [36]. These techniques offer essential information about various aspects of structural heart interventions such as geometric measurements, hemodynamic characterization, and mechanical assessment of cardiac components in real-time. Additionally, 3D echocardiography has the potential to provide surgical guidance during procedures like mitral valve intervention and can even produce physical replicas tailored to individual patients to anticipate surgical outcomes [37]. Transthoracic echocardiography is the gold-standard non-invasive method for assessing AS patients at the early stages [38]. The severity of AS can be determined by examining hemodynamic and anatomical parameters using Doppler echocardiography [39]. Continuous wave Doppler is used to measure the velocity waveform of the AS jet during ejection across the aortic valve. With the help of the simplified Bernoulli equation, the pressure gradient across the aortic valve can be estimated. The severity of AS can be determined based on the maximum velocity and pressure gradient [38]. Ultimately, this information (the maximum velocity and pressure gradient) will aid in the medical decision-making process. Overall, transthoracic echocardiography, in conjunction with Doppler echocardiography, provides critical information to the heart team in assessing and determining the severity of AS, which can guide appropriate intervention decisions for patients.

2.2 Computed Tomography (CT) The technology of cardiac computed tomography (CT) imaging relies on the use of rotating X-ray tubes and multi-detector systems [36]. The advancements in rotation time and detector density have made it highly effective for various cardiac applications [36]. In comparison to echocardiography, it offers superior spatial resolution and blood-tissue contrast differentiation, making it great for diagnosing structural cardiac diseases [36]. However, the amount of ionizing radiation required for the procedure can vary depending on the protocols used [41]. Despite its benefits, cardiac CT imaging is relatively costly and may not be accessible in resource-limited settings [42]. Cardiac CT is commonly utilized to obtain precise geometric measurements of both vascular and valvular dimensions, as well as to identify the appropriate surgical approach. It plays a critical role in planning AS interventions, including surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR) [35, 43]. Due to its superior spatial and contrast resolution, it has become broadly used to produce accurate 3D reconstructions of valves and major arteries [35, 44, 45]. For example, in generating high-fidelity virtual simulators for cardiovascular procedures, cardiovascular computed tomography angiography (CCTA) can be used to optimize

24

S. Khodaei and Z. Keshavarz-Motamed

approaches for surgery before hospitalization [46]. The use of 3D models is very beneficial for intervention planning and understanding of morphological changes post-intervention, whether it is by using 3D modeling software or by physically printing 3D models of specified regions [45]. However, there are some limitations associated with the use of cardiac CT. The larger field of view may make it challenging to precisely segment certain cardiovascular regions (e.g., valves, atria) due to obfuscation by surrounding tissues with similar intensity [36]. Moreover, the temporal resolution of typical CT acquisitions is lower than that of other modalities, which limits the possibility of dynamical assessment of cardiac motion [36].

2.3 Magnetic Resonance Imaging (MRI) The process of magnetic resonance imaging (MRI) involves exciting protons in the body with a magnetic field and observing the response to this excitation. This process helps to differentiate various tissues depending on the strength and frequency of the magnetic field [36]. When applied to the heart, MRI can be synchronized with cardiac motion for a detailed structural and functional analysis of cardiac components [36]. However, the spatial resolution of typical cardiac MRI sequences is relatively lower than that of CT. While higher spatial resolution is achievable, it requires longer scan times and longer breath-holding periods for patients [36]. In contrast, cardiac MRI offers better temporal resolution compared to CT, but requires longer scanning times. One significant drawback of cardiac MRI is that it’s not recommended for patients with electro-metal implants such as pacemakers, defibrillators, insulin pumps, and cochlear implants, which can limit its use [36, 47]. Despite its limitations, cardiac MRI has demonstrated potential in evaluating heart chamber and valve diseases by providing a detailed analysis of the shape, structure, and function of heart chambers and valves [48]. Additionally, it can be utilized for virtual training in surgical intervention [46] and real-time surgical guidance [48]. What sets cardiac MRI apart from other imaging modalities in the context of cardiac interventions is its capability to assess intricate 3D blood flow dynamics via velocity encoding or phase-contrast flow quantification [36, 49]. These techniques have been proven to be rapid and straightforward methods for quantifying cardiac flow, with applications like quantifying chamber stroke volumes, aortic and mitral valve flows, and pressure gradients [36]. In particular for patients with AS, the primary advantage of MRI lies in its capacity to categorize patients based on their myocardial response, including fibrosis and morphological and functional changes in the heart. Furthermore, 4D flow MRI is a hopeful method for comprehending AS pathophysiology, particularly the dynamics of blood flow and their impact on the aortic wall [50].

Enhancing Medical Imaging with Computational Modeling for Aortic …

25

3 Intervention Planning and Post Operation Complications Patients with moderate AS have limited medical treatment options, including the use of statins [15]. However, for those with chronic severe AS, there is currently no medical treatment available, making aortic valve replacement the most appropriate option [15]. Patients being considered for aortic valve replacement have two options: surgical aortic valve replacement (SAVR) and transcatheter aortic valve implantation (TAVI) also known as transcatheter aortic valve replacement (TAVR). While SAVR is a risky and invasive procedure, TAVR is minimally invasive and not inferior to SAVR [51]. In fact, TAVR may be the better option for patients of all risk levels [52], including younger patients and those at low-risk [53].

3.1 Surgical and Minimally Invasive Options for Aortic Valve Intervention For years, the standard treatment for AS has been either surgical aortic valve replacement (SAVR) or aortic valve replacement (AVR). SAVR offers two prosthetic valve options, namely, mechanical and biological valves, each with its own set of pros and cons. The choice of the most suitable option for each patient should consider possible complications in the future, such as the need for anticoagulation and valvein-valve insertion if the prosthetic valve fails [54]. While SAVR has been the preferred surgical option for decades, it poses a life-threatening risk to elderly patients because it requires thoracotomy to access the heart and valve position, as well as cardiopulmonary bypass [55, 56]. Observational studies have identified various subgroups of patients, including those with advanced age or left ventricle dysfunction, who face a high risk of death following AVR. To reduce the risk of death, a less invasive approach may be a viable option for such patients [57]. For elderly patients, it is preferable to opt for non-invasive or minimally invasive interventions as they carry a lower risk of hospitalization and future complications [58]. Transcatheter Aortic Valve Replacement (TAVR) or Transcatheter Aortic Valve Implantation (TAVI) is a new technique that is suitable for patients who cannot undergo invasive open-heart surgery (SAVR). This method is considered promising as it involves minimal invasiveness compared to SAVR [55, 59]. However, the decision on the type of surgery to be performed depends on various factors, and it is the responsibility of the heart valve surgery team to make a careful assessment of patients who are candidates for TAVR or high-risk SAVR. Therefore, the outcomes of TAVR are highly dependent on the surgery team’s ability [60] to select suitable patients for the procedure [61].

26

S. Khodaei and Z. Keshavarz-Motamed

3.2 Managing Complications Post-intervention Monitoring the patient’s condition is crucial after undergoing TAVR surgery to ensure its success. The primary concern is predicting the patient’s long-term condition, as some complications may arise even though TAVR has been shown to be a good substitute for SAVR. These complications can occur at any stage of the TAVR procedure and may cause irreversible issues leading to death. At the vascular level, guiding the catheter to reach the aortic valve can result in vascular injury and perforation. At the heart level, there are several other potential complications. Misaligned positioning of the valve during deployment can cause positional shift or leakage, and if it is deployed too close to the coronary arteries, which are only about 10 mm above the aortic valve, it may block the coronary inlet [62]. When selecting a prosthesis, it is crucial to consider the size carefully. If the prosthesis is too small for the available space, it may result in positional shift or paravalvular leakage. Conversely, if the prosthesis is too large, it can cause aortic root rupture or valve deformation, leading to transvalvular leakage [62]. Paravalvular leakage is a commonly occurring complication following TAVR, and it can impact the flow and pressure in the left ventricle [63]. Additionally, thrombosis around TAVR or on the leaflets and leaflet thickening are significant complications that can occur either together with the aforementioned issues or independently [64]. TAVR is often associated with paravalvular leakage (PVL), which is the most common drawback of this procedure [65]. The prevalence of PVL is higher in TAVR compared to surgical aortic valve replacement, affecting around 26–67% of patients [59, 66]. In general, leakage in the valve can occur in different areas, such as central (transvalvular), between the prosthesis and the deployment zone (PVL), or supraskirtal. PVL can result from improper TAVR placement within the annulus or incomplete fitting of the valve inside the annulus. The impact of leakage can specifically affect the left ventricle (LV), causing changes in LV diameter, volume, and mass due to the available preload in diastole phase. Prosthesis-patient mismatch is a condition where the valve’s effective orifice area (EOA) is smaller than a normal valve [67, 68]. This condition is more common in female patients and can lead to ventricular outflow obstruction and increased LV pressure [69]. Surgeons face a difficult task of determining the optimal valve size and placement for each patient based on their unique anatomical and physiological characteristics. They must carefully weigh the risks and benefits of implantation depth, as well as potential complications such as device migration, conduction disturbance, and thrombosis, on a case-by-case basis [70]. Valve thrombosis, which can be either clinical or sub-clinical and identified by hypo-attenuating leaflet thickening (HALT), can limit valve leaflet mobility and increase the risk of early valve deterioration or embolic stroke [71–73]. The frequency of leaflet thrombosis and subclinical HALT is uncertain, but studies have reported up to 30% for leaflet thrombosis and up to 40% for HALT [74]. Despite these trends, there is still no clear understanding of the correlation between hemodynamic details and clinical outcomes [75–77]. Post-TAVR thrombosis has been attributed to factors

Enhancing Medical Imaging with Computational Modeling for Aortic …

27

such as transcatheter heart valve (THV) metallic frame material, incomplete valve expansion, prothrombotic conditions, and blood flow stagnation [78, 79]. Recent studies have revealed that blood stasis in the sinus and neo-sinus region is strongly associated with thrombus formation [80]. Obstruction to the coronary arteries can occur due to various factors such as the displacement of calcified leaflets into the coronary ostia, the lower anatomical position of the coronary ostia, and incorrect placement of the THV [81, 82]. This condition can happen immediately after TAVR or may be delayed, particularly in selfexpanding TAVR cases [83]. Symptoms of a patient with coronary artery obstruction are usually severe hypertension and ventricular arrhythmias [83]. While revascularization and stent usage may be necessary, it is crucial to evaluate a patient’s risk thoroughly before the intervention [83].

4 Computational Modeling 4.1 Cardiovascular Hemodynamic Modelling In recent years, high fidelity multiscale patient-specific cardiovascular simulations that accurately depict a patient’s cardiovascular system at multiple levels have been proven to have great potential in improving cardiovascular research, optimizing medical devices, and planning interventions [84, 85]. While medical imaging techniques have greatly improved the ability of clinicians to diagnose patients noninvasively, there are still uncertainties surrounding the understanding of a patient’s condition. Advanced imaging techniques such as 3D MRI, 4D-MRI, and CT scans provide a significant amount of information on the shape of cardiovascular organs like the LV, aortic valve, mitral valve, atrium, and aorta as well as the velocity of blood flow. However, current imaging technologies are unable to provide precise measurements of pressure in the atrium, LV, aorta, and heart valves (aortic and mitral). In addition, low spatial resolution flow measurement from medical imaging techniques, such as ECHO and 4D flow MRI, cannot provide accurate information on crucial hemodynamic parameters like wall shear stress [86]. This is because accurate quantification of blood flow near the tissue wall requires a higher spatial resolution. It is essential to note that incorrect quantification of these parameters can lead to the wrong interpretation of data [87]. Computational fluid dynamics (CFD) and advanced 3D imaging technologies have made significant advancements over the recent years, enabling the simulation of pressure and velocity fields in virtual models of a patient’s cardiovascular system in a personalized manner. By combining CFD with input parameters specific to the patient (such as anatomical geometries and flow measurement), it is possible to create a potent tool that provides accurate data with high temporal and spatial resolution for diagnosis, surgical planning, and prediction [86, 88–90].

28

S. Khodaei and Z. Keshavarz-Motamed

The use of numerical simulation to model TAVR has gained significant interest in the last 15 years. In 2009, the CFD simulation of TAVR was conducted by Dwyer et al. [91]. Their objective was to determine the prosthesis migration force. Patientspecific simulation was attempted for the first time by Sirois et al. [92] in 2011. Subsequently, many studies have been carried out to predict the valve’s final position and assess aortic wall stress during prosthesis deployment [93–95]. Furthermore, several researches have been undertaken to gain a better understanding of blood flow interactions across heart valves [96] and within the left ventricle [97–99]. However, despite these developments, there is still a lack of quantitative knowledge about the impact of pre-existing pathologies on post-TAVR recovery. To comprehend the relationship between valvular diseases, left ventricular remodeling, and TAVR, more research is necessary. Accurate quantification relies on measuring local and global blood flow dynamics. By utilizing high-quality personalized numerical simulations alongside in vivo data, a comprehensive virtual model of the TAVR procedure could become an effective diagnostic and predictive tool. With technological improvements and resources, this is an achievable goal.

4.2 Image Analysis, Geometry Reconstruction and Meshing Cardiovascular disease assessment software that is approved by regulators is typically offered as a package with medical imaging systems from vendors like GE, Siemens, Phillips, and Toshiba. However, due to the limited use cases of these tools, additional software is often required. Companies that specialize in cardiac image processing offer both specialized and general-purpose software to optimize workflows for cardiac assessment, including the evaluation of valvular and coronary disease, ventricular disease and cardiac function, and image interpretation. The majority of medical image research platforms utilize digital imaging and communications in medicine (DICOM) image formats and standards [100], as well as ITK for image processing operations [101] and VTK for visualization and 3D modeling [102] in their software architecture. Several general medical image analysis platforms, such as MeVisLab [103], MITK [104], 3D Slicer[105], ITK-SNAP [106], and RADStation3G [107] are based on ITK/VTK technology and offer volumetric visualization, annotation, segmentation, and quantification features for various imaging modalities. However, the ease of use and capability levels of these features vary. Segmentation is a process that creates labels to identify specific areas of an image. However, this method is not suitable for 3D printing or mathematical simulations that require a higher level of resolution and geometric reconstruction. Additionally, segmentation outputs often have many defects. To address this, specialized software like VMTK [108] is used for image-based modeling of vascular blood segments. This software focuses on developing a streamlined pipeline to create accurate patient-specific computational meshes of blood vessels, but it is limited in its ability to segment more complex structures like valves or ventricular chambers [109].

Enhancing Medical Imaging with Computational Modeling for Aortic …

29

TubeTK [110, 111] was primarily created to address the challenging task of identifying and producing reconstructions of vascular/tubular structures, especially in the context of vascular applications. However, it is not equipped to generate meshes that are suitable for computational analysis. To create direct surface and volumetric meshing, one can rely on general-purpose platforms such as gmsh [112], iso2mesh [113], and CGAL [114]. This can be done by treating a labeled segmentation area as a uniform mesh comprising voxel elements, which can enable quicker production of patient-specific computational domains for simulations. However, this method requires precise segmentations of the regions of interest to be available in advance.

4.3 Personalized Computational Modelling The sophistication of numerical simulation techniques for cardiovascular and valvular disease has increased over time. These simulations are usually based on MRI or CT data, which provide detailed anatomy including ventricles, valves, multiple vessel bifurcations and outlets. Boundary conditions for simulations are often based on measurements of flow from 2D or 4D phase-contrast MRI [115]. Outflow boundary conditions are particularly challenging but advances in modeling and numerical stability have made simulations more physiologically realistic [115]. Simulations that solve fluid–structure interactions (FSI) problems can provide information about wall deformations, stresses, and strains. Due to the cardiovascular system’s complexity and the high computational cost of modeling hemodynamics in the entire circulatory system, CFD/FSI models are limited to a specific region of the cardiovascular system. To account for downstream and upstream, boundary conditions are applied, and the accuracy of computational models is highly dependent on the accuracy of the boundary condition. With the advancement of precision medicine for valvular and ventricular diseases, personalized boundary conditions for computational models of TAVR are essential for translating numerical models into clinical practice [26, 116, 117]. Unfortunately, accurate hemodynamic clinical data in all domains is often unavailable, and several current studies use non-patient specific boundary conditions for their computational simulations [99, 118–120]. Currently, only lumped parameter models (LPM) have the capability to calculate the hemodynamics and resolve the complexities of supplying precise boundary conditions for computational models [121, 122]. LPM models can generate personalized boundary conditions for all parts of the cardiovascular system, including the aorta [123–126], ventricle [127, 128], valves [27, 129, 130], and coronary arteries [131, 132]. However, these LPM models need to be customized for each patient and based on individual clinical data [121]. Additionally, they should rely on non-invasive input parameters such as routine echocardiography or sphygmomanometer [26]. With the combination of accurate boundary conditions and the exponential growth of computational capabilities, the future of personalized cardiovascular simulation, including TAVR, looks very promising.

30

S. Khodaei and Z. Keshavarz-Motamed

Keshavarz-Motamed [26, 27] has recently created a patient-specific framework using Doppler echocardiography to quantify global hemodynamics and cardiac function for patients experiencing complex valvular, vascular, and ventricular diseases (Fig. 2). This tool can predict the impact of each disease on the heart workload and determine which valvular surgery (or sequence of surgeries) would minimize the heart workload in cases where multiple valvular diseases are present (Fig. 3). The developed framework was validated against cardiac catheterization data with a considerable inter- and intra-patient variability with a broad range of diseases in a population of forty-nine patients (with AS) [26]. Moreover, some of the sub-models of the patient-specific lumped parameter algorithm have been used previously [26, 127, 132, 133], with validation against in vivo cardiac catheterization (N = 34) [123, 134] in patients with vascular diseases, in vivo MRI data (N = 57) [135] in patients with AS, and in vivo MRI data (N = 23) [124–126] in patients with mixed valvular diseases and coarctation. Similarly, Ben-Assa et al. [133] utilized an echocardiography-based model to calculate left ventricular stroke work and vascular impedance spectrum for the patients undergoing TAVR (Fig. 4). In terms of multiscale computational modelling, Khodaei et al. [127, 128, 132, 136, 137] created patient-specific computational models using Doppler-based lumped parameter models and fluid–structure interaction to address complex valvular, ventricular and mini-vascular diseases. These frameworks were applied to patients receiving TAVR to calculate various metrics such as heart workload, pressure gradients, and blood flow dynamics like vortex formation, growth, and shedding, as well as ventricular, valvular, and vascular (coronary arteries) time average wall shear stress (Figs. 5, 6, and 7). Non-invasive computational modeling is the sole means of obtaining these essential metrics, which hold great significance in effectively managing AS and conducting thorough preoperative investigations. In the future, computational hemodynamic tools may become more helpful for clinicians to answer important questions related to medical procedures. These tools can provide information about how a procedure will affect the heart’s mechanics and function, when is the ideal time to perform the intervention, and how to identify which patients may have a more favorable outcome. However, extensive validation and further development are necessary before these tools can be fully utilized in the clinical settings.

4.4 Artificial Intelligence and Machine Learning Application in Cardiovascular Pathophysiology The use of artificial intelligence (AI) and machine learning (ML) has resulted in significant advancements in various fields, including the cardiovascular field. ML has played a crucial role in addressing significant challenges in the cardiovascular field, ultimately leading to better health outcomes [138]. It has the ability to detect abnormalities in imaging, calcification scoring [139], predict the risk of mortality

Enhancing Medical Imaging with Computational Modeling for Aortic …

31

a. Anatomical Diagram of the lumped Parameter model

b. Electrical Diagram of the lumped Parameter model Fig. 2 Schematic diagram of a diagnostic and predictive lumped parameter modelling framework to quantify local and global hemodynamics. a Anatomical representation; b Electrical representation) in patients with complex valvular, vascular, and ventricular diseases (from Keshavarz-Motamed [26])

32

S. Khodaei and Z. Keshavarz-Motamed

a.MVD Sample #1

c.MVD Sample #2

b.Non- MVD Sample #1

d.MVD Sample #2

Fig. 3 Examples of LV workloads in sample patients with mixed valvular disease (MVD) between baseline and 90 days post-TAVR (from Keshavarz-Motamed et al. [27])

[140], intra-operative online prediction, monitor patients’ post-intervention longitudinally [141], provide feedback, expedite rehabilitation [142], and assist with systemic reviews or meta-analyses [143]. In addition, the field of medical diagnosis has made significant strides due to the use of deep learning technology, which has demonstrated exceptional outcomes in improving the accuracy of diagnoses. This progress has also led to innovative developments in analyzing medical imaging data, advancing at a rate previously unachievable [144–146]. The integration of ML in the cardiovascular field is aimed at reducing errors in clinical translation, and it has proven to be an effective tool for clinicians and patients’ families in making preoperative decisions. Thanks to advancements in computation, digitization of data, and modern imaging techniques, it is now possible to visualize vascular anatomy with incredible precision. By combining vascular imaging and computational methods, ML offers a powerful tool for detecting vascular anomalies at an earlier stage, leading to the development of diagnostic and predictive tools tailored to the needs of individual patients. As computational power and digitized data continue to grow, ML is expected to become increasingly important in both research and clinical settings. Traditional statistical approaches may not be sufficient for handling the large and complex data sets involved in this field.

Enhancing Medical Imaging with Computational Modeling for Aortic …

33

Fig. 4 Changes in SWLV and valvular pressure gradients in a cohort of 70 patients who received TAVR (from Ben-assa et al. [133])

The field of cardiology is witnessing remarkable advancements in AI that are paving the way for the next phase of cardiovascular simulation tools. The integration of these advanced techniques has the potential to make LPM and multiscale frameworks even more personalized, reduce computation time, and provide clinicians and researchers with valuable data and insights that were previously inaccessible [147– 149]. As computational capabilities continue to improve and digital cardiology data grows exponentially, simulation tools are increasingly being integrated into clinical workflows. LPM is playing a crucial role in personalizing both standalone 0D models and multiscale models, which are used to examine global and local hemodynamics and the impacts of cardiovascular diseases. These tools are being used to predict and manage diseases, plan interventions, quantify outcomes, and study disease mechanisms from new perspectives.

5 Challenges and Limitations of Computational Modeling 5.1 Accuracy and Validation Challenges Validating computational modeling results with clinical data is a significant challenge in cardiovascular modeling, as obtaining accurate and specific clinical data that matches the computational modelling is often difficult due to difficult protocols

34

S. Khodaei and Z. Keshavarz-Motamed

Fig. 5 Changes in local and global hemodynamics in a sample patient between baseline and 90-day post-TAVR. a Global hemodynamics: LV workload, aorta and LV pressures; b Local hemodynamics: vortical structure; c Local hemodynamics: time-averaged wall shear stress; d Clinical assessment of hemodynamics (from Khodaei et al. [127])

Enhancing Medical Imaging with Computational Modeling for Aortic …

35

Fig. 6 3D distribution contours of coronary wall shear stress at peak diastole in 2 sample patients between baseline and 90-day post-TAVR (from Khodaei et al. [132])

and practical limitations [150]. This might result in some inconsistencies between the clinical data and the assumptions made in the computational model, which can lead to differences between the numerical predictions and the actual clinical outcomes [151]. To overcome these challenges, it is essential to conduct sensitivity analysis that examines the dependency of the computational solution and its corresponding clinical findings on the data and numerically chosen parameters used [151, 152].

36

S. Khodaei and Z. Keshavarz-Motamed

Fig. 7 Local hemodynamics at baseline and post-TAVR in a sample patient: a Blood flow vortical structure in the sinus and neo-sinus in the central plane of each leaflet during diastole in preand post-TAVR states; b Blood stasis volume per leaflet neo-sinus at peak diastole and CT-based evidence of HALT on the leaflets (from Khodaei et al. [136])

This analysis can help identify the factors that have the most significant impact on the computational results and prioritize the validation of these factors using clinical data. In addition, sensitivity analysis can offer useful information about the unknown factors related to the model parameters and numerical solver, which can help us enhance the accuracy of the model predictions [151, 152]. Validating the computational modeling outcomes with clinical data is a difficult yet crucial process to guarantee the dependability of cardiovascular models. By integrating sensitivity analysis with thorough validation and optimization of the model parameters, we can create dependable and precise models that furnish significant knowledge about the hemodynamics and support individualized clinical decision-making.

Enhancing Medical Imaging with Computational Modeling for Aortic …

37

5.2 Limitations of Current Computational Models Despite recent progress, there are still several significant limitations that impede the practical use of cardiovascular computational hemodynamics simulations. To perform a reliable personalized CFD simulation, it’s necessary to have an accurate 3D reconstruction of the valves, ventricles, aorta, and coronary artery lumen, which can be affected by inaccuracies in tracing, inadequate spatial resolution, or blooming artifacts [152–154]. Additionally, the definition of boundary conditions is challenging and has a high degree of uncertainty, as accurate measurements of cardiovascular flow are rarely performed in clinical settings [155, 156]. Computational time requirements for CFD simulations vary depending on model complexity, spatiotemporal discretization, and computer specifications [152]. Lastly, to justify their routine clinical use in catheterization laboratories, more robust clinical evidence is needed to support CFD results [157, 158].

6 Future Perspectives and Emerging Technologies The ongoing incorporation of modern advancements in translational hemodynamic modeling, imaging, AI and machine learning will further boost our comprehension of cardiovascular and valvular irregularities, enhance our capacity to devise individualized diagnostic and prognostic tools for evaluating cardiovascular intervention outcomes, aid in the selection of treatment alternatives, expedite modeling, enhance data accuracy, and optimize each technique to facilitate the seamless application of technology in high-volume clinical environments [40, 159]. For computational methods to be more widely adopted in clinical practice, it is necessary to conduct larger and more comprehensive studies involving multiple sites and modes of patient care. These studies should evaluate the potential benefits, accuracy, fairness, generalizability, explain ability, reliability, and safety of using computational tools in relation to cardiac and circulatory procedures [160]. To ensure the validity of these models, thorough validation against established benchmarks (such as catheterization for modeling or precise annotations for machine learning) is also essential. As computing power and digital data continue to expand, these approaches are likely to gain even more traction in improving patient outcomes and reducing healthcare costs associated with cardiovascular disease.

38

S. Khodaei and Z. Keshavarz-Motamed

References 1. Cardiovascular diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/cardio vascular-diseases-(cvds). Accessed 20 Mar 2023 2. S.S. Virani et al., Heart disease and stroke statistics—2020 update: a report from the American heart association. Circulation 141(9), e139–e596 (2020). https://doi.org/10.1161/CIR.000000 0000000757 3. P. H. A. of Canada, Tracking Heart Disease and Stroke in Canada 2009 (2009), https:// www.canada.ca/en/public-health/services/reports-publications/2009-tracking-heart-diseasestroke-canada.html. Accessed 20 Mar 2023 4. Cardiovascular Disease Burden Report, www.heart.org. https://www.heart.org/en/get-inv olved/advocate/federal-priorities/cardiovascular-disease-burden-report. Accessed 20 Mar 2023 5. CVD Statistics 2017, https://ehnheart.org/cvd-statistics/cvd-statistics-2017.html. Accessed 20 Mar 2023 6. D. Mozaffarian et al., Heart disease and stroke statistics—2015 update. Circulation 131(4), e29–e322 (2015). https://doi.org/10.1161/CIR.0000000000000152 7. K.J. Clerkin et al., COVID-19 and cardiovascular disease. Circulation 141(20), 1648–1655 (2020). https://doi.org/10.1161/CIRCULATIONAHA.120.046941 8. Y. Xie, E. Xu, B. Bowe, Z. Al-Aly, Long-term cardiovascular outcomes of COVID-19. Nat. Med. 28(3), Art. no. 3 (2022). https://doi.org/10.1038/s41591-022-01689-3 9. A. Banerjee et al., Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic. Eur. J. Prev. Cardiol. 28(14), 1599–1609 (2021). https://doi.org/10.1093/eurjpc/ zwaa155 10. P. Joseph et al., Reducing the global burden of cardiovascular disease, Part 1. Circ. Res. 121(6), 677–694 (2017). https://doi.org/10.1161/CIRCRESAHA.117.308903 11. J. Bhangu et al., Long-term cardiac monitoring in older adults with unexplained falls and syncope. Heart 102(9), 681–686 (2016). https://doi.org/10.1136/heartjnl-2015-308706 12. M. Saqlain, A. Riaz, A. Ahmed, S. Kamran, A. Bilal, H. Ali, Predictors of health-related quality-of-life status among elderly patients with cardiovascular diseases. Value Health Reg. Issues 24, 130–140 (2021). https://doi.org/10.1016/j.vhri.2020.11.003 13. G.N. Levine, Cardiology Secrets (Elsevier Health Sciences, 2021) 14. S.G. Rouleau, W.J. Brady, A. Koyfman, B. Long, Transcatheter aortic valve replacement complications: a narrative review for emergency clinicians. Am. J. Emerg. Med. 56, 77–86 (2022). https://doi.org/10.1016/j.ajem.2022.03.042 15. B.A. Carabello, W.J. Paulus, Aortic stenosis. Lancet 373(9667), 956–966 (2009). https://doi. org/10.1016/S0140-6736(09)60211-7 16. M.P. Jalava et al., Transcatheter and surgical aortic valve replacement in patients with recent acute heart failure. Ann. Thorac. Surg. 109(1), 110–117 (2020). https://doi.org/10.1016/j.ath oracsur.2019.05.044 17. R. Rosenhek et al., Predictors of outcome in severe, asymptomatic aortic stenosis. N. Engl. J. Med. 343(9), 611–617 (2000). https://doi.org/10.1056/NEJM200008313430903 18. A.S.V. Shah et al., Left ventricular hypertrophy with strain and aortic stenosis. Circulation 130(18), 1607–1616 (2014). https://doi.org/10.1161/CIRCULATIONAHA.114.011085 19. A.L. Klein, J. Ramchand, S.F. Nagueh, Aortic stenosis and diastolic dysfunction. J. Am. Coll. Cardiol. 76(25), 2952–2955 (2020). https://doi.org/10.1016/j.jacc.2020.10.034 20. A. El Sabbagh, R.A. Nishimura, Clinical conundrum of coronary artery disease and aortic valve stenosis. J. Am. Heart Assoc. 6(2), e005593. https://doi.org/10.1161/JAHA.117.005593 21. M. Urena et al., Arrhythmia burden in elderly patients with severe aortic stenosis as determined by continuous electrocardiographic recording. Circulation 131(5), 469–477 (2015). https:// doi.org/10.1161/CIRCULATIONAHA.114.011929 22. S. Stortecky et al., Atrial fibrillation and aortic stenosis. Circ. Cardiovasc. Interv. 6(1), 77–84 (2013). https://doi.org/10.1161/CIRCINTERVENTIONS.112.000124

Enhancing Medical Imaging with Computational Modeling for Aortic …

39

23. C. Andreasen et al., Incidence of ischemic stroke in individuals with and without aortic valve stenosis. Stroke 51(5), 1364–1371 (2020). https://doi.org/10.1161/STROKEAHA.119. 028389 24. J. Ross, E. Braunwald, Aortic stenosis. Circulation 38(1s5), V–61 (1968). https://doi.org/10. 1161/01.CIR.38.1S5.V-61 25. D.P. Zipes, Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine, 11th edn. BMH Med. J. ISSN 2348–392X 5(2), Art. no. 2 (2018) 26. Z. Keshavarz-Motamed, A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases. Sci. Rep. 10(1), Art. no. 1 (2020). https:// doi.org/10.1038/s41598-020-63728-8 27. K.-M. Zahra et al., Mixed valvular disease following transcatheter aortic valve replacement: quantification and systematic differentiation using clinical measurements and image-based patient-specific in silico modeling. J. Am. Heart Assoc. 9(5), e015063 (2020). https://doi.org/ 10.1161/JAHA.119.015063 28. S. Sankaran, M. Esmaily Moghadam, A.M. Kahn, E.E. Tseng, J.M. Guccione, A.L. Marsden, Patient-specific multiscale modeling of blood flow for coronary artery bypass graft surgery. Ann. Biomed. Eng. 40(10), 2228–2242 (2012). https://doi.org/10.1007/s10439-012-0579-3 29. M.-A. Clavel, P. Pibarot, A decade of revolutions in calcific aortic stenosis. Cardiol. Clin. 38(1), xiii–xiv (2020). https://doi.org/10.1016/j.ccl.2019.10.001 30. G.C.M. Siontis et al., Transcatheter aortic valve implantation vs. surgical aortic valve replacement for treatment of symptomatic severe aortic stenosis: an updated meta-analysis. Eur. Heart J. 40(38), 3143–3153 (2019). https://doi.org/10.1093/eurheartj/ehz275 31. L.A. Piérard, B.A. Carabello, Ischaemic mitral regurgitation: pathophysiology, outcomes and the conundrum of treatment. Eur. Heart J. 31(24), 2996–3005 (2010). https://doi.org/10.1093/ eurheartj/ehq411 32. G.W. Stone et al., Transcatheter mitral-valve repair in patients with heart failure. N. Engl. J. Med. 379(24), 2307–2318 (2018). https://doi.org/10.1056/NEJMoa1806640 33. L. Athanasiou, F.R. Nezami, E.R. Edelman, Computational cardiology. IEEE J. Biomed. Health Inform. 23(1), 4–11 (2019). https://doi.org/10.1109/JBHI.2018.2877044 34. A. Enriquez et al., Use of intracardiac echocardiography in interventional cardiology. Circulation 137(21), 2278–2294 (2018). https://doi.org/10.1161/CIRCULATIONAHA.117. 031343 35. F.E. Corrigan et al., Imaging for predicting, detecting, and managing complications after transcatheter aortic valve replacement. JACC Cardiovasc. Imaging 12(5), 904–920 (2019). https:// doi.org/10.1016/j.jcmg.2018.07.036 36. J.L. Zamorano et al. (eds.), The ESC Textbook of Cardiovascular Imaging, 3rd edn, New to this Edition, in The European Society of Cardiology Series (Oxford University Press, Oxford, New York, 2021) 37. L. Labrousse et al., Guidance of the MitraClip® procedure by 2D and 3D imaging. Arch. Cardiovasc. Dis. 111(6), 432–440 (2018). https://doi.org/10.1016/j.acvd.2018.05.003 38. N. Saikrishnan, G. Kumar, F.J. Sawaya, S. Lerakis, A.P. Yoganathan, Accurate assessment of aortic stenosis. Circulation 129(2), 244–253 (2014). https://doi.org/10.1161/CIRCULATI ONAHA.113.002310 39. H. Baumgartner et al., Recommendations on the echocardiographic assessment of aortic valve stenosis: a focused update from the European Association of Cardiovascular Imaging and the American Society of Echocardiography. Eur. Heart J. Cardiovasc. Imaging 18(3), 254–275 (2017). https://doi.org/10.1093/ehjci/jew335 40. M. Kadem, L. Garber, M. Abdelkhalek, B.K. Al-Khazraji, Z. Keshavarz-Motamed, Hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. IEEE Rev. Biomed. Eng. 16, 403–423 (2023). https://doi.org/10.1109/ RBME.2022.3142058 41. A. Ceballos, R. Prather, E. Divo, A.J. Kassab, W.M. DeCampli, Patient-specific multi-scale model analysis of hemodynamics following the hybrid Norwood procedure for hypoplastic left heart syndrome: effects of reverse Blalock-Taussig shunt diameter. Cardiovasc. Eng. Technol. 10(1), 136–154 (2019). https://doi.org/10.1007/s13239-018-00396-w

40

S. Khodaei and Z. Keshavarz-Motamed

42. N. Shah, Access to imaging technology in the developing world, in Radiology in Global Health: Strategies, Implementation, and Applications, ed. by D.J. Mollura, M.P. Lungren (Springer, New York, NY, 2014), pp. 13–17. https://doi.org/10.1007/978-1-4614-0604-4_3 43. M. Abdelkhalek et al., Patterns and structure of calcification in aortic stenosis. JACC Cardiovasc. Imaging. https://doi.org/10.1016/j.jcmg.2023.02.011 44. M. Gatti et al., Accuracy of cardiac magnetic resonance generated 3D models of the aortic annulus compared to cardiovascular computed tomography generated 3D models. Int. J. Cardiovasc. Imaging 36(10), 2007–2015 (2020). https://doi.org/10.1007/s10554-020-019 02-9 45. S.A. Chen, C.S. Ong, N. Malguria, L.A. Vricella, J.R. Garcia, N. Hibino, Digital design and 3D printing of aortic arch reconstruction in HLHS for surgical simulation and training. World J. Pediatr. Congenit. Heart Surg. 9(4), 454–458 (2018). https://doi.org/10.1177/215013511 8771323 46. J.S. Shinbane, L.A. Saxon, Virtual medicine: utilization of the advanced cardiac imaging patient avatar for procedural planning and facilitation. J. Cardiovasc. Comput. Tomogr. 12(1), 16–27 (2018). https://doi.org/10.1016/j.jcct.2017.11.004 47. F. Calamante, B. Ittermann, E. Kanal, T.I.-S.W.G. on M. Safety, and D. Norris, Recommended responsibilities for management of MR safety. J. Magn. Reson. Imaging 44(5), 1067–1069 (2016). https://doi.org/10.1002/jmri.25282 48. A.E. Campbell-Washburn et al., Real-time MRI guidance of cardiac interventions. J. Magn. Reson. Imaging 46(4), 935–950 (2017). https://doi.org/10.1002/jmri.25749 49. G. Soulat, P. McCarthy, M. Markl, 4D Flow with MRI. Annu. Rev. Biomed. Eng. 22(1), 103–126 (2020). https://doi.org/10.1146/annurev-bioeng-100219-110055 50. Y. Bohbot et al., Usefulness of cardiac magnetic resonance imaging in aortic stenosis. Circ. Cardiovasc. Imaging 13(5), e010356 (2020). https://doi.org/10.1161/CIRCIMAGING.119. 010356 51. C.M. Barker, Clinical Practice Update: Who Should Be Referred for Transcatheter Aortic Valve Replacement in 2017? (2017) [Online]. https://www.uscjournal.com/articles/clinicalpractice-update-who-should-be-referred-transcatheter-aortic-valve-replacement. Accessed 25 May 2022 52. R. Waksman et al., Transcatheter aortic valve replacement in low-risk patients with symptomatic severe aortic stenosis. J. Am. Coll. Cardiol. 72(18), 2095–2105 (2018). https://doi. org/10.1016/j.jacc.2018.08.1033 53. M. Coylewright, J.K. Forrest, J.M. McCabe, T.M. Nazif, TAVR in low-risk patients. J. Am. Coll. Cardiol. 75(10), 1208–1211 (2020). https://doi.org/10.1016/j.jacc.2019.12.057 54. R.M. Suri, H.V. Schaff, Selection of aortic valve prostheses: contemporary reappraisal of mechanical versus biologic valve substitutes. Circulation 128(12), 1372–1380 (2013). https:// doi.org/10.1161/CIRCULATIONAHA.113.001681 55. P. Vy et al., Review of patient-specific simulations of transcatheter aortic valve implantation. Int. J. Adv. Eng. Sci. Appl. Math. 8(1), 2–24 (2016) 56. A. Cheung, R. Ree, Transcatheter aortic valve replacement. Anesthesiol. Clin. 26(3), 465–479 (2008). https://doi.org/10.1016/j.anclin.2008.05.002 57. M.B. Leon et al., Transcatheter or surgical aortic-valve replacement in intermediate-risk patients. N. Engl. J. Med. 374(17), 1609–1620 (2016). https://doi.org/10.1056/NEJMoa151 4616 58. A.A. Kolkailah, R. Doukky, M.P. Pelletier, A.S. Volgman, T. Kaneko, A.F. Nabhan, Transcatheter aortic valve implantation versus surgical aortic valve replacement for severe aortic stenosis in people with low surgical risk. Cochrane Database Syst. Rev. (12), CD013319 (2019). https://doi.org/10.1002/14651858.CD013319.pub2 59. M.B. Leon et al., Transcatheter or surgical aortic-valve replacement in intermediate-risk patients. N. Engl. J. Med. 374(17), 1609–1620 (2016) 60. S. Arora, J.A. Misenheimer, R. Ramaraj, Transcatheter aortic valve replacement: comprehensive review and present status. Tex. Heart Inst. J. 44(1), 29–38 (2017). https://doi.org/10. 14503/THIJ-16-5852

Enhancing Medical Imaging with Computational Modeling for Aortic …

41

61. M. Chiarito et al., Evolving devices and material in transcatheter aortic valve replacement: what to use and for whom. J. Clin. Med. 11(15), Art. no. 15 (2022). https://doi.org/10.3390/ jcm11154445 62. J. Shannon et al., Recognition and management of complications during transcatheter aortic valve implantation. Expert Rev. Cardiovasc. Ther. 9(7), 913–926 (2011). https://doi.org/10. 1586/erc.11.84 63. B.E. Stähli, W. Maier, R. Corti, T.F. Lüscher, R. Jenni, F.C. Tanner, Aortic regurgitation after transcatheter aortic valve implantation: mechanisms and implications. Cardiovasc. Diagnos. Ther. 3(1), 15–22 (2013) 64. S.V. Pislaru, V.T. Nkomo, G.S. Sandhu, Assessment of prosthetic valve function after TAVR. JACC Cardiovasc. Imaging 9(2), 193–206 (2016). https://doi.org/10.1016/j.jcmg.2015.11.010 65. M. Gotzmann, M. Lindstaedt, A. Mügge, From pressure overload to volume overload: aortic regurgitation after transcatheter aortic valve implantation. Am. Heart J. 163(6), 903–911 (2012). https://doi.org/10.1016/j.ahj.2012.03.017 66. G. Manoharan et al., Treatment of symptomatic severe aortic stenosis with a novel resheathable supra-annular self-expanding transcatheter aortic valve system. JACC Cardiovasc. Interv. 8(10), 1359–1367 (2015). https://doi.org/10.1016/j.jcin.2015.05.015 67. S.H. Rahimtoola, The problem of valve prosthesis-patient mismatch. Circulation 58(1), 20–24 (1978). https://doi.org/10.1161/01.CIR.58.1.20 68. S.A. Daneshvar, S.H. Rahimtoola, Valve prosthesis-patient mismatch (VP–PM): a long-term perspective. J. Am. Coll. Cardiol. 60(13), 1123–1135 (2012). https://doi.org/10.1016/j.jacc. 2012.05.035 69. L.M. Astudillo et al., Clinical predictors of prosthesis-patient mismatch after aortic valve replacement for aortic stenosis. Clinics (Sao Paulo) 67(1), 55–60 (2012). https://doi.org/10. 6061/clinics/2012(01)09 70. H. Hatoum, Fluid mechanics of transcatheter aortic valve replacement. The Ohio State University (2018). [Online]. https://etd.ohiolink.edu/apexprod/rws_olink/r/1501/10?clear= 10&p10_accession_num=osu1541781379381912. Accessed 25 May 2022 71. O. Abdul-Jawad Altisent et al., Neurological damage after transcatheter aortic valve implantation compared with surgical aortic valve replacement in intermediate risk patients. Clin. Res. Cardiol. 105(6), 508–517 (2016). https://doi.org/10.1007/s00392-015-0946-9 72. G. Athappan et al., Influence of transcatheter aortic valve replacement strategy and valve design on stroke after transcatheter aortic valve replacement: a meta-analysis and systematic review of literature. J. Am. Coll. Cardiol. 63(20), 2101–2110 (2014). https://doi.org/10.1016/ j.jacc.2014.02.540 73. S. Arora, C.J. Ramm, J.A. Misenheimer, J.P. Vavalle, Early transcatheter valve prosthesis degeneration and future ramifications. Cardiovasc. Diagnos. Ther. 7(1), Art. no. 1 (2017) 74. J. Karády et al., Quantification of hypo-attenuated leaflet thickening after transcatheter aortic valve implantation: clinical relevance of hypo-attenuated leaflet thickening volume. Eur. Heart J. Cardiovasc. Imaging 21(12), 1395–1404 (2020). https://doi.org/10.1093/ehjci/jeaa184 75. S. Garcia et al., Clinical impact of hypoattenuating leaflet thickening after transcatheter aortic valve replacement. Circ. Cardiovasc. Interv. 121, 011480. https://doi.org/10.1161/CIRCIN TERVENTIONS.121.011480 76. R.R. Makkar et al., Subclinical leaflet thrombosis in transcatheter and surgical bioprosthetic valves: PARTNER 3 cardiac computed tomography substudy. J. Am. Coll. Cardiol. 75(24), 3003–3015 (2020). https://doi.org/10.1016/j.jacc.2020.04.043 77. P. Blanke et al., Bioprosthetic aortic valve leaflet thickening in the evolut low risk sub-study. J. Am. Coll. Cardiol. 75(19), 2430–2442 (2020). https://doi.org/10.1016/j.jacc.2020.03.022 78. S. Nakatani, Subclinical leaflet thrombosis after transcatheter aortic valve implantation. Heart 103(24), 1942–1946 (2017). https://doi.org/10.1136/heartjnl-2017-311818 79. D. Mylotte et al., Transcatheter heart valve failure: a systematic review. Eur. Heart J. 36(21), 1306–1327 (2015). https://doi.org/10.1093/eurheartj/ehu388 80. H. Hatoum et al., Predictive model for thrombus formation after transcatheter valve replacement. Cardiovasc. Eng. Technol. 12(6), 576–588 (2021). https://doi.org/10.1007/s13239-02100596-x

42

S. Khodaei and Z. Keshavarz-Motamed

81. R.J. Lederman et al., Preventing coronary obstruction during transcatheter aortic valve replacement. JACC Cardiovasc. Interv. 12(13), 1197–1216 (2019). https://doi.org/10.1016/j.jcin. 2019.04.052 82. J. M. Khan et al., Preventing coronary obstruction during transcatheter aortic valve replacement. JACC Cardiovasc. Interv. 14(9), 941–948 (2021). https://doi.org/10.1016/j.jcin.2021. 02.035 83. R. Valvo, G. Costa, M. Barbanti, How to avoid coronary occlusion during TAVR valve-invalve procedures. Front. Cardiovasc. Med. 6, 168 (2019). https://doi.org/10.3389/fcvm.2019. 00168 84. C.A. Taylor, T.A. Fonte, J.K. Min, Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J. Am. Coll. Cardiol. 61(22), 2233–2241 (2013). https://doi.org/10.1016/j.jacc.2012.11.083 85. A. Randles, D.H. Frakes, J.A. Leopold, Computational fluid dynamics and additive manufacturing to diagnose and treat cardiovascular disease. Trends Biotechnol. 35(11), 1049–1061 (2017). https://doi.org/10.1016/j.tibtech.2017.08.008 86. C.A. Taylor, M.T. Draney, Experimental and computational methods in cardiovascular fluid mechanics. Annu. Rev. Fluid Mech. 36(1), 197–231 (2004). https://doi.org/10.1146/annurev. fluid.36.050802.121944 87. E. Soudah et al., Estimation of wall shear stress using 4d flow cardiovascular mri and computational fluid dynamics. J. Mech. Med. Biol. 17(03), 1750046 (2017). https://doi.org/10.1142/ S0219519417500464 88. -Legazpi Pablo Martinez et al., Stasis mapping using ultrasound. JACC Cardiovasc. Imaging 11(3), 514–515 (2018). https://doi.org/10.1016/j.jcmg.2017.06.012 89. A. Postigo et al., Assessment of blood flow transport in the left ventricle using ultrasound. validation against 4-D flow cardiac magnetic resonance. Ultrasound Med. Biol. 48(9), 1822– 1832 (2022). https://doi.org/10.1016/j.ultrasmedbio.2022.05.007 90. V. Vu et al., Mitral valve prosthesis design affects hemodynamic stasis and shear in the dilated left ventricle. Ann. Biomed. Eng. 47(5), 1265–1280 (2019). https://doi.org/10.1007/s10439019-02218-z 91. H.A. Dwyer et al., Computational fluid dynamics simulation of transcatheter aortic valve degeneration. Interact. CardioVasc. Thorac. Surg. 9(2), 301–308 (2009). https://doi.org/10. 1510/icvts.2008.200006 92. E. Sirois, Q. Wang, W. Sun, Fluid simulation of a transcatheter aortic valve deployment into a patient-specific aortic root. Cardiovasc. Eng. Technol. 2(3), 186–195 (2011). https://doi.org/ 10.1007/s13239-011-0037-7 93. W. (proxy) Sun, E. Sirois, W. Mao, K. Li, J. Calderan, Simulated transcatheter aortic valve flow: implications of elliptical deployment and under-expansion at the aortic annulus. Artif. Organs 42(7), E141–E152 (2018). https://doi.org/10.1111/aor.13107 94. Q. Wang, E. Sirois, W. Sun, Patient-specific modeling of biomechanical interaction in transcatheter aortic valve deployment. J. Biomech. 45(11), 1965–1971 (2012). https://doi.org/10. 1016/j.jbiomech.2012.05.008 95. S. Morganti et al., Simulation of transcatheter aortic valve implantation through patientspecific finite element analysis: two clinical cases. J. Biomech. 47(11), 2547–2555 (2014). https://doi.org/10.1016/j.jbiomech.2014.06.007 96. F. Sotiropoulos, T.B. Le, A. Gilmanov, Fluid mechanics of heart valves and their replacements. Annu. Rev. Fluid Mech. 48(1), 259–283 (2016). https://doi.org/10.1146/annurev-fluid-122 414-034314 97. T.B. Le, F. Sotiropoulos, D. Coffey, D. Keefe, Vortex formation and instability in the left ventricle. Phys. Fluids 24(9), 091110 (2012). https://doi.org/10.1063/1.4747164 98. T.B. Le, M.S.M. Elbaz, R.J. Van Der Geest, F. Sotiropoulos, High resolution simulation of diastolic left ventricular hemodynamics guided by four-dimensional flow magnetic resonance imaging data. Flow Turb. Combust. 102(1), 3–26 (2019). https://doi.org/10.1007/s10494-0180003-7

Enhancing Medical Imaging with Computational Modeling for Aortic …

43

99. W. Mao, A. Caballero, R. McKay, C. Primiano, W. Sun, Fully-coupled fluid-structure interaction simulation of the aortic and mitral valves in a realistic 3D left ventricle model. PLoS One 12(9) (2017). https://doi.org/10.1371/journal.pone.0184729 100. M. Onken, M. Eichelberg, J. Riesmeier, P. Jensch, Digital imaging and communications in medicine, in Biomedical Image Processing, ed. by T.M. Deserno, Biological and Medical Physics, Biomedical Engineering (Springer, Berlin, Heidelberg, 2011), pp. 427–454. https:// doi.org/10.1007/978-3-642-15816-2_17 101. T.S. Yoo et al., Engineering and algorithm design for an image processing API: a technical report on ITK—the insight toolkit. Med. Meets Virtual Real. 02(10), 586–592 (2002). https:// doi.org/10.3233/978-1-60750-929-5-586 102. The Visualization Toolkit, 2nd edn. Guide books. https://dl.acm.org/doi/abs/10.5555/272980. Accessed 28 Mar 2023 103. F. Ritter et al., Medical image analysis. IEEE Pulse 2(6), 60–70 (2011). https://doi.org/10. 1109/MPUL.2011.942929 104. M. Nolden et al., The medical imaging interaction toolkit: challenges and advances. Int. J. CARS 8(4), 607–620 (2013). https://doi.org/10.1007/s11548-013-0840-8 105. A. Fedorov et al., 3D Slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323–1341 (2012). https://doi.org/10.1016/j.mri. 2012.05.001 106. M.T. Ngo, U.Y. Lee, H. Ha, J. Jung, D.H. Lee, H.S. Kwak, Improving blood flow visualization of recirculation regions at carotid bulb in 4D flow MRI using semi-automatic segmentation with ITK-SNAP. Diagnostics 11(10), Art. no. 10 (2021). https://doi.org/10.3390/diagnostics1 1101890 107. F. Perez et al., RADStation3G: a platform for cardiovascular image analysis integrating PACS, 3D+t visualization and grid computing. Comput. Methods Prog. Biomed. 110(3), 399–410 (2013). https://doi.org/10.1016/j.cmpb.2012.12.002 108. M. Fedele, A. Quarteroni, Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function. Int. J. Numer. Methods Biomed. Eng. 37(4), e3435 (2021). https://doi.org/10.1002/cnm.3435 109. J. Shen, A.H. Faruqi, Y. Jiang, N. Maftoon, Mathematical reconstruction of patient-specific vascular networks based on clinical images and global optimization. IEEE Access 9, 20648– 20661 (2021). https://doi.org/10.1109/ACCESS.2021.3052501 110. E. Bullitt et al., The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography. Neurobiol. Aging 31(2), 290–300 (2010). https://doi.org/ 10.1016/j.neurobiolaging.2008.03.022 111. S.R. Aylward, E. Bullitt, Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans. Med. Imaging 21(2), 61–75 (2002). https://doi.org/10.1109/42.993126 112. C. Geuzaine, J.-F. Remacle, Gmsh: A 3-D finite element mesh generator with built-in preand post-processing facilities. Int. J. Numer. Meth. Eng. 79(11), 1309–1331 (2009). https:// doi.org/10.1002/nme.2579 113. Q. Fang, D.A. Boas, Tetrahedral mesh generation from volumetric binary and grayscale images, in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2009), pp. 1142–1145. https://doi.org/10.1109/ISBI.2009.5193259 114. M. Strocchi et al., A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations. PLoS One 15(6), e0235145 (2020). https://doi.org/10.1371/ journal.pone.0235145 115. A.L. Marsden, Optimization in cardiovascular modeling. Annu. Rev. Fluid Mech. 46(1), 519–546 (2014). https://doi.org/10.1146/annurev-fluid-010313-141341 116. M. Baiocchi et al., Effects of choice of medical imaging modalities on a non-invasive diagnostic and monitoring computational framework for patients with complex valvular, vascular, and ventricular diseases who undergo transcatheter aortic valve replacement. Front. Bioeng. Biotechnol. 9, 389 (2021). https://doi.org/10.3389/fbioe.2021.643453

44

S. Khodaei and Z. Keshavarz-Motamed

117. T.B. Le, M. Usta, C. Aidun, A. Yoganathan, F. Sotiropoulos, Computational methods for fluid-structure interaction simulation of heart valves in patient-specific left heart anatomies. Fluids 7(3), Art. no. 3 (2022). https://doi.org/10.3390/fluids7030094 118. F. Viola, V. Meschini, R. Verzicco, Fluid–structure-electrophysiology interaction (FSEI) in the left-heart: a multi-way coupled computational model. Eur. J. Mech. B. Fluids 79, 212–232 (2020). https://doi.org/10.1016/j.euromechflu.2019.09.006 119. C. Andrés, M. Wenbin, M. Raymond, S. Wei, The impact of balloon-expandable transcatheter aortic valve replacement on concomitant mitral regurgitation: a comprehensive computational analysis. J. R. Soc. Interface 16(157), 20190355 (2019). https://doi.org/10.1098/rsif.2019. 0355 120. A. Santiago et al., Fully coupled fluid-electro-mechanical model of the human heart for supercomputers. Int. J. Numer. Methods Biomed. Eng. 34(12), e3140 (2018). https://doi.org/ 10.1002/cnm.3140 121. L. Garber, S. Khodaei, Z. Keshavarz-Motamed, The critical role of lumped parameter models in patient-specific cardiovascular simulations. Arch. Comput. Methods Eng. (2021). https:// doi.org/10.1007/s11831-021-09685-5 122. Z. Keshavarz-Motamed, A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases. Sci. Rep. 10(1), 6905 (2020). https:// doi.org/10.1038/s41598-020-63728-8 123. R. Sadeghi, S. Khodaei, J. Ganame, Z. Keshavarz-Motamed, Towards non-invasive computational-mechanics and imaging-based diagnostic framework for personalized cardiology for coarctation. Sci. Rep. 10(1), Art. no. 1 (2020). https://doi.org/10.1038/s41598-02065576-y 124. R. Sadeghi, B. Tomka, S. Khodaei, J. Garcia, J. Ganame, Z. Keshavarz-Motamed, Reducing morbidity and mortality in patients with coarctation requires systematic differentiation of impacts of mixed valvular disease on coarctation hemodynamics. J. Am. Heart Assoc. (in press). https://doi.org/10.1161/JAHA.121.022664 125. R. Sadeghi et al., Impact of extra-anatomical bypass on coarctation fluid dynamics using patient-specific lumped parameter and Lattice Boltzmann modeling. Sci. Rep. 12(1), Art. no. 1 (2022). https://doi.org/10.1038/s41598-022-12894-y 126. R. Sadeghi, N. Gasner, S. Khodaei, J. Garcia, Z. Keshavarz-Motamed, Impact of mixed valvular disease on coarctation hemodynamics using patient-specific lumped parameter and Lattice Boltzmann modeling. Int. J. Mech. Sci. 217, 107038 (2022). https://doi.org/10.1016/ j.ijmecsci.2021.107038 127. S. Khodaei et al., Personalized intervention cardiology with transcatheter aortic valve replacement made possible with a non-invasive monitoring and diagnostic framework. Sci. Rep. 11(1), 10888 (2021). https://doi.org/10.1038/s41598-021-85500-2 128. S. Khodaei, R. Sadeghi, P. Blanke, J. Leipsic, A. Emadi, Z. Keshavarz-Motamed, Towards a non-invasive computational diagnostic framework for personalized cardiology of transcatheter aortic valve replacement in interactions with complex valvular, ventricular and vascular disease. Int. J. Mech. Sci. 202–203, 106506 (2021). https://doi.org/10.1016/j.ijm ecsci.2021.106506 129. L. Cai et al., The comparison of different constitutive laws and fiber architectures for the aortic valve on fluid–structure interaction simulation. Front. Physiol. 12 (2021). [Online]. https:// www.frontiersin.org/article/10.3389/fphys.2021.682893. Accessed 26 May 2022 130. N. Bahadormanesh, B. Tomka, M. Kadem, S. Khodaei, Z. Keshavarz-Motamed, An ultrasound-exclusive non-invasive computational diagnostic framework for personalized cardiology of aortic valve stenosis. Med. Image Anal. 87, 102795 (2023). https://doi.org/ 10.1016/j.media.2023.102795 131. M. Mirramezani, S.L. Diamond, H.I. Litt, S.C. Shadden, Reduced order models for transstenotic pressure drop in the coronary arteries. J Biomech Eng 141(3), 0310051– 03100511 (2019). https://doi.org/10.1115/1.4042184

Enhancing Medical Imaging with Computational Modeling for Aortic …

45

132. S. Khodaei, L. Garber, J. Bauer, A. Emadi, Z. Keshavarz-Motamed, Long-term prognostic impact of paravalvular leakage on coronary artery disease requires patient-specific quantification of hemodynamics. Sci. Rep. 12(1), Art. no. 1 (2022). https://doi.org/10.1038/s41598022-21104-8 133. E. Ben-Assa et al., Ventricular stroke work and vascular impedance refine the characterization of patients with aortic stenosis. Sci. Transl. Med. 11(509), 11 (2019). https://doi.org/10.1126/ scitranslmed.aaw0181 134. Z. Keshavarz-Motamed et al., Elimination of transcoarctation pressure gradients has no impact on left ventricular function or aortic shear stress after intervention in patients with mild coarctation. JACC Cardiovasc. Intervent. 9(18), 1953–1965 (2016) 135. Z. Keshavarz-Motamed et al., Non-invasive determination of left ventricular workload in patients with aortic stenosis using magnetic resonance imaging and doppler echocardiography. PLoS One 9(1), e86793 (2014). https://doi.org/10.1371/journal.pone.0086793 136. S. Khodaei, et al. Early detection of risk of neo-sinus blood stasis post-transcatheter aortic valve replacement using personalized hemodynamic analysis. Struct. Heart 100180 (2023) 137. S. Khodaei, et al. Reducing long-term mortality post transcatheter aortic valve replacement requires systemic differentiation of patient-specific coronary hemodynamics. J. Am. Heart Assoc. 12(11), e029310 (2023) 138. J. Cowls, A. Tsamados, M. Taddeo, L. Floridi, A definition, benchmark and database of AI for social good initiatives. Nat. Mach. Intell. 3(2), Art. no. 2 (2021). https://doi.org/10.1038/ s42256-021-00296-0 139. M.A. Rogers, E. Aikawa, Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery. Nat. Rev. Cardiol. 16(5), Art. no. 5 (2019). https://doi.org/ 10.1038/s41569-018-0123-8 140. A. Karthikesalingam et al., An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair; a retrospective observational study. PLoS One 10(7), e0129024 (2015). https://doi.org/10.1371/journal.pone.0129024 141. H. De Cannière et al., Wearable monitoring and interpretable machine learning can objectively track progression in patients during cardiac rehabilitation. Sensors 20(12), Art. no. 12 (2020). https://doi.org/10.3390/s20123601 142. S. Chang et al., Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium. Eur. J. Radiol. 137, 109582 (2021). https://doi.org/ 10.1016/j.ejrad.2021.109582 143. R. van de Schoot et al., An open source machine learning framework for efficient and transparent systematic reviews. Nat. Mach. Intell. 3(2), Art. no. 2 (2021). https://doi.org/10.1038/ s42256-020-00287-7 144. C. Chen et al., Deep learning for cardiac image segmentation: a review. Front. Cardiovasc. Med. 7 (2020). [Online]. Available: https://www.frontiersin.org/articles/10.3389/fcvm.2020. 00025. Accessed 18 Apr 2023 145. K.K.L. Wong, G. Fortino, D. Abbott, Deep learning-based cardiovascular image diagnosis: a promising challenge. Futur. Gener. Comput. Syst. 110, 802–811 (2020). https://doi.org/10. 1016/j.future.2019.09.047 146. H. Greenspan, B. van Ginneken, R.M. Summers, Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016). https://doi.org/10.1109/TMI.2016.2553401 147. T. Mansi, T. Passerini, D. Comaniciu, Artificial Intelligence for Computational Modeling of the Heart (Academic Press, 2019) 148. F. Lopez-Jimenez et al., Artificial intelligence in cardiology: present and future. Mayo Clin. Proc. 95(5), 1015–1039 (2020). https://doi.org/10.1016/j.mayocp.2020.01.038 149. B. Feiger et al., Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks. Sci. Rep. 10(1), Art. no. 1 (2020). https://doi.org/10.1038/s41 598-020-66225-0 150. W. Yang, J.A. Feinstein, A.L. Marsden, Chapter 12—Computational modeling and personalized surgery, in 3-Dimensional Modeling in Cardiovascular Disease, ed. by E.M. Zahn (Elsevier, 2020), pp. 155–175. https://doi.org/10.1016/B978-0-323-65391-6.00012-0

46

S. Khodaei and Z. Keshavarz-Motamed

151. D.A. Steinman, F. Migliavacca, Editorial: special issue on verification, validation, and uncertainty quantification of cardiovascular models: towards effective VVUQ for translating cardiovascular modelling to clinical utility. Cardiovasc. Eng. Technol. 9(4), 539–543 (2018). https:// doi.org/10.1007/s13239-018-00393-z 152. H. Xu, D. Baroli, A. Veneziani, Global sensitivity analysis for patient-specific aortic simulations: the role of geometry, boundary condition and large eddy simulation modeling parameters. J. Biomech. Eng. 143(2) (2020). https://doi.org/10.1115/1.4048336 153. M.J. Grundeken et al., The need for dedicated bifurcation quantitative coronary angiography (QCA) software algorithms to evaluate bifurcation lesions. EuroIntervention 11(Suppl V), V44–V49 (2015). https://doi.org/10.4244/eijv11sva10 154. U. Morbiducci, A.M. Kok, B.R. Kwak, P.H. Stone, D.A. Steinman, J.J. Wentzel, Atherosclerosis at arterial bifurcations: evidence for the role of haemodynamics and geometry. Thromb. Haemost. 115(3), 484–492 (2016). https://doi.org/10.1160/th15-07-0597 155. M. Lodi Rizzini et al., Does the inflow velocity profile influence physiologically relevant flow patterns in computational hemodynamic models of left anterior descending coronary artery? Med. Eng. Phys. 82, 58–69 (2020). https://doi.org/10.1016/j.medengphy.2020.07.001 156. M. Lodi Rizzini et al., Modelling coronary flows: impact of differently measured inflow boundary conditions on vessel-specific computational hemodynamic profiles. Comput. Methods Prog. Biomed. 221, 106882 (2022). https://doi.org/10.1016/j.cmpb.2022.106882 157. A. Candreva et al., Current and future applications of computational fluid dynamics in coronary artery disease. RCM 23(11), Art. no. 11 (2022). https://doi.org/10.31083/j.rcm2311377 158. D.J. Quam et al., Immersive visualization for enhanced computational fluid dynamics analysis. J. Biomech. Eng. 137(3) (2015). https://doi.org/10.1115/1.4029017 159. S.A. Niederer, N.P. Smith, Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between? J. Physiol. 594(23), 6849–6863 (2016). https://doi.org/10.1113/JP272003 160. R.L. Winslow, N. Trayanova, D. Geman, M.I. Miller, Computational medicine: translating models to clinical care. Sci. Transl. Med. 4(158), 158rv11–158rv11 (2012). https://doi.org/ 10.1126/scitranslmed.3003528

Construction of an Algorithm for Three-Dimensional Bone Segmentation from Images Obtained by Computational Tomography Marta Barbosa, Francesco Renna, Nuno Dourado, and Rúben Costa

1 Introduction The skeleton is an elementary structure responsible for multiple functions, among which the structural function stands out, that besides allowing the protection of internal organs, it also allows locomotion [1, 2], as well as the metabolic function, which allows minerals to be reserved. The human skeleton is constituted of 206 bones, these being the most consistent structures in the body [3]. Besides their high hardness, bones present a complex, anisotropic, hierarchical and heterogeneous microstructure, being one of the most dynamic and metabolically active tissues [3–5]. As a way of accentuating the great complexity of this material, it is possible to observe alterations in the bone density upon administration or removal of loads, as well as in its shape, due to the consolidation of fractures or certain surgical interventions [3]. Moreover, in case of alteration in the metabolic balance of the body, the M. Barbosa (B) · F. Renna · R. Costa University of Porto, Porto, Portugal e-mail: [email protected] F. Renna e-mail: [email protected] R. Costa e-mail: [email protected] F. Renna INESC TEC, Porto, Portugal N. Dourado CMEMS-UMINHO, University of Minho, Braga, Portugal LABBELS—Associate Laboratory, Braga, Portugal N. Dourado e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Daimi et al. (eds.), Current and Future Trends in Health and Medical Informatics, Studies in Computational Intelligence 1112, https://doi.org/10.1007/978-3-031-42112-9_3

47

48

M. Barbosa et al.

structural function is always sacrificed in favour of the metabolic one [6], a sacrifice that causes a variation in the bone constitution and, consequently, an alteration in its mechanical properties. As the structural function is sacrificed, certain diseases begin to appear, such as osteoporosis, which causes the appearance of pores in the bone and, thus, a reduction in its mechanical resistance [7]. Therefore, in order to have a clear notion of the performance of the bone, it is necessary to have knowledge of its shape, thickness and the existence or not of pores. As for the classification by shape, there are different types of bones [2, 8]. Long bones are so called when they present a length greater than their thickness and width, with some examples of bones in this category being the femur, the tibia or the radius. These bones present a structure similar to the one schematized in Fig. 1, that is, they are divided into three main regions: the diaphysis, which is the central region of the bone, and the two epiphyses (proximal and distal), found at the extremities. It is then constituted of different types of tissues: the cartilage, present in the regions of the joints, the cortical bone tissue, which gives the bone strength and hardness, the cancellous bone tissue, that gives it lightness, and the marrow and blood vessels, responsible for carrying the fluids fundamental to life. As the regions present very different characteristics between them, this chapter focuses only on the diaphyseal region. This region has essentially cortical bone tissue on the outside, marrow and blood vessels on the inside, and spongy tissue in the border regions with the epiphyses. Fig. 1 Structure of a long bone (adapted from [9])

Construction of an Algorithm for Three-Dimensional Bone …

49

As a result of road accidents, agricultural and industrial accidents, cases associated with violence, falls, sports practice, medical conditions, among others [10], it is estimated that about 37% of the visits to hospital emergency rooms correspond to consequences of bone fractures. This is reflected in about 27 million people being admitted to hospitals and 1.9 million requiring hospitalisation worldwide [8]. In addition to this high incidence, traumatic injuries correspond to the fourth leading cause of death in the world, the main one for ages between 1 and 44 years. Even so, when they are not fatal, they sometimes lead to a reduction in the quality of life of the patient and to high costs, both for the health system and for society in general [8]. Precisely, due to these statistical data, there is an immediate need to act in this sector and develop solutions to control and reduce the risk of death or increase the quality of life of people who suffer this category of injuries. To develop bone fixation systems, it is essential to test different system geometries and fixation sites in order to identify which one is the most suitable. Hence, computer simulations become particularly interesting as they allow multiple variables to be tested and modified, saving time and resources. As it has been just seen, bone is not a homogeneous structure composed of only one type of tissue, and so, in order to obtain good simulations that represent reality, it is essential to have an almost exact knowledge of the real structure, that is, to distinguish clearly between cortical and spongy tissue. One of the best ways to differentiate these two types of tissue is to start from a diagnostic imaging exam and apply image processing to it until the desired result is achieved. After the discovery of X-rays in 1895, computed tomography (CT) was one of the methods that contributed most to medical imaging diagnosis. Firstly thought up and carried out by the English engineer Hounsfield in the 1970s, this is currently the key method used to acquire data from which three-dimensional models of long bones are generated [11]. This technique possesses great sharpness in the cuts obtained, which allows the structures present in the region to be analysed so that they can be delineated with great precision. In addition, it is considered to be a non-invasive method that allows images of the interior of the body to be obtained without superimposing anatomical structures [12–14]. As the name indicates, tomography derives from the Greek word “tomos”, which means “cut”. Thus, the CT is based on a system of cuts (sections) in which multiple 2D images of a 3D volume are obtained through successive scans of an X-ray beam in the same region and data computation processes. The operating principle is based on the amount of energy of the emitted X-rays that is absorbed and, with this information, the density of the structures of the slice is obtained by direct proportionality [15]. Joining all the cuts and extrapolating the values for the thickness between them, it is possible to visualize the three-dimensional structure that one wants to analyse [12]. In order to obtain computational models of medical relevance, it is necessary to consider both the various aspects related to the origin of the medical image (Xray, CT, magnetic resonance image, etc.) and its digitalization process, besides the processing itself. This happens since it is necessary to extract the structure of interest and treat it in order to eliminate unnecessary information or noise in the image that may generate errors in the final result.

50

M. Barbosa et al.

Accordingly, in this article it is intended to start from images obtained by computational tomography and, through attempts of several image processing techniques, evaluate which one presents a better performance to distinguish cortical bone tissue from cancellous bone tissue. In the end, the aim is to generate a three-dimensional model with the information of the two tissues that can be used to generate a solid model which allows the construction of finite element meshes (FEM) and to perform simulations that employ cohesive damage models.

2 Background The steps necessary to go from a CT dataset to the FEM results are represented in Fig. 2. The green zone (left) corresponds to the image segmentation and creation of the 3D model, objective of this work. In turn, the orange zone (right) corresponds to the finite element modelling, objective for a further approach.

Fig. 2 Steps to go from a TC dataset to finite element results [16]

Construction of an Algorithm for Three-Dimensional Bone …

51

The image processing techniques normally used in commercial softwares do not present the best results for long bones. Besides generally resorting to only one type of technique, the use of a threshold in most cases, they also apply only one value of this threshold to segment the whole region. These techniques, despite being able to present good results in small bones, become inaccurate in long bones, as these present anatomic regions with different contrast levels [11]. When searching the databases of scientific articles selected, results can be found for multiple articles on bone segmentation. Nonetheless, with long bones, 3D model formation and use with finite elements, results become very scarce. The literature addresses two main types of bone models with finite element application: the continuous model and the high-resolution model. The works based on the first model resort to a simplistic modelling, which uses the segmentation “shell” for the cortical zone and densities to model the cancellous part. In spite of this, some of the papers make simplifying assumptions, for example that the thickness of the “shell” is constant. Thus, there are articles, as is the case of Liebschner’s team [17] and that of Imai [18], which assume that throughout the whole bone the thickness of cortical tissue is the same, which implies a significant loss of anatomical information in modelling. On the other hand, regarding high-resolution models, the “shell” is generated automatically and, for this, it requires software with better segmentation techniques to distinguish with high precision the cortical tissue zone from that of the spongy tissue. Some authors also resort to a combination between the two techniques, such as Zysset and Curnier [19]. They recognise the importance of making a good distinction between the two types of bone tissue, however, they argue that information regarding the spongy tissue can be perfectly approximated with density-based models, assuming that the internal structure of the bone is homogeneous. Again, although it is recognised that this approach greatly simplifies the technique used, this modelling assumption is in stark contrast with the observation that the structure of bone is heterogeneous. Consequently, this technique does not correspond with high accuracy to reality. Focusing on the segmentation, this is still a challenging task and there is no single approach that addresses all the needs. The most effective algorithms are obtained through careful customisation of the component combinations, which are adjusted according to image characteristics and the desired result [16]. There are several advantages of fully automatic segmentation algorithms, namely the reduction of user intervention and the consequent reduction of human errors, as well as the repeatability of results even with different users. Despite this, in the literature review carried out, there were few results of applications of this methodology in long bones, since most of the results referred to CT of short bones, mostly metacarpal bones. Thus, it is extremely important to develop new algorithms for automatic segmentation in long bones. Taking into account the objectives of the present work, the article of Jan Dupej’s team [20] stands out, where, although there is no FEM construction, the algorithm presented builds solid models that, despite not having clear evidence, could be used in the construction of FEMs.

52

M. Barbosa et al.

The work by Tuck-Lee’s team [21] also considers a related task, however, it focuses on soft tissues which, as already seen, present completely dissimilar characteristics to long bone tissues. Despite this, the algorithm presented clearly allows the construction of FEM, despite the use of tetrahedral elements, when it would be preferable to use hexahedral ones, and the images used are obtained from micro-computed tomography, a technique that is not very widespread in clinical laboratories. The algorithm proposed by Sami P. Väänänen’s team [22] allows generating meshes of bone pieces with interest for fracture mechanics, in particular large bones, and it is clearly stated that the periosteal surfaces of the bones are obtained automatically. However, neither the type of finite element used nor the discretization of the models along the bone wall thickness is shown or referred. Thus, the application of the Computational Mechanics that was employed with the FEM developed is not clear, as the authors only refer to performing compression tests. Although they present deformation fields, they mention that these are obtained at the surface, citing another work by the same authors as a way of validating the results obtained. The revision of the current literature on this topic reveals a need to develop an algorithm that simultaneously clearly distinguishes the cortical from the cancellous tissue throughout the specimen under study, does not lose information about the anatomical structure (which, contrary to what is assumed in most of the bibliography, is not homogeneous), is able to be applied to long bones and allows for the generation of hexahedral FEMs.

3 Materials The equipment used for the imaging tests inherent to this project was a Siemens somatom definition flash machine belonging to the Centro Hospitalar Universitário São João (CHUSJ). This equipment allows the obtaining of CTs in which the spacing between each slice is only 0.5 mm, which to have a good perception of the morphology of the region to be analysed. Since one of the most common long bones to fracture is the tibia, the bones under study are tibias. The quick collection of the bones and performance of the exams after the death of the animal was considered, in order to bring the values as close as possible to the in vivo reality. Two CT scans of adult bovine tibiae (Fig. 3) acquired in a partner abattoir of the project were performed, corresponding to a total of 2 × 759 images with a 0.5 mm spacing between them. Among the literature reviewed regarding bone segmentation algorithms, most of them only used single bone images and the highest number was seventeen elements.

Construction of an Algorithm for Three-Dimensional Bone …

53

Fig. 3 One of the tibias used in the work

4 Methodology, Results and Discussion 4.1 Morphological Study Before starting the algorithm development, a study of the morphology inside the tibiae was carried out in order to get a better understanding of the tissues that constitute them and their characteristics. For that, some bovine tibiae were sawed transversally (Fig. 4) and longitudinally (Fig. 5). As can be seen in Fig. 4, the external region of the bone and lighter in colour (cortical tissue) does not have the same thickness throughout the whole diameter, as some algorithms consider. With the longitudinal cut (Fig. 5), it was found that the external region of the bone was very hard and lighter in colour. This was not the case inside the bone, which was very soft and pasty, full of blood vessels and therefore with a redder colouring. An attempt was made to remove the soft substance inside the bone (marrow + blood vessels) in order to visualize the other tissues more easily. This substance came off easily (Fig. 6), and it was possible to see that the spongy tissue (porous tissue) only existed at the extremities, and the cortical tissue (hard tissue) covered and shaped the whole structure (Fig. 7). The pores of the cancellous tissue were also filled with marrow.

4.2 Ground Truth Determination In order to have a means of numerical evaluation of the developed algorithm, initially a manual segmentation of all images from the database created for this project was

54 Fig. 4 Cross section of tibial diaphysis of a bovine

Fig. 5 Longitudinal section of the tibial diaphysis of a bovine

M. Barbosa et al.

Construction of an Algorithm for Three-Dimensional Bone …

55

Fig. 6 Removal of marrow and blood vessels

performed. To carry out this segmentation, the application “image label” of the Matlab software [23] was used. With the objective to simplify, as well as due to the complexity and time required to manually segment each of the cavities present in the spongy tissue as containing marrow, in the manual segmentation (which is considered as real value), the internal regions of these pores were considered as being spongy tissue as well.

4.3 Validation Metrics To quantitatively evaluate the results obtained, two metrics were calculated between the results obtained by the computational segmentation and the manual segmentation reference. The first metric was the Dice Coefficient, which generates a value based on how similar the analysed objects are (similarity). The way of calculating the Dice Coefficient is presented in Eq. (1) where A and B are the different images to compare.

56

M. Barbosa et al.

Fig. 7 Inside tibial diaphysis without medulla

Dice coefficient(A, B) =

2 ∗ |A ∩ B| |A| + |B|

(1)

This means that the Dice Coefficient will be a value between 0 and 1, with 1 meaning the greater similarity between the two images. In addition, the mean-squared error (MSE) was computed in order to evaluate the distance between the reference mask and the segmented image. Mathematically, this metric calculates the mean square difference between the estimated values and the real value, which means that the closer to zero, the more similar the images are. The formula for calculating this metric, as well as the explanation of its name, is presented in Eq. (2) [24].

1

(2)

Since each of these metrics is calculated per image, to calculate the metric on the tissue type the average of all the images of the respective tissue was used.

Construction of an Algorithm for Three-Dimensional Bone …

57

Segmentation The data were exported directly from the testing machine in DICOM format, which means that they present in their metadata multiple information that can and should be used in order to obtain the correct dimensions and morphologies of the object. The following data are of special relevance: • InstanceNumber—Corresponds to the sequential number of the image in the examination. It is particularly important for it to be possible to organize the images in order; • pixel_array—Corresponds to the value of the intensities of the image pixels; • SliceLocation, RescaleIntercept and RescaleSlope—These are relevant parameters for determining the distance between the different slices and, as such, they are important for maintaining proportions in 3D reconstructions. Bearing in mind the parameters that have been mentioned, one can start visualising the raw data. Among all the images that constitute the bone, three were selected to be the ones presented as visual results in all the tested techniques. Images 5, 100 and 250 were chosen because they correspond to different regions: 5 is the beginning of the diaphysis, 100 the centre of the diaphysis and 250 the final region. Figure 8 shows these images without any processing, that is, with the data as taken in the machine. In the different images of Fig. 8 it is possible to see an arch in the lower region of the images, which corresponds to the stretcher where the examination was performed, as well as some light and wavy surfaces, corresponding to the sheet placed between the bone and the stretcher. One can also distinguish a white region, indicative of the cortical tissue, a light grey region inside the cortical region, the medulla, small grey portions outside the cortical tissue, which are areas of badly removed flesh from the bone, and finally, especially in the first image, spongy tissue of intermediate grey colouring interspersed with the darker grey areas of the medulla, not very easy to distinguish by colours. With the images correctly loaded, different ways of segmenting them were tested.

Fig. 8 Original images to be segmented

58

M. Barbosa et al.

4.4 Segmentation Based on Morphological Filters The thresholding method is a technique in which the grey levels of an image are converted into 0s and 1s based on a given threshold. Mathematically, the initial image being defined by f (x, y), and T the threshold value, the Threshold function (g(x, y)) can be defined by Eq. (3). { g(x, y) =

0, 1,

f (x, y) < T f (x, y) ≥ T

(3)

The histograms of the images defined in Fig. 8 are shown in Fig. 9. The Hounsfield scale (HU) is a quantitative scale for describing radiodensity in medical CT and provides a precise density for each tissue type. On this scale, air (black on the grey scale) is represented by a value of −1000, and bone (white on the grey scale) between +700 (cancellous bone) and +3000 (cortical bone) [15]. One can easily find a relationship between the histograms in Fig. 9 and the values defined in this scale. This is achieved by converting the values of the different images to this scale and applying ranges of values with acceptable value intervals to the one defined by the scale. The visual results obtained for each type of tissue can be seen in Fig. 10. Observing the images in Fig. 10, it can be noted that, by applying a simple threshold filter based on the HU scale information and the image histogram, the cortical tissue presents an almost perfect result in the three images. The spongy tissue fails only at the edges of the cortical tissue, which is understandable given that, since they are edge pixels, they present less intensity. Additionally, the medulla is very well-identified, also presenting only some edges of the cortical tissue for the same reasons and regions external to the cortical tissue which are actually meat remains. In order to improve the results obtained, different morphological filters with different parameters were tested until the parameter and filter with the most favourable result was found.

Fig. 9 Histograms of the selected images

Construction of an Algorithm for Three-Dimensional Bone …

59

Fig. 10 Visual results obtained for a segmentation by threshold and HU scale

The Sobel filter [25] is widely used in image processing for contour detection. Its operating principle is simple: it calculates the intensity gradient at each point with the direction and intensity of the greatest variation, i.e., it shows how the luminosity in the image varies at each point (smoother or more abrupt) and, based on this information, as well as knowing that the greatest variations correspond to well-defined borders of the objects, its contours are known. With the same goal of defining the contours, the scikit-image library [26] has a function called clear_border [27] which uses a binary image. When this has points with value 1 isolated, it passes them to 0, thus allowing to obtain well-defined edges of the image. By applying these two filters to the test images, it was found that both filters visually delimit very well the edges of the cortical tissue, which may be very useful in the application of other filters in the future. However, the Sobel filter only shows the edges and requires a subsequent filling in the cortical tissue, and in the spongy tissue and medulla it does not limit as expected and the images of the two tissues are practically the same. The clear_border presents more advantages, in that it does

60

M. Barbosa et al.

Fig. 11 Visual comparison between the simple HU, HU + Sobel and HU + Clear_Border filters on the different tissues of the image 5

not present the borders, but clears the lost stitches, however, its application in no way improves the results for both the spongy tissue and the medulla. As an example, Fig. 11 shows the visual results of the application of these two filters on image 5. Looking at the visual results in Fig. 10, there appear to be small regions in the spongy tissue and marrow where continuity pixels seem to be missing. For this reason, a “closing” filter seemed a good approach to improve the results. This type of filter performs a dilation followed by erosion and is therefore mainly used to smooth out contours or fill small holes [28]. The results seemed quite satisfactory as many of the unfilled pixels inside the tissues were now filled, but there seems to have also been an addition of pixels from the edge regions and so there is a need for a little erosion. The purpose of the erosion is to reduce the areas in the object. Figures 12, 13 and 14 show the visual results obtained with the application of the closing filter and the closing filter followed by erosion. The metrics to numerically evaluate the performance of the developed algorithm are presented in Table 1. By observing Table 1, it can be seen that with the application of the knowledge obtained by the Hounsfield scale, the cortical tissue obtains an average Dice coefficient higher than 0.90, being even higher than 0.95, and an MSE lower than 0.01. When the bone is considered as a complete structure that includes the three types of tissues, the Dice rises to 0.98 and the MSE remains lower than 0.01. The results confirm that there was a slight improvement in the results with the closing filter. After erosion, although visually the disappearance of the boundary regions that did not correspond to the tissue under evaluation was observed, since the results of the numerical metrics worsen slightly, it can be concluded that part of the boundaries that should be considered were also removed.

Construction of an Algorithm for Three-Dimensional Bone …

61

Fig. 12 Visual comparison between HU, HU + closing filter and HU + closing filter + erosion filter on different tissues from image 5

With the segmentation using the HU scale and pixel-by-pixel filters, the best result of numerical evaluation metrics of the total bone was obtained for the HU scale + closing filter with a Dice of 0.98074 and an MSE of 0.00194, which represents extremely positive results.

4.5 Segmentation by Active Contour Methods The active contour method [29], often called Snake, is widely used to identify irregular shapes. This method is based on curves defined in the image that move by the action of internal forces of the curve itself and external forces caused by the characteristics of the image, which cause the snake to seek the border of the desired object.

62

M. Barbosa et al.

Fig. 13 Visual comparison between HU, HU + closing filter and HU + closing filter + erosion filter on different tissues from image 100

This method gives better results the better the initialisation applied to it. That is, instead of asking the algorithm to search for the forces of the image objects in the whole image, the smaller the area to search for these forces, the better the outcome, ensuring that all the object to be segmented is within the defined area. Due to the approximately circular shape of the bone, a circle was defined as the initial area and it was confirmed that the whole bone was within the defined circle in all images. Figure 15 shows the area defined as the initialisation region which presented the best results. It should be noted that the algorithm is very sensitive to the area and location of this region. An attempt was made to use this method to determine the edges of the tissues under study, however, good results were only obtained for the cortical bone tissue after multiple combinations of the alpha, beta and gamma parameters, with alpha being related to the distances between contour points, beta being related to the smoothing of the points and gamma being related to the smoothing of the contour line points.

Construction of an Algorithm for Three-Dimensional Bone …

63

Fig. 14 Visual comparison between HU, HU + closing filter and HU + closing filter + erosion filter on different tissues from image 250 Table 1 Results of the metrics for evaluating the performance of the different morphological filters tested Tissue

HU + histogram

HU + close

HU + close + erode

Dice

MSE

Dice

MSE

Dice

MSE

Cortical

0.95607

0.00244

0.95494

0.00251

0.87457

0.00634

Cancellous

0.12238

0.01070

0.16228

0.00501

0.13395

0.00512

Medulla

0.64903

0.00551

0.82023

0.00739

0.73312

0.00839

Bone

0.98069

0.00194

0.98074

0.00194

0.95191

0.00464

64

M. Barbosa et al.

Fig. 15 Initialization set for active_contour method on selected images

Fig. 16 Visual results for contour determination with the active_contour method for the best parameters found

Figure 16 shows the best results that could be obtained, corresponding to parameters of alpha = 75, beta = 5 and gamma = 1. This method is extremely sensitive to the variation of all parameters, so it is quite complicated to find the ideal value. In this way, Fig. 17 shows examples of visual results obtained by varying slightly some of the parameters considered ideal. The working principle of the random walker segmentation algorithm [30] is based on the neighbourhood information, calculating weights for the pixel and gathering pixels that present similar weights. Figure 18 shows the best result obtained for the segmentation based on this algorithm. As can be seen, in the last two images (image 100 and 250), the result for the bone segmentation was almost perfect if the goal was to segment the whole bone, ignoring its tissue type. However, in image 5, the first one, the result was not so favourable and the segmentation obtained was corresponding only to the spongy tissue and medulla. With these results, it is believed that this algorithm would be a good approach to segment bone, but only if it is complemented with other forms of segmentation and if the goal is not to distinguish tissue types with close intensities, as is the case of bone tissues.

Construction of an Algorithm for Three-Dimensional Bone …

65

Fig. 17 Examples of visual results for contour determination with the active_contour method using other parameters in image 100

Fig. 18 Visual results for contour determination with the Random Walker Segmentation method

The FindContours algorithm of the OpenCV library is one of the most used to segment objects in images. This algorithm starts from a binary image, in which the background must be black and the objects white, and presents the contours of the detected objects. Figure 19 presents the visual results obtained with this technique. Once again, it was not possible to distinguish the different types of tissues, but, considering the bone as a whole, the visual results seem exceptional. Although with the active contour algorithms, it is only possible to separate the bone from the remaining structures without being able to distinguish its different tissues, the numerical evaluation metrics used in the other forms of segmentation were also calculated. These metrics are shown in Table 2. As it can be seen in Table 2, with the segmentation using the Random Walker Segmentation algorithm, Dice values were considerably more favourable than in the active_contour method. As it could be predicted by the visual results, the Dice and MSE values by the FindContours method are better than the other tested semiautomatic segmentation techniques. Still, they are still not better than the combination between HU scale and a closure filter.

66

M. Barbosa et al.

Fig. 19 Visual results for contour determination with the FindContours method

Table 2 Results of the performance evaluation metrics of the different active contouring methods tested Tissue

Active_contour Dice

MSE

Dice

MSE

Dice

MSE

Bone

0.93094

0.00745

0.94184

0.089562

0.95191

0.00464

Random_walker segmentation

FindContours

4.6 3D Model A 3D model uses a software to represent an object. To be able to do this, the software maps both its shape and other properties onto a three-dimensional coordinate system. The shape is represented by a set of points, called vertices, and these vertices connect to shape lines, known as edges. A closed set of three or more edges connect to form faces, being a 3D model a set of faces. These faces are generated through a mesh, and so the better the mesh, the better the generated 3D model. Due to the existence of good functions for creating and enhancing the mesh in the VTK library, this was the most used library in this process. VTK is short for Visualization Toolkit and consists of a library of classes that, although developed in C++, has many interfaces that allow it to develop applications in other languages, including Python. This library is used for 3D computer graphics, as well as for image processing and visualisation [31]. As is the requirement of most of the functions of this library, binary images that were NumPy arrays are initially converted to vtk. In this conversion, attention is paid to the origin, the dimensions, the spacing and the directions of the images that constitute the numpy array, so that there are no deformations in them. In the vtk format, the mesh can be created to generate the three-dimensional model. The mesh of the model developed in this work follows the following methodology: • Mesh creation by “marching_cubes”;

Construction of an Algorithm for Three-Dimensional Bone …

67

Marching cubes [32] is a computer graphics algorithm, published in 1987 by Lorensen and Cline [33], for extracting a polygonal mesh from a three-dimensional scalar isosurface (voxels). • Smoothing of the mesh; Due to irregularities in the created mesh, a smoothing of the mesh becomes indispensable, for which the Windowed Sinc PolyData filter (vtkWindowedSincPolyDataFilter [33]) was used. This filter “smooths” the mesh, making the voxels better modelled and the vertices more evenly distributed. In this filter, for each vertex, v, a topological and geometric analysis is performed in order to determine which vertices and cells are connected to v. Then, a connectivity array is built for each vertex (the connectivity matrix is a list of lists of vertices that connect directly to each vertex). After this action, an iteration phase starts on all vertices. For each vertex v, the coordinates of v are modified using a windowed sinc function interpolation kernel [34]. • Convert the mesh to decimal; Mesh decimation aims to optimise the 3D model by simplifying the images and reducing the file size, thus increasing the image loading speed. This function reduces the number of vertices, edges, or faces that make up the surfaces of the polygons that form the building blocks of the 3D model, without substantially changing the shape, volume or boundaries of the model. • Filling holes in the mesh; To identify and fill holes in the mesh, the Fill Holes filter (vtkFillHolesFilter [35]) was used. • Mesh triangulation; The faces that constitute a mesh are polygons that can be composed of three or more edges. However, the polygons formed by three edges (triangles) are the easiest to manage and, therefore, a triangulation of the generated mesh was performed. For this, the triangulation filter (vtkTriangleFilter [36]) was used. • Removal of redundant points; With a triangulation of the mesh, redundant or irrelevant points may remain, so it is good practice to merge or remove these points. For this purpose, a function to remove unnecessary points (vtkCleanPolyData [37]) was used. A good example of the applicability of this function is to imagine that imagining the generation of a cube. A cube is made up of 6 different faces, and each of these faces is a square, so it is made up of 4 vertices, giving a total of 24 vertices. When you put the faces together to make a cube, some of these vertices are overlapped, and only 8 are distinct from each other. What this function does here is merge all the vertices that are overlapped into one.

68

M. Barbosa et al. A

B

C

Fig. 20 Result of the 3D model created for one of the tibias in the database. The mesh generated is highlighted on A, a mesh can be seen in B on the solid to apply simulations and the object generated through the mesh can be seen on C

• Norm filter. To finish creating the mesh, a norms filter (vtkPolyDataNormals [38]) is applied in order to reorder polygons to ensure consistent orientation between neighbouring polygons and removal of sharp edges. Finally, once the 3D model has been created, it is converted from vtk to .STL so that it can be read by most 3D visualisation programmes. Figure 20 shows an image of the 3D model created from the best segmentation obtained by the techniques tested. That is, segmentation based on HU scale information with a closing filter. The model of Fig. 20 is formed by two objects (one corresponding to the cortical bone tissue and the other to the cancellous bone tissue) which correspond to the tissues that present some relevant mechanical resistance (the medulla and the blood vessels present negligible properties in the order of great of the others).

5 Conclusions The present work intends to identify the possibility, through image processing techniques, of generating a three-dimensional model for bone structures whose focus is not only to collect information about the external surfaces of this organ, but also to acquire knowledge about its anatomical structure and develop it. For this, the aim was to create a database with images coming from long bones, as well as to use these images to test different segmentation techniques and, finally, to create a function to generate a three-dimensional model using the best available segmentation.

Construction of an Algorithm for Three-Dimensional Bone …

69

Based on the defined objectives, a database with a total of 1518 images from CT scans of bovine tibiae was created, among which about half were segmented manually. In order to identify the best segmentation technique for the problem under study, the segmentation of cortical, cancellous and medullar tissues was tested based on the image histogram information, the simple Hounsfield scale (HU) information, the HU information with morphological operator filters and the active contour methods (active contour, random walker segmentation and findContours). These segmentations were evaluated qualitatively through a visual comparison between them and quantitatively through the calculation of the parameter Dice Coefficient and MeanSquared Error. Among the techniques studied, the conversion to HU scale followed by the application of a morphological filter (closing) was the technique that presented the best results considering the different tissue types and considering the whole bone ignoring the tissues. For the bone values, a Dice Coefficient higher than 0.98 and a Mean-Squared Error lower than 0.01 were obtained, and for the tissues a Dice higher than 0.95 was obtained for the cortical tissue (the tissue that has the most impact on the mechanical properties of bone). By using the active contour techniques it was not possible to distinguish the different bone tissues in the segmentations, but through the remaining tested techniques it was possible. This work also presents an algorithm to generate a three-dimensional model of the segmented bone in .stl format that was obtained based on the formation of a mesh in .vtk format.

References 1. A. Remedios, Bone and bone healing. Vet. Clin. North Am. Small Anim. Pract. 29(5), 1029– 1044 (1999) 2. G.J. Tortora, B.H. Derrickson, Principles of Anatomy and Physiology (Wiley, 2018) 3. A. Completo, F. Fonseca, Fundamentos de Biomecânica Músculo- Esquelética e Ortopédica (Publindústria, Produção de Comunicação, Lda, 2011) 4. J.D. Heckman, R.W. Bucholz, P. Tornetta III, Rockwood and Green’s Fractures in Adults, 8th edn. (LWW, 2015) 5. J.-Y. Rho, L. Kuhn-Spearing, P. Zioupos, Mechanical properties and the hierarchical structure of bone. Med. Eng. Phys. 20(2), 92–102 (1998) 6. F. Judas, P. Palma, R. Falacho, H. Figueiredo, Estrutura E Dinâmica Do Tecido Ósseo (2012) 7. M. Mehta, P. Strube, A. Peters, C. Perka, D. Hutmacher, P. Fratzl, G.N. Duda, Influences of age and mechanical stability on volume, microstructure, and mineralization of the fracture callus during bone healing: is osteoclast activity the key to age-related impaired healing? Bone 47(2), 219–228 (2010). https://doi.org/10.1016/j.bone.2010.05.029 8. J. Ebnezar, Textbook of Orthopedics: With Clinical Examination Methods in Orthopedics (JP Medical Ltd., 2010) 9. The Musculoskeletal System (Structure and Function) (Nursing) Part 1 [Online], http://whatwhen-how.com/nursing/the-musculoskeletal-system-structure-and-function-nursing-part-1/. Accessed 04 March 2023

70

M. Barbosa et al.

10. M.J. Sánchez-Fernández, H. Hammoudeh, R.P. Félix Lanao, M. van Erk, J.C.M. van Hest, S.C.G. Leeuwenburgh, Bone-adhesive materials: clinical requirements, mechanisms of action, and future perspective. Adv. Mater. Interfaces 6(4), 1802021 (2019) 11. K. Rathnayaka, T. Sahama, M.A. Schuetz, B. Schmutz, Effects of CT image segmentation methods on the accuracy of long bone 3D reconstructions. Med. Eng. Phys. 33(2), 226–233 (2011). https://doi.org/10.1016/j.medengphy.2010.10.002 12. J.B.M. Pisco, Noções fundamentais de imagiologia (1998) 13. E.Y. Lee, A. Hunsaker, B. Siewert, Computed Body Tomography with MRI Correlation (Lippincott Williams & Wilkins, 2019) 14. M.M. Khalil, P. Zanzonico, Basic Sciences of Nuclear Medicine (Springer, 2011) 15. R. Bibb, D. Eggbeer, A. Paterson, Medical imaging. Med. Model 7–34 (2015). https://doi.org/ 10.1016/B978-1-78242-300-3.00002-0 16. D.H. Pahr, P.K. Zysset, From high-resolution CT data to finite element models: development of an integrated modular framework. Comput. Methods Biomech. Biomed. Eng. 12(1), 45–57 (2009). https://doi.org/10.1080/10255840802144105 17. M.A.K. Liebschner, D.L. Kopperdahl, W.S. Rosenberg, T.M. Keaveny, Finite element modeling of the human thoracolumbar spine. Spine (Phila. Pa. 1976) 28(6), 559–565 (2003). https://doi. org/10.1097/01.BRS.0000049923.27694.47 18. K. Imai, I. Ohnishi, M. Bessho, K. Nakamura, Nonlinear finite element model predicts vertebral bone strength and fracture site. Spine (Phila. Pa. 1976) 31(16), 1789–1794 (2006). https://doi. org/10.1097/01.brs.0000225993.57349.df 19. P.K. Zysset, A. Curnier, An alternative model for anisotropic elasticity based on fabric tensors. Mech. Mater. 21(4), 243–250 (1995). https://doi.org/10.1016/0167-6636(95)00018-6 20. J. Dupej, A. Lacoste Jeanson, J. Pelikán, J. Br˚užek, Semiautomatic extraction of cortical thickness and diaphyseal curvature from CT scans. Am. J. Phys. Anthropol. 164(4), 868–876 (2017). https://doi.org/10.1002/ajpa.23315 21. J.P. Tuck-Lee, P.M. Pinsky, C.R. Steele, S. Puria, Finite element modeling of acoustomechanical coupling in the cat middle ear. J. Acoust. Soc. Am. 124(1), 348–362 (2008). https:// doi.org/10.1121/1.2912438 22. S.P. Väänänen et al., Automated segmentation of cortical and trabecular bone to generate finite element models for femoral bone mechanics. Med. Eng. Phys. 70, 19–28 (2019). https://doi. org/10.1016/j.medengphy.2019.06.015 23. Get Started with the Image Labeler-MATLAB & Simulink, https://www.mathworks.com/help/ vision/ug/get-started-with-the-image-labeler.html 24. V. Haltakov, Math Terms Mean Squared Error (MSE) 25. Feature Detectors—Sobel Edge Detector [Online], https://homepages.inf.ed.ac.uk/rbf/HIPR2/ sobel.htm 26. S. Van Der Walt et al., Scikit-image: image processing in python. PeerJ 1, 2014 (2014) 27. Module: Segmentation—skimage v0.20.0 docs [Online], https://scikit-image.org/docs/sta ble/api/skimage.segmentation.html#skimage.segmentation.clear_border. Accessed 20 March 2023 28. R.C. Gonzales, R.E. Woods, Digital Image Processing, 3rd edn. (Pearson Prentice Hall, New Jersey, 2008) 29. M. Kass, A. Witkin, Snakes: active contour models. Int. J. Comput. Vis. 321–331 (1988) 30. N. Craswell, M. Szummer, Random walks on the click graph, in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’07 (2007), pp. 239–246 31. W. Schroeder, K.M. Martin, W.E. Lorensen, The Visualization Toolkit an Object-Oriented Approach to 3D Graphics (Prentice-Hall, Inc., 1998) 32. VTK: vtkMarchingCubes Class Reference [Online], https://vtk.org/doc/nightly/html/classv tkMarchingCubes.html 33. W.E. Lorensen, H.E. Cline, Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggraph Comput. Gr. 21(4), 163–169 (1987)

Construction of an Algorithm for Three-Dimensional Bone …

71

34. VTK: vtkWindowedSincPolyDataFilter Class Reference [Online], https://vtk.org/doc/nightly/ html/classvtkWindowedSincPolyDataFilter.html 35. VTK: vtkFillHolesFilter Class Reference [Online], https://vtk.org/doc/nightly/html/classvtkF illHolesFilter.html 36. VTK: vtkTriangleFilter Class Reference [Online], https://vtk.org/doc/nightly/html/classvtkT riangleFilter.html 37. VTK: vtkCleanPolyData Class Reference [Online], https://vtk.org/doc/nightly/html/classvtkC leanPolyData.html 38. VTK: vtkPolyDataNormals Class Reference [Online], https://vtk.org/doc/nightly/html/classv tkPolyDataNormals.html

Healthcare/Medical Information Systems Supporting Patients and the Public

Point-of-Care Devices in Healthcare: A Public Health Perspective Armita Zarnegar

1 Introduction Point-of-Care testing (POCT) is performed near or at the site of a patient at far less cost than that of laboratory tests and is predicted to disrupt clinical practice [56]. POC testing offers several benefits, including higher portability, more timely results, more convenience and less need for medical expertise, rapid emergency detection, and lower costs [59]. These devices also reduce human errors compared to manual data recording [1]. Haghi et al. [35] suggest that many POCT and diagnostic devices are now small enough to be wearable and connected via the internet. These will transform medicine from symptom to disease to treatment to monitoring to prediction to prevention [35]. The wearables market has driven the growth of the medical device market and made the healthcare industry’s future more secure and reliable [1]. It is predicted that by 2026, the global POCT market will reach $50 billion [93]. Hospitals and clinics use most of the products, but aged care facilities and laboratories are other consumers of POC devices. The home market for POC devices is the third largest market after those of hospitals and clinics; with the introduction of wearables, this whole market is growing rapidly [70]. POC devices are currently and routinely used to perform an equivalent of laboratory testing at home. This includes tests for blood glucose [68], brain natriuretic peptide [4], glycated hemoglobin [29], coagulation [85], cardiac markers [38], thyroid stimulating hormones [19], blood gases [43], and many other biomarkers. Wearable technologies have proven effective in advanced wound diagnostics [86] and managing complicated catheters [80]. Disease management devices can detect antibodies (IgG and IgM) for dengue virus in human blood, and other viruses, such as SARS-Cov-2, or perform rapid drug screening [47]. Currently, advanced care such as dialysis and oxygen therapy are running at home [74]. Emerging invasive PoC A. Zarnegar (B) Swinburne University of Technology, Melbourne, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Daimi et al. (eds.), Current and Future Trends in Health and Medical Informatics, Studies in Computational Intelligence 1112, https://doi.org/10.1007/978-3-031-42112-9_4

75

76

A. Zarnegar

sensors have demonstrated multifunctional capabilities such as sweat stimulation. They can also be connected to drug delivery units via a wireless network, enabling an active role of the technology in disease management [79]. Haghi et al. [35] stated that the use of wearable devices for proactive healthcare can create Big data, enabling the linking of monitoring data to external healthcare systems. The big data generated by wearable devices is both a challenge and an opportunity for researchers to further develop and apply Artificial Intelligence (AI) techniques for risk assessment and optimal service quality in hospitals by using real-time monitoring and detecting the spreading of a disease earlier [78]. Haghi et al. [35] categorized POC devices into four domains: (i) environmental parameters only, (ii) environmental and behavioral parameters, (iii) environmental, behavioral, and physiological parameters, and (iv) environmental, behavioral, physiological, and psychological parameters. They also considered the mode of wearability, costs, and prolonged monitoring. Based on applications, POC can be categorized into monitoring [47], specific diagnosis [48], and disease management [60]. POC devices have also been categorized based on several other characteristics [47], including disposability, reusability, multi-functionality, invasiveness or non-invasiveness, field, and medical grade or home use.

2 Public Health Implications of POC Despite the constant increase in the utilization of POCT and wearables, few studies have examined the public health benefits of such devices. We suggest that POC devices can enhance public health in four ways and through (i) enhancing patient engagement, (ii) rebalancing health inequalities caused by distance (to medical facilities/experts), cost, or other factors, (iii) detecting undiagnosed conditions, (iv) conducting more sophisticated health data analytics for epidemiological insights, and (v) relieving the pressure that is on the healthcare system. These five proposed public health benefits of POC devices and POCT are discussed in the following sections.

2.1 Patient Engagement and Health Literacy We claim that POC devices increase patient engagement. Graffigna and Barello [33] define patient engagement as a set of behaviors including two overarching domains: (i) managing health behaviors, which are both the self-management of chronic disease and the adoption of healthy behaviors, and (ii) managing healthcare behaviors, which can be both patient and consumeristic behaviors. Patient engagement is associated with reduced risk and errors in the healthcare system and also with increased effectiveness of the treatment plan and efficiency of the system [16]. One of the enablers of patients’ engagement is access to information which increases self-efficacy/risk perceptions and increases intention to act.

Point-of-Care Devices in Healthcare: A Public Health Perspective

77

eHealth and eHealth technologies are proven to be effective resources for fostering the active role of patients in managing their healthcare by improving patients’ awareness, access, and ease of use of their healthcare information. Patients’ behaviors are often observed by monitoring interactions with eHealth tools. This results in the patients’ acquiring new skills to effectively manage their illness experience, a systematic review of the literature reported [14]. A separate report by the World Health Organisation (WHO) shows that eHealth can significantly improve health outcomes for people with multi-morbidity [13]. Patient engagement in managing diabetes was also found to increase the use of a blood glucose monitor [67]; however, only a few other studies evaluated patient engagement using POC devices. There is a need for long-term research to study such an impact. One of the reported barriers to patient engagement is time-intensive involvement with practitioners [24]. POC devices can substantially reduce time-intensive involvements as many tests can be performed in a short amount of time by the patient, and results become available in a short period of time. Another enabler of patient engagement offered by POC devices is keeping patients at home, in their environment outside where services are delivered, to increase participation and comfort. Patients who use POC devices may have better health outcomes overall compared to those who do not have access to the same technology. A study by Gialamas et al. [32] showed that patients that used POCT test results were at least as medication adherent as those who had the tests in laboratory settings, with 2.3% higher adherence in the intervention group compared to the control group. While medication adherence is one of the core issues in improving patients’ health, especially in the cases of chronic diseases, such adherence can be affected by several factors. Previous studies have found that active patient involvement in decision-making is one of such factors that can positively impact patient outcomes, see e.g., [42]. The utilization of POC devices provides patients with immediate results which is, therefore, seen as a means through which patients can become more actively involved in the doctor-patient decision-making processes and hence improved patient outcomes are expected. Such improved outcomes also include better medication adherence. Higher medication adherence by patients will also ease the burden on the healthcare system [32]. The use of POC devices by patients and at their locations may, however, pose some difficulties and health risks where patients become either overly concerned or too lenient toward the POC results, especially in the case of devices that are not directly connected to any healthcare management system or premises for expert monitoring. To our knowledge, these difficulties are yet to be systematically identified and studied at scale.

2.2 Health Inequity Cuts in health funds and competition for budgets require enhanced economic efficiency in the provision of healthcare services. Challenges of generating enough supply for global health diagnosis have been discussed in previous works [22].

78

A. Zarnegar

Insufficient competition among producers that are capable of innovating for populations with limited purchasing power and low-resource settings are among such barriers [77]. Access to POC testing services reduces test result turnaround times and facilitates disease diagnosis and treatment initiation [25]. These devices impact on access, utilization, and equity of health diagnosis, ultimately resulting in improved health outcomes, particularly in resource-limited settings [49]. Previous studies on health inequity found that utilizing technology reduces such inequity over time [87, 94]. A recent study of POC devices in rural areas in Australia [89] found that POC devices are beneficial for maternal, pediatric, and neonatal care. HELLP syndrome (hemolysis, elevated liver enzymes, and low platelet count) are pregnancy-associated health effects, leading to high morbidity and mortality incidences. HELLP is particularly difficult to diagnose and manage, as it progresses extremely fast, often leading to organ failures, coma, or death in less than 3 h. Early and accurate diagnosis of such conditions is a vital prerequisite for patient survival. A mobile phone-based POC low-cost platform for detecting hemolysis was shown to provide life-saving benefits within a 10-min turnaround time with a cost of approximately $1 per unit. This was a significant improvement over the non-POC setting that would take more than 4 h using traditional laboratory (analytical) methods that are not accessible in remote areas [10, 89]. Screening clinics that make use of POC devices can offer economic benefits, are easy to set up, and can replace costly traditional clinics. A study by Shephard et al. [73], showed that POC devices could make a difference in rural areas in Australia, where access to laboratory-based pathology testing is often limited, and the burden of chronic, acute, and infectious diseases is high. Therefore, we argue that setting up monitoring clinics utilizing low-cost POC devices helps with health inequality.

2.3 Detecting Undetected Conditions Many people have undetected diseases such as diabetes and hypertension [90]. A Canadian study assessed the prevalence of diagnosed diabetes, undetected diabetes, prediabetes, and their distribution across socio-demographic and lifestyle factors to report that 37.5% of the cases are undiagnosed [37]. Diabetes and prediabetes were more commonly diagnosed among less-educated individuals than higher-educated people. These conditions were also more commonly diagnosed among individuals with lower to middle-income levels than those with the highest income level [77]. Another study on an Indian population suggested that the rate of undetected diabetes is nearly 50%. These individuals are at increased risk of developing diabetic complications [77]. The same study reported that the ratio of undiagnosed to diagnosed diabetes is higher in rural areas than in urban areas. The HUNT Study [40] in Norway found that the mortality rate between detected and undetected diabetes can increase the mortality hazard ratio to 1.96. Hypertension is another condition that can lead to severe complications if untreated. It is the leading cause of end-stage renal disease and a significant risk factor for hem-

Point-of-Care Devices in Healthcare: A Public Health Perspective

79

orrhagic and thrombotic strokes. Hypertension is directly responsible for 57% of all deaths resulting from stroke and 24% of deaths related to coronary heart disease in India [8]. Hypertension is often asymptomatic; thus, it commonly remains undetected. Adhikari et al. [3] found that 54% of people were unaware of their hypertensive status. The utilization of POC devices by patients has been shown to improve the detection rate [84].

2.4 Big Data Mining and Knowledge Discovery According to the report released by Cisco, there are currently 29.3 billion connected devices globally by 2023 [66]. The most prevalent application of such devices is in automotive and transportation, where health and personal monitoring is the third most common application [20]. IoT medical devices consist of many layers; the device layer consists of sensors, actuators, embedded systems, and physical objects that collect information. The transmission layer sends data through media such as Bluetooth, Wi-Fi, and wired connections to the internet or a nearby device to get transferred to a middleware layer where data are stored, processed, and analyzed. At the end of this architectural pipeline, the application layer delivers customer-oriented services to the user and directly interacts with the end-user. There is also a business layer that oversights the entire process and takes care of the privacy of users and the business model [65]. POC devices and wearables also generate streams of data in real time. Such data have a powerful potential to be used for identifying new digital markers and patterns of risk, automated patient health monitoring, analyzing human behavior, and therapy management. When combined with clinical data, realtime POC data can improve chronic disease management and quality of life while preventing serious disease-related complications [45]. A recent report by Deloitte emphasizes that having shared data across sectors is one of the future dimensions of public health promotion [15]. The volume of information and insight provided by such data will profoundly affect the way many chronic diseases, such as diabetes, are prevented, managed, and characterized. This is a significant change from (and an improvement over) the current status in which patients are characterized by only a few recent measurements of fasting glucose levels and glycated hemoglobin to a world where various key parameters at thousands of time points are available and analyzed to assist healthcare professionals and research scientists [28]. Traditional mechanisms of healthcare and health management are costly [54], lack the capability to effectively manage the increasing number of patients and diseases [81], and will not process huge amounts of medical data [69]. Further, data collection in health primarily focused on supporting care rather than analysis, and machine learning techniques lack interpretability in the health context [31]. Also, these models may have algorithmic bias and lack generalizability [44], while most of them do not provide reasoning (unless in very specific contexts where decision trees are used) [5]. In addition, machine learning techniques do not generate new

80

A. Zarnegar

knowledge, nor do they discover hidden patterns from the available health-related data [65]. Besides, there are security risks [57] due to the storage of an immense amount of data in a single database. An integrated platform is required where cloud computing serves as the backend computing infrastructure to store and process big data in combination with AI and machine learning algorithms to collect, store, and analyze data from wearables and POCT. Jiang et al. [39] presented a prototype for the elderly healthcare system based on big data requirements. The authors stated that the big data approach would serve the long-term goal of elderly wearable healthcare systems. The major challenges that need to be addressed in a big data platform based on wearable systems include dynamic input data, the nonlinear trend of sensor data, and the multi-variant nature of the sensor data. The top data mining tasks performed on POC data are anomaly detection, prediction, and decision-making when considering continuous time-series measurements [12]. Two challenges in this area are data preprocessing and system reliability [65].

2.5 Impact on Healthcare Systems and Health Promotion The US Census Bureau expects that by 2060, people aged 65 and over will comprise a quarter of the country’s population, amounting to 95 million individuals [83]. As a result, the demand for technologies supporting home care will constantly grow [61]. Population aging is resulting in increased needs for and costs of healthcare. A literature review on monitoring technologies has suggested that a combination of monitoring technologies, including ambient and wearable sensor technologies, is the most effective solution for independently living older people [61]. A systematic review of in-home care monitoring in the aging population shows that remote monitoring reduces specific 30-day hospital readmission and mortality rates [72]. Remote Patient Monitoring (RPM) has become increasingly popular during the COVID pandemic [76]. Mayo Clinic, a US-based nonprofit healthcare organization, used RPM to monitor Coronavirus patients’ conditions at home and reported that remote monitoring devices resulted in early detection which can limit the severity of decompensation potentially reducing the need for admission or shortening a hospital stay [21]. The European Society of Cardiology (ESC) recommended RPM to reduce the number of in-office follow-ups in patients with a Pacemaker (PMK) who have difficulties attending in-person visits, which also prevents the spread of the virus among cardiovascular patients [55]. In the CHAMPION trial study, an implantable microsystems technology was used in chronic heart failure patients to prevent Acute Heart Failure (AHF) through pulmonary artery pressure monitoring versus usual care, showing a significant reduction in hospitalization for AHF patients [2]. Using POC to measure troponin, a complex protein found exclusively in the heart and released into blood upon myocardial injury, reduces morbidity and mortality by expediting necessary interventions. This also reduces the waiting time and resource utilization in patients with concerning symptoms in whom the condition must be ruled out [23].

Point-of-Care Devices in Healthcare: A Public Health Perspective

81

Patient outcomes could be generally improved with device-based intensive monitoring compared with traditional in-clinic follow-ups at regular intervals [64]. As one other type of POC device, Point of Care Ultrasound (POCUS) at home has also been found to lead to better patient outcomes [9, 62]. POCUS is also used to assess the abdominal cavity to identify fluid accumulation and check kidneys for obstructive pathology and major vasculature (e.g., to evaluate circulatory volume) [23]. A systematic review of POCUS in primary care settings shows that POCUS not only outperforms conventional X-rays in some conditions (e.g., rib fractures, tibia and fibula fractures, and pneumothorax) but also, results in faster diagnosis, decreased discharge time, and decreased treatment failure rates [75].

3 Challenges and Limitations As reviewed in the previous sections, there are several benefits to the utilization of POC devices in public health including patient engagement, impact on health inequality, and health promotion. However, there are currently some issues hindering the full benefits of POCT for public health monitoring and management. These issues include POC data management and integration, the calibration of POC devices and evaluation of their data, protocols used for data collection and transfer, connectivity, as well as a lack of a global repository to store and present the functional specifications of the devices are among the major challenges of the utilization of POC devices today.

3.1 Data Management and Integration The integration of Electronic Health Records (EHR) and results from POC devices can add value by providing a complete picture of patients’ health history and current condition, allowing healthcare providers to make more informed decisions about their care. Insurance companies are interested in such integration as they encourage the use of wearable devices through their reward programs. Health Information Exchange (HIE) platforms are crucial for the exchange of health information between different facilities. Such health information includes POC results data that are collected in home care settings and need to be transferred to clinics or other health organizations. The integration of patient data through wearable devices is a relatively new area of health technology, lacking the necessary platforms. Several challenges still remain concerning the progress of effective HIE systems in terms of transferring POC device data [74]. The major concerns include (i) the effective transfer of patient information between the clinic that prescribed home care and the hospital where the patient may need to be admitted, (ii) accurate and correct use of electronic medical records to transcribe POC results at home, (iii) the use of personal (daily) health records in combination with POC records, (iv) HIE network management and connectivity of POC devices, (v) data security and privacy, and (vi)

82

A. Zarnegar

the need to standardize data formats and protocols [27]. Magnocavallo et al. [55] reported that validation of technologies and data management strategies are among the challenges to be further studied and addressed in order for the utilization of remote patient monitoring systems to become systematic and widespread. According to the same study, further research is required to achieve more effective, continuous data collection strategies for such remote monitoring devices. There are several features that are required to make the integration of POC data with EHR practical. These features include plug-and-play standards of the POC device, device compatibility, streamlined scalability, and reconfigurability between different vendors [91]. Systems must be able to detect new devices, negotiate communication, and allow devices to synchronize and work with each other. In a study of home monitoring technologies [61], it was reported that work overload, lack of time, cumbersome procedures especially for ambient sensor systems and ECG, and fear of weakening of the relationship with older adults are barriers to implementing in-home sensors in aged care settings. A scoping review of studies related to the integration of various medical devices reported alarm fatigue and data overload as one of the main concerns and suggested integrating metadata and the use of AI to elevate this issue [88].

3.2 Transfer Protocols and Connectivity While rapid turnaround time is one of the major benefits of using POC devices, with these devices, however, there are known issues regarding loss of connectivity to a lab or a health information system especially when the devices run out of battery or are out of cellular range. Poor connectivity of POC devices can impede the effective management of patient health information and may negatively affect turnaround time and decision-making processes by physicians [26]. In another study of remote patient monitoring devices in professional settings, poor connectivity, along with large volumes of alerts and staffing, were the major concerns reported by health professionals [36]. The study found over 88% of the participants raised concerns with regard to managing the connectivity of remote monitoring devices. Although Erasmus et al. [26] propose some future strategies to enhance POC connectivity through the utilization of advanced artificial intelligence models and fast 5G network connectivity concepts, POC connectivity remains an ongoing challenge to be further addressed. Such issues demand further investigation and a risk assessment plan for POC devices.

3.3 Performance and Calibration Technologies that are used for medical diagnostic purposes by practitioners are usually prioritized on the basis of their diagnostic accuracy [63]. Turner et al. [82] found

Point-of-Care Devices in Healthcare: A Public Health Perspective

83

that one of the major barriers to adopting POC devices, from the perspective of practitioners, is the technical performance of POC devices and the diagnostic accuracy of such devices. Similarly, a separate survey in the Netherlands found that general practitioners believe the reliability of POC tests is one of the most important aspects of the utilization of these devices along with their impact on clinical management [17]. These findings were in line with another investigation into the important factors of POC adoption by Kip et al. [46] where it was found that the technical performance of POC tests ranked the second most important factor among 20 criteria by a cohort of 10 medical experts. The technical performance of the devices has been found to be dependent on the type of POC tests. A study by Alshaer et al. [6] summarized several significant alignments and clinical disagreements between POC results and lab results, especially in different types of blood tests. As one example, they refer to a previous work by Kanji et al. [41] in which it has been found that clinical agreement between the results of POC tests and those obtained through laboratory analysis is greater in central blood analysis than arterial blood analysis. Such differences in POC technical performances need to be identified and addressed before the widespread adoption of the technology in home care settings. An important aspect of assessing POCT is that POCTs have varying performances in terms of sensitivity and specificity [30]. Recognizing the potential impact of these test characteristics on false positive and false negative results and the need for confirmatory testing is essential. For example, in remote health monitoring systems, one drawback is that individual sensors must be regularly recalibrated, ensuring accuracy in the monitoring process [7, 58]. In a study of biosensors by Li et al. [52], it was mentioned that the need for calibration and recalibration of biosensor devices such as glucose monitors is one of the significant hurdles limiting their widespread use. It increases the complexity of sensors and their cost and introduces errors leading to inappropriate clinical intervention. In [34], POC devices were used to detect Octane for diagnostic of ARDS in an ICU setting successfully but the authors mentioned the biggest challenge in their study was the regular analysis of the calibration standards. A study of 541 health professionals by Yayan et al. [92] in 2020 revealed that 72% of healthcare professionals did not receive training on calibration. In addition, 40% were not aware of the presence of uncalibrated equipment in their units. There is ongoing research on calibration-free potentiometric sensors [11, 18, 71]. Calibration also raises the question of what errors can be tolerated in which application.

4 A Framework for POC Device Regulation Before discussing the proposed framework for POC device regulation, the assessment factors for the evaluation of POC devices are reviewed.

84

A. Zarnegar

4.1 POC Assessment Factors According to the study in [35], the following parameters are used for assessing wearable devices: ● Completeness: The number of domains (non-medical, disease management, general monitoring, or specific diagnosis) ● Continuity of service: Partial or continuous monitoring, power, current and battery runtime ● Cost-effectiveness: Billing and associated expenses of materials ● Convenience: Mode of wearability, compactness, and weight. Wrist-worn devices are more convenient ● Sampling rates: The number of measurements per second (higher sampling rates increase accuracy but require more power and storage) ● Resolution: The higher the resolution, the higher reliability and effectiveness. Lower resolution and the sensor’s coverage range limit the detection ● Data management: Data transmission (short range or long range) and data usage (propagation). Whether the device stores data internally or transfers it in a short range to a smartphone, short or long range to a computer, or long-range to a cloud The authoritative repository can help identify the sensitivity and specificity of devices as well as identify a device’s calibration information.

4.2 Lack of a Global Repository A systematic review of 83 POCT papers across several diseases indicated that for the evaluation of a POC test to be useful for primary care clinicians, future evaluations should not only focus on the technical performance aspects of a test but also report on the aspects relating to the clinical utility and risks [53]. Among those published studies, many did not report the manufacturer of the device and from those that reported the manufacturer information only a handful of companies were still operational. Additional bias was also reported as a majority of device evaluations were performed in primary care settings and few were conducted in secondary care. Engel and Wolffs [25] suggested strengthening alignment work between healthcare practitioners, innovators, and developers to improve coordination among intermediaries. Such coordination reduces duplicating efforts, identifies gaps and issues, and improves business models. Therapeutic Goods Administration (TGA) in Australia, Food and Drug Administration (FDA) in the US, and Conformité Européenne (EC) in Europe are the governing bodies for medical device certification and approval, which covers the certification and approval of POC devices too. The approval process requires that manufacturers submit certified manufacturing evidence and clinical studies to the governing bodies when approval is sought. TGA, EC, and FDA maintain a list of

Point-of-Care Devices in Healthcare: A Public Health Perspective

85

sponsors (persons or companies that are legally responsible for the product), device information, and their distribution, as well as the samples of these devices for evaluation. However, this information is not publicly available. Although there are studies such as that by Lenaerts et al. [51] which assess tools for the evaluation of the data generated by POC devices. Repositories of POC devices and their specifications are limited to a few marketplaces like Labcompare [50], and a standard validation framework is lacking in the domain.

4.3 Proposed Framework for POC Device Regulations The POC industry hardly has few standards and processes to address the versatile market of the devices and provide health professionals (and patients) with clear guidelines. Therefore, we believe having an online repository can provide the benefits mentioned above. Small startups and businesses will still be provided with the opportunity to remain active and productive since no big marketing budgets are required for such companies to attract attention. We suggest having an online repository where inventors, manufacturers, and healthcare practitioners can access and share information. Having such a repository will help health practitioners find quality information more efficiently. It will help balance the market so that a wide range of businesses (including those with smaller marketing budgets) can receive attention and provide their products and services. This will also enable the comparison of devices and access to calibration, maintenance information, and training guidelines increasing the quality of healthcare. Figure 1 illustrates the proposed framework and the flow of information for an online repository of POC devices and services. The proposed framework has been designed to address the need for transparency, standards, and evaluation measures of various POC devices. Three groups of stakeholders will be interacting with this repository: health practitioners, manufacturers, and the research community. These groups, along with the framework management, constitute the overall proposed fourcomponent framework for POC marketing and regulation. Manufacturers provide and upload POC documentation (including clinical trials, certification results, and marketing) to the repository, which will ensure that there will be sufficient information available for the POC devices that will be listed within the repository. This will provide visibility for the manufacturers of the devices regardless of their marketing budget. Using this framework, manufacturers can also assess and compare their products against other manufacturers’ devices easier in terms of the specifications and the market value of their products and direct their marketing channels to the lower or upper ends. The research community can use this repository to compare and survey products. Health professionals can browse the catalog and use search capabilities within the repository to find the most suitable product/s for a given use case. As a result of the transparency of POC specifications and the provision of manufacturer information,

86

A. Zarnegar

Fig. 1 The proposed framework for POC device comparison and regulation

health professionals can gather further information by contacting the manufacturer directly in case of any ambiguities. This platform also requires regulated management and supervision to evaluate each device to validate the legitimacy of the devices and manufacturers. Management of this repository involves manual checks to verify that uploaded POC documentation and information are valid and sufficient. This will also include contacting manufacturers for further clarification on any POC device that does not list minimal and necessary product information. After the manual verification process has been completed, the POC device can be included in the catalog, becoming accessible to health practitioners and the research community. In addition to the four main components of the proposed framework, in this platform, POC products can receive independent reviews from consumers; thus, health professionals can make more informed decisions and choices from a customer’s perspective too. Moreover, this framework will provide the base and an opportunity for finding the market gaps and will inspire innovation in the domain.

5 Conclusion This viewpoint discusses the benefits and challenges of using emerging Point-ofCare (POC) devices to improve public health provision and management. Emerging POC devices have the potential to disrupt public health by increasing patient engagement in (i) more effectively and efficiently managing their conditions, (ii) reducing inequalities in access to healthcare systems and devices caused by geographic or

Point-of-Care Devices in Healthcare: A Public Health Perspective

87

resource factors, (iii) the automated detection of undiagnosed conditions, and (iv) the provision of the healthcare system and the assembly of large datasets that can be analyzed to guide public health policy. However, for such public health benefits to be realized, POC devices need to be regulated more openly and transparently. A framework is proposed to provide POC information transparently for the comparison, maintenance, and best use of POC devices and to facilitate the inclusion of these devices in the healthcare system. The plan is to implement the framework and assess its benefits to the community of POC developers and practitioners using these devices in a follow-up study.

References 1. R. Abdolkhani, A. Borda, K. Gray, Quality management of patient generated health data in remote patient monitoring using medical wearables—a systematic review. Stud. Health Technol. Inform. 252, 1–7 (2018) 2. W.T. Abraham, L.W. Stevenson, R.C. Bourge, J.A. Lindenfeld, J.G. Bauman, P.B. Adamson, C.T.S. Group, Sustained efficacy of pulmonary artery pressure to guide adjustment of chronic heart failure therapy: complete follow-up results from the champion randomised trial. Lancet 387, 453–641 (2016). https://doi.org/10.1016/S0140-6736(15)00723-0 3. P. Adhikari, S. Pemminati, R. Pathak, M.S. Kotian, S. Ullal, Prevalence of hypertension in Boloor diabetes study (BDS-II) and its risk factors. J. Clin. Diagnos. Res. JCDR 9, IC01–IC04 (2015). https://doi.org/10.7860/JCDR/2015/16509.6781 4. P. Aggarwal, S. Galwankar, J. Nayer, Utility of point-of-care testing of natriuretic peptides (brain natriuretic peptide and n-terminal pro-brain natriuretic peptide) in the emergency department. Int. J. Crit. Illn. Inj. Sci. 4, 209 (2014). https://doi.org/10.4103/2229-5151.141406 5. A. Alanazi, Using machine learning for healthcare challenges and opportunities (2022). https:// doi.org/10.1016/j.imu.2022.100924 6. A. Alshaer, B.A. Badgheish, Z.H. Alsadah, K. Sewify, S. Alghazal, S. Alzahrani, A. Qadi, R. Alqahtani, G.A. Farsani, A. Shilash, Comparing the accuracy of point-of-care with laboratory (capillary, venous, and arterial) blood glucose levels in critically ill patients with and without shock. BMC Res. Notes 15(1), 372 (2022) 7. C. American Pathologists, Point-of-care-testing checklist CAP accreditation program (2021), www.cap.org. Accessed 09.22.2021 8. R. Anchala, N.K. Kannuri, H. Pant, H. Khan, O.H. Franco, E.D. Angelantonio, D. Prabhakaran, Hypertension in india: a systematic review and meta-analysis of prevalence, awareness, and control of hypertension (2014). https://doi.org/10.1097/HJH.0000000000000146 9. E. Andrès, L. Meyer, A.A. Zulfiqar, M. Hajjam, S. Talha, T. Bahougne, S. Ervé, J. Hajjam, J. Doucet, N. Jeandidier, A.H.E. Hassani, Telemonitoring in diabetes: evolution of concepts and technologies, with a focus on results of the more recent studies (2019). https://doi.org/10. 25122/jml-2019-0006 10. E. Archibong, K.R. Konnaiyan, H. Kaplan, A. Pyayt, A mobile phone-based approach to detection of hemolysis. Biosens. Bioelectron. 88, 204–209 (2017). https://doi.org/10.1016/j. bios.2016.08.030 11. E. Bakker, Can calibration-free sensors be realized? ACS Sensors 1, 838–841 (2016) 12. H. Banaee, M.U. Ahmed, A. Loutfi, Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges (2013). https://doi.org/10.3390/s131217472 13. F. Barbabella, M.G. Melchiorre, S. Quattrini, R. Papa, G. Lamura, European observatory policy briefs (2017) 14. S. Barello, S. Triberti, G. Graffigna, C. Libreri, S. Serino, J. Hibbard, R. Giuseppe, ehealth for patient engagement: a systematic review. Front. Psychol. (2016)

88

A. Zarnegar

15. D. Betts, R. Gordon, A.M. Egizi, C.B. Cruse, The future of the public’s health (2021), https:// www2.deloitte.com/us/en/insights/industry/health-care/the-future-of-public-health.html 16. Y. Bombard, G.R. Baker, E. Orlando, C. Fancott, P. Bhatia, S. Casalino, K. Onate, J.L. Denis, M.P. Pomey, Engaging patients to improve quality of care: a systematic review. Implement. Sci. 13, 98 (2018). https://doi.org/10.1186/s13012-018-0784-z 17. J.W.L. Cals, A.M.R. Schols, H.C.P.M. van Weert, F. Stevens, C.G.I.P. Zeijen, G. Holtman, W.A.M. Lucassen, M.Y. Berger, Point-of-care testing in family practices: present use and need for tests in the future. Ned. Tijdschr. Geneeskd. 158, A8210 (2014) 18. S. Campuzano, M. Pedrero, M. Gamella, V. Serafín, P. Yáñez-Sedeño, J.M. Pingarrón, Beyond sensitive and selective electrochemical biosensors: towards continuous, real-time, antibiofouling and calibration-free devices. Sensors (Basel) 20(12), 3376 (2020) 19. A.D. Cerbo, N. Quagliano, A. Napolitano, F. Pezzuto, T. Iannitti, A.D. Cerbo, Comparison between an emerging point-of-care tool for TSH evaluation and a centralized laboratory-based method in a cohort of patients from southern Italy. Diagnostics 11 (2021). https://doi.org/10. 3390/diagnostics11091590 20. CISCO: Cisco annual internet report (2018-2023) white paper (2020) 21. M. Clinic, Remote patient monitoring: comprehensive care at home (2021), https:// www.mayoclinichealthsystem.org/hometown-health/speaking-of-health/remote-patientmonitoring-comprehensive-care-at-home 22. P. Commission, Managed competition in health care (The Commission, 2002), p. 97 23. G.L. Damhorst, E.A. Tyburski, O. Brand, G.S. Martin, W.A. Lam, Diagnosis of acute serious illness: the role of point-of-care technologies. Curr. Opin. Biomed. Eng. 11, 22–34 (2019) 24. C. Doherty, C. Stavropoulou, Patients’ willingness and ability to participate actively in the reduction of clinical errors: a systematic literature review. Soc. Sci. Med. 75, 257–263 (2012). https://doi.org/10.1016/j.socscimed.2012.02.056 25. N. Engel, P. Wolffs, Aligning diagnostics to the point-of-care: lessons for innovators, evaluators and decision-makers from tuberculosis and HIV. BMJ Global Health 5 (2020). https://doi.org/ 10.1136/bmjgh-2020-003457 26. R. Erasmus, S. Sahni, R. El-Sharkawy, Connectivity strategies in managing a POCT service. EJIFCC 32(2), 190–194 (2021) 27. J. Espinoza, N.Y. Xu, K.T. Nguyen, D.C. Klonoff, The need for data standards and implementation policies to integrate CGM data into the electronic health record. J. Diabetes Sci. Technol. 17(2), 495–502 (2023) 28. G. Fagherazzi, P. Ravaud, Digital diabetes: perspectives for diabetes prevention, management and research. Diabetes Metab. 45, 322–329 (2019). https://doi.org/10.1016/J.DIABET.2018. 08.012 29. V. Foerster, M. Severn, Point-of-care glycated hemoglobin testing to diagnose type 2 diabetes (2017) 30. A. Ghaffari, R. Meurant, A. Ardakani, COVID-19 point-of-care diagnostics that satisfy global target product profiles. Diagnostics 11 (2021). https://doi.org/10.3390/diagnostics11010115 31. M. Ghassemi, T. Naumann, P. Schulam, A.L. Beam, I.Y. Chen, R. Ranganath, A review of challenges and opportunities in machine learning for health, in AMIA Joint Summits on Translational Science (2020) 32. A. Gialamas, L.N. Yelland, P. Ryan, K. Willson, C.O. Laurence, T.K. Bubner, P. Tideman, J.J. Beilby, Does point-of-care testing lead to the same or better adherence to medication? A randomised controlled trial: the poct in general practice trial. Med. J. Aust. 191, 487–491 (2009). https://doi.org/10.5694/j.1326-5377.2009.tb02910.x 33. G. Graffigna, S. Barello, Spotlight on the patient health engagement model (PHE model): a psychosocial theory to understand people’s meaningful engagement in their own health care. Patient Prefer Adherence 12, 1261–1271 (2018). https://doi.org/10.2147/ppa.S145646 34. L.A. Hagens, A.R.M. Verschueren, A. Lammers, N.F.L. Heijnen, M.R. Smit, T.M.E. Nijsen, I. Geven, M.J. Schultz, D.C.J.J. Bergmans, R.M. Schnabel, L.D.J. Bos, Development and validation of a point-of-care breath test for octane detection. Analyst 146(14), 4605–4614 (2021)

Point-of-Care Devices in Healthcare: A Public Health Perspective

89

35. M. Haghi, S. Danyali, S. Ayasseh, J. Wang, R. Aazami, T.M. Deserno, Wearable devices in health monitoring from the environmental towards multiple domains: A survey. Sensors 21, 1–24 (2021). https://doi.org/10.3390/s21062130, cited by: 3 Export Date: 30 October 2021 36. M. Harvey, A. Seiler, Challenges in managing a remote monitoring device clinic. Heart Rhythm O2 3(1), 3–7 (2022) 37. Z. Hosseini, S.J. Whiting, H. Vatanparast, Type 2 diabetes prevalence among Canadian adults— dietary habits and sociodemographic risk factors. Appl. Physiol. Nutr. Metab. 44, 1099–1104 (2019). https://doi.org/10.1139/apnm-2018-0567 38. M.P. Hudson, R.H. Christenson, L.K. Newby, A.L. Kaplan, E.M. Ohman, Cardiac markers: point of care testing (1999) 39. P. Jiang, J. Winkley, C. Zhao, R. Munnoch, G. Min, L.T. Yang, An intelligent information forwarder for healthcare big data systems with distributed wearable sensors. IEEE Syst. J. 10(3), 1147–1159 (2016). https://doi.org/10.1109/JSYST.2014.2308324 40. P. Jørgensen, A. Langhammer, S. Krokstad, S. Forsmo, Mortality in persons with undetected and diagnosed hypertension, type 2 diabetes, and hypothyroidism, compared with persons without corresponding disease—a prospective cohort study; the HUNT Study, Norway. BMC Fam. Pract. 18, 98 (2017). https://doi.org/10.1186/s12875-017-0672-7 41. S. Kanji, J. Buffie, B. Hutton, P.S. Bunting, A. Singh, K. McDonald, D. Fergusson, L.A. McIntyre, P.C. Hebert, Reliability of point-of-care testing for glucose measurement in critically ill adults. Crit. Care Med. 33(12), 2778–2785 (2005) 42. S.H. Kaplan, S. Greenfield, J.E. Ware, Assessing the effects of physician-patient interactions on the outcomes of chronic disease. Med. Care 27(3 Suppl), S110–S127 (1989) 43. D. Kapoor, P. Singh, M. Srivastava, Point of care blood gases with electrolytes and lactates in adult emergencies. Int. J. Crit. Illn. Inj. Sci. 4, 216 (2014). https://doi.org/10.4103/2229-5151. 141411 44. C.J. Kelly, A. Karthikesalingam, M. Suleyman, G. Corrado, D. King, Key challenges for delivering clinical impact with artificial intelligence (2019). https://doi.org/10.1186/s12916-0191426-2 45. S. Kennedy, How big data analytics can support preventive health (2022), https:// healthitanalytics.com/features/how-big-data-analytics-can-support-preventive-health 46. M.M.A. Kip, J.M. Hummel, E.B. Eppink, H. Koffijberg, R.M. Hopstaken, M.J. IJzerman, R. Kusters, Understanding the adoption and use of point-of-care tests in Dutch general practices using multi-criteria decision analysis. BMC Fam. Pract. 20(1), 8 (2019) 47. A.N. Konwar, V. Borse, Current status of point-of-care diagnostic devices in the Indian healthcare system with an update on Covid-19 pandemic (2020). https://doi.org/10.1016/j.sintl.2020. 100015 48. S. Kumar, M. Nehra, S. Khurana, N. Dilbaghi, V. Kumar, A. Kaushik, K.H. Kim, Aspects of point-of-care diagnostics for personalized health wellness (2021). https://doi.org/10.2147/IJN. S267212 49. D. Kuupiel, V. Bawontuo, P.K. Drain, N. Gwala, T.P. Mashamba-Thompson, Supply chain management and accessibility to point-of-care testing in resource-limited settings: a systematic scoping review. BMC Health Services Res. 19, 519 (2019). https://doi.org/10.1186/s12913019-4351-3 50. Labcompare: Point of care devices (POC diagnostic devices) (2022), https://www.labcompare. com/Clinical-Diagnostics/5096-POC-Diagnostic-Devices/ 51. G. Lenaerts, G.E. Bekkering, M. Goossens, L. De Coninck, N. Delvaux, S. Cordyn, J. Adriaenssens, P. Vankrunkelsven, Tools to assess the trustworthiness of evidence-based point-ofcare information for health care professionals: systematic review. J. Med. Internet Res. 22(1), e15415 (2020) 52. H. Li, P. Dauphin-Ducharme, G. Ortega, K.W. Plaxco, Calibration-free electrochemical biosensors supporting accurate molecular measurements directly in undiluted whole blood. J. Am. Chem. Soc. 139(32), 11207–11213 (2017) 53. D. Lingervelder, H. Koffijberg, R. Kusters, M.J. Ijzerman, Point-of-care testing in primary care: a systematic review on implementation aspects addressed in test evaluations. Int. J. Clin. Pract. 73 (2019). https://doi.org/10.1111/ijcp.13392

90

A. Zarnegar

54. F. Liu, P. Demosthenes, Real-world data: a brief review of the methods, applications, challenges and opportunities (2022). https://doi.org/10.1186/s12874-022-01768-6 55. M. Magnocavallo, G. Vetta, A. Bernardini, A. Piro, M.C. Mei, M.D. Iorio, M.V. Mariani, D.G.D. Rocca, P. Severino, R. Quaglione, G. Giunta, C. Chimenti, F. Miraldi, C.D. Vizza, F. Fedele, C. Lavalle, Impact of COVID-19 pandemic on cardiac electronic device management and role of remote monitoring (2022). https://doi.org/10.1016/j.ccep.2021.10.010 56. A. Manocha, S. Bhargava, Emerging challenges in point-of-care testing. Curr. Med. Res. Pract. (2019) 57. J. McKeon, Ai in healthcare poses algorithmic bias, data privacy, security risks (2022), https:// healthitanalytics.com/news/biases-in-artificial-intelligence-led-to58. M.P. Mcnerney, Y. Zhang, P. Steppe, A.D. Silverman, M.C. Jewett, M.P. Styczynski, Pointof-care biomarker quantification enabled by sample-specific calibration. Sci. Adv. 5 (2019), https://www.science.org 59. MEDTEK: 5 important benefits of point-of-care testing (poct) (2022), https://medtek.com.ph/ 2022/06/03/5 60. B.B. Nunna, D. Mandal, S. Zhuang, E.S. Lee, A standalone micro biochip to monitor the cancer progression by measuring cancer antigens as a point-of-care (POC) device for enhanced cancer management, pp. 212–215 (2017). https://doi.org/10.1109/HIC.2017.8227622 61. B. Pais, P. Buluschek, G. DuPasquier, T. Nef, N. Schütz, H. Saner, D. Gatica-Perez, V. Santschi, Evaluation of 1-year in-home monitoring technology by home-dwelling older adults, family caregivers, and nurses. Front. Public Health 8 (2020). https://doi.org/10.3389/fpubh.2020. 518957 62. M.L. Parker, P.M. Yip, L.V. DeCherrie, C. Escobar, A.K. Füzéry, C.P. Price, A. St John, There’s no place like home: exploring home-based, acute-level healthcare. Clin. Chem. 64(8), 1136– 1142 (2018). https://doi.org/10.1373/clinchem.2017.283093 63. A. Plüddemann, C. Heneghan, M. Thompson, N. Roberts, N. Summerton, L. Linden-Phillips, C. Packer, C.P. Price, Prioritisation criteria for the selection of new diagnostic technologies for evaluation. BMC Health Serv. Res. 10(1), 109 (2010) 64. A.S. Poncette, L. Mosch, C. Spies, M. Schmieding, F. Schiefenhövel, H. Krampe, F. Balzer, Improvements in patient monitoring in the intensive care unit: survey study. J. Med. Internet Res. 22, e19091 (2020). https://doi.org/10.2196/19091 65. T. Poongodi, R. Krishnamurthi, R. Indrakumari, P. Suresh, B. Balusamy, Wearable Devices and IoT (Springer International Publishing, Cham, 2020), pp. 245–273. https://doi.org/10. 1007/978-3-030-23983-1_10 66. C. Public, Cisco annual internet report (2018) 67. C.C. Quinn, E.C. Butler, K.K. Swasey, M.D. Shardell, M.D. Terrin, E.A. Barr, A.L. GruberBaldini, Mobile diabetes intervention study of patient engagement and impact on blood glucose: mixed methods analysis. JMIR mHealth uHealth 6, e31.2 (2018). https://doi.org/10.2196/ mhealth.9265 68. R. Rajendran, G. Rayman, Point-of-care blood glucose testing for diabetes care in hospitalized patients: an evidence-based review (2014). https://doi.org/10.1177/1932296814538940 69. M.I. Razzak, M. Imran, G. Xu, Big data analytics for preventive medicine. Neural Comput. Appl. 32, 4417–4451 (2020). https://doi.org/10.1007/s00521-019-04095-y 70. Research, Markets: Global point of care diagnostics/testing market size, share & trends analysis report, 2019-2025—researchandmarkets.com (2019), https://search. ebscohost.com/login.aspx?direct=true&AuthType=sso&db=bwh&AN=bizwire.c88717437& site=eds-live&scope=site&custid=s2775460, business Wire (English) Accession Number: bizwire.c88717437; Issue Information; Place of Event: DUBLIN; Document Type: Article 71. C.R. Rousseau, P. Bühlmann, Calibration-free potentiometric sensing with solid-contact ionselective electrodes. TrAC Trends Anal. Chem. 140, 116277 (2021). https://doi.org/10.1016/ j.trac.2021.116277; www.sciencedirect.com/science/article/pii/S016599362100100X 72. A.H. Sapci, H.A. Sapci, Innovative assisted living tools, remote monitoring technologies, artificial intelligence-driven solutions, and robotic systems for aging societies: systematic review. JMIR Aging 2(2), e15429 (2019). https://doi.org/10.2196/15429

Point-of-Care Devices in Healthcare: A Public Health Perspective

91

73. M. Shephard, A. Shephard, S. Matthews, K. Andrewartha, The benefits and challenges of pointof-care testing in rural and remote primary care settings in Australia. Arch. Pathol. Lab. Med. 144 (2020). https://doi.org/10.5858/arpa.2020-0105-RA 74. N. Shimizu, K. Kotani, Health information exchange in relation to point-of-care testing in home care: issues in Japan. Clin. Chim. Acta 532, 10–12 (2022). https://doi.org/10.1016/j.cca.2022. 05.013 75. B. Sorensen, S. Hunskaar, Point-of-care ultrasound in primary care: a systematic review of generalist performed point-of-care ultrasound in unselected populations. Ultrasound J. 11(1), 31 (2019) 76. R. Steinberg, B. Anderson, Z. Hu, T.M. Johnson, J.B. O’Keefe, L.C. Plantinga, R. Kamaleswaran, B. Anderson, Associations between remote patient monitoring programme responsiveness and clinical outcomes for patients with COVID-19. BMJ Open Qual. 10 (2021). https://doi.org/10.1136/bmjoq-2021-001496 77. M. Stevenson, A situation analysis of the state of supply of in vitro diagnostics in low-income countries. Glob. Public Health 15, 1836–1846 (2020). https://doi.org/10.1080/17441692.2020. 1801791 78. A. Stranieri, S. Venkatraman, J. Minicz, A. Zarnegar, S. Firmin, V. Balasubramanian, H. Jelinek, Emerging point of care devices and artificial intelligence: prospects and challenges for universal health risk assessment. Under review (2021) 79. E.K.W. Tan, Y.Z. Au, G.K. Moghaddam, L.G. Occhipinti, C.R. Lowe, Towards closed-loop integration of point-of-care technologies. Trends Biotechnol. 37(7), 775–788 (2019) 80. S. Thukral, S. Vedantham, Clinical medicine catheter-based therapies and other management strategies for deep vein thrombosis and post-thrombotic syndrome. https://doi.org/10.3390/ jcm9051439 81. S. Tian, W. Yang, J.M.L. Grange, P. Wang, W. Huang, Z. Ye, Smart healthcare: making medical care more intelligent. J. Glob. Health 3, 62–65 (2019). https://doi.org/10.1016/j.glohj.2019.07. 001 82. P.J. Turner, A. Van den Bruel, C.H.D. Jones, A. Plüddemann, C. Heneghan, M.J. Thompson, C.P. Price, J. Howick, Point-of-care testing in UK primary care: a survey to establish clinical needs. Fam. Pract. 33(4), 388–394 (2016) 83. J. Vespa, L. Medina, D.M. Armstrong, Demographic turning points for the united states: population projections for 2020 to 2060 population estimates and projections current population reports (2020), www.census.gov/programs-surveys/popproj 84. C. Wang, M. Liu, Z. Wang, S. Li, Y. Deng, N. He, Point-of-care diagnostics for infectious diseases: from methods to devices (2021). https://doi.org/10.1016/j.nantod.2021.101092 85. C.F. Weber, K. Zacharowski, Perioperative point-of-care-Gerinnungsdiagnostik. Deutsches Arzteblatt Int. 109 (2012). https://doi.org/10.3238/arztebl.2012.0369 86. M.A. Weigelt, R. Sivamani, H. Lev-Tov, The therapeutic potential of cannabinoids for integumentary wound management. Exp. Dermatol. 30, 201–211 (2021). https://doi.org/10.1111/ exd.14241 87. D. Weiss, H.T. Rydland, E. Øversveen, M.R. Jensen, S. Solhaug, S. Krokstad, Innovative technologies and social inequalities in health: a scoping review of the literature. PLoS One 13, e0195447–e0195447 (2018). https://doi.org/10.1371/journal.pone.0195447 88. J.B. Withall, J.M. Schwartz, J. Usseglio, K.D. Cato, A scoping review of integrated medical devices and clinical decision support in the acute care setting. Appl. Clin. Inform. 13(5), 1223– 1236 (2022) 89. H.Y. Wong, L.G. Marcu, E. Bezak, N.A. Parange, Review of health economics of point-ofcare testing worldwide and its efficacy of implementation in the primary health care setting in remote Australia. Risk Manag. Healthc. Policy 13, 379–386 (2020). https://doi.org/10.2147/ rmhp.s247774 90. World Health Organization, More than 700 million people with untreated hypertension (2021), https://www.who.int/news/item/25-08-2021-more-than-700-million-peoplewith-untreated-hypertension

92

A. Zarnegar

91. J. Yao, R. Schmitz, S. Warren, A wearable point-of-care system for home use that incorporates plug-and-play and wireless standards. IEEE Trans. Inf. Technol. Biomed. 9(3), 363–371 (2005) 92. E.H. Yayan, M. Zengin, A key point in medical measurements: device calibration and knowledge level of healthcare professionals. Int. J. Car. Sci. 13(2), 1346–1354 (2020) 93. A. Yu, The value of POCT (Point of Care Technology) investing in 2020 (2020), https://www. linkedin.com/pulse/value-poct-point-care-technology-investing-2020-andy-yu/ 94. E. Øversveen, Stratified users and technologies of empowerment: theorising social inequalities in the use and perception of diabetes self-management technologies. Sociol. Health Illn. 42, 862–876 (2020). https://doi.org/10.1111/1467-9566.13066

Digital Platforms to Support Feedback Processing in Aged Care Homes: Friend or Foe? Tanya Linden and Rosemary Fisher

1 Introduction Providing feedback on products and services has become a common practice in modern times, as companies analyse this feedback and take action to improve their offerings. Similarly, in healthcare, feedback has been increasingly utilised to develop quality improvement strategies aimed at enhancing patient care. In Australia, aged care homes are one area in urgent need of quality improvement. Aged care homes (ACHs), also known as nursing homes or residential aged care facilities, provide accommodation, meals, and various levels of support from health professionals to meet the individual needs of residents. By collecting feedback from residents, their families, and staff, ACHs can identify areas for improvement and implement changes that enhance the quality of care provided. In recent years, there has been a surge in reports of substandard care and abuse in ACHs in Australia. According to the Royal Commission into Aged Care Quality and Safety, in 2019–2020 there were 5,718 reported cases of such incidents made by patients, their family members, and healthcare workers who witnessed the events. While collecting feedback is crucial to identifying problem areas, it is not enough unless it leads to corrective actions. To understand their shortcomings, causes of substandard care, and improve their services, healthcare providers need an effective feedback processing system [4]. Without such a system, complaints may not be addressed effectively, if at all, and the quality of care may suffer. T. Linden (B) School of Computing and Information Systems, Faculty of Engineering and IT, University of Melbourne, Melbourne, Australia e-mail: [email protected] R. Fisher School of Business, Swinburne University of Technology, Technology and Entrepreneurship, Melbourne, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Daimi et al. (eds.), Current and Future Trends in Health and Medical Informatics, Studies in Computational Intelligence 1112, https://doi.org/10.1007/978-3-031-42112-9_5

93

94

T. Linden and R. Fisher

In Australia, the Aged Care Quality and Safety Commission (ACQ&SC) has developed eight quality standards and accompanying measures for evaluation aimed at improving the quality of care provided in ACHs. Standard 6 requires ACHs to implement a system to collect and resolve feedback and complaints from consumers about their service, adjust their practices based on the review of that feedback, all with a view to continuous improvement of services provided [1]. Implementing Standard 6 enables ACHs to continuously improve their services by establishing an ongoing feedback loop. This helps to identify areas requiring improvement and address concerns raised by patients and their families, leading to enhancements in the quality of care provided. Additionally, ACHs that implement Standard 6 demonstrate their commitment to providing person-centred care that meets the needs and preferences of their residents. Numerous studies in healthcare have compared paper-based data collection with electronic data collection using handheld devices, including mobile phones [27]. These studies have shown that pen-and-paper data collection results in inaccurate data, takes longer to transfer data from entry points to the analysis point, and is more labour-intensive than electronic data collection. In addition, many patients and users prefer electronic devices for data collection. However, some patient groups, particularly those belonging to the older generation, still prefer paper-based methods due to their unfamiliarity with electronic devices, cognitive or physical limitations, or personal preferences [30, 33, 34]. This preference is especially relevant in the context of ACHs. Regardless of the method used to collect feedback data, electronic data analysis is essential to ensure the accuracy and reliability of results. The Royal Commission into Aged Care Quality and Safety has therefore recommended that aged care providers invest in information and communication technology (ICT) to enhance data analysis and provide timely and streamlined access to information. Digital tools, such as mobile apps or web-based surveys, can overcome some of the challenges associated with traditional paper-based feedback systems. By exploring the use of digital feedback systems, ACHs may make feedback collection more accessible and convenient for residents, and allow for more efficient collection and analysis of feedback data. Moreover, digital feedback systems can enable real-time responses to feedback and are likely to promote a culture of continuous quality improvement in ACHs. Although there has been extensive research on collecting patient feedback in healthcare, there is a gap in the context of collecting digital feedback in aged care settings. Therefore, the aim of this study is to investigate the effects of computerising feedback collection and processing in ACHs as well as to determine whether this will have a positive impact on improving quality of care and satisfaction of staff and residents. Specifically we set forth the following objectives: 1. Investigate the effectiveness of a digital tool in collecting ACH resident feedback, 2. Identify the functions and features of digital tools that contribute to job efficiency for staff,

Digital Platforms to Support Feedback Processing in Aged Care Homes …

95

3. Evaluate how using such a tool can contribute towards improving the quality of care provision and resident satisfaction. Tell Touch (telltouch.com), is a digital platform specifically designed for capturing feedback and complaints from ACH residents allowing the identification of potential issues before they escalate. Tell Touch is a digital platform that consists of an easyto-use app for residents and families, and an application for staff with a back-end processing system that allows ACH staff to respond to feedback in real-time and generate reports that comply with the Aged Care Quality and Safety Standard 6. Tell Touch developers claim that the use of their digital platform offers a promising solution to the challenges of ACH feedback related procedures, fosters a culture of continuous quality improvement, and ensures that services meet the needs and expectations of residents and their families.

2 Background Collecting feedback to evaluate health services is not a new concept; patient feedback and complaints are considered important for improving patient care services [2]. Although questionnaires are the most commonly used method for collecting feedback in healthcare settings the effectiveness and impact of survey results depends on the purpose of questionnaires and the interpretation of responses [12, 16, 19]. One of the most frequent uses of patient feedback in healthcare is to evaluate the performance of individual practitioners and healthcare institutions [16, 37]. Feedback has the potential to facilitate improvements in care practices, particularly when there is a mechanism in place to intervene based on the feedback provided [6, 17]. In certain cases, service funding and funding amounts may be contingent on improvements reported through patient feedback [35, 36]. Administering patient satisfaction questionnaires and processing collected data could be challenging, and if not done adequately, could contribute to negative patient experiences. However, patient feedback is a valid indicator of healthcare services quality [38]. The satisfaction or dissatisfaction of residents can assist providers in identifying areas of service that require attention and improvement from the perspective of those receiving care [11]. In the 1990s in the UK several quality assurance frameworks and auditing packages were developed for nursing homes [8], however the majority failed to incorporate the collection of resident views in their design making it unlikely for aged care homes to collect this important data [5]. A similar lack of consultation with consumers was observed in Australia [10]. The Department of Health and Family Services addressed this shortcoming by introducing a system where aged care homes were evaluated on a set of standards and their accreditation was dependent on demonstrating that they had sought and acted upon resident feedback [15]. Collecting patient feedback has inherent challenges, and in aged care settings additional issues may arise due to the frail physical and mental condition of many

96

T. Linden and R. Fisher

residents [5]. Both technology aspects and resident health aspects such as a lack of technical literacy, mistrust of the Internet, computer anxiety, visual impairment, hand–eye coordination problems, or limited mental capacity can act as barriers to providing feedback by residents themselves [24, 26]. Previous research indicates that the quality of feedback is not affected by the method of delivery, whether it is provided through paper-based forms or Internetbased technology [31]. However, feedback provided electronically is immediately available for processing and necessary action may be taken faster, whereas collecting paper-based feedback adds to care staff workload. Nurses are responsible for transferring information, which can be time-consuming and inefficient due to suboptimal processes, such as transcribing paper notes into a digital format for system input [18]. At the same time implementing technology in ACHs does not improve quality of care unless there is substantial investment in reliable infrastructure and staff training and support during the technology adoption period [25]. Additional barriers to feedback collection and use for improvement include the timing of feedback and a lack of details or relevance in feedback to allow correct reaction [19]. At the same time residents and family members may be wary of providing negative feedback in fear of repercussions [9]. A small number of past studies evaluated the effect of digital technology used in ACHs on resident quality of care and resident concerns [3]. Most studies report on staff satisfaction with technology, its usability and its usefulness for staff performing tasks as opposed to how technology improves resident-specific outcomes. Subsequently these past studies focus on staff perspectives when examining barriers to technology adoption in ACHs. So it is imperative to study the impact of technology on the well-being of residents. We observe that in practice questionnaires are commonly used to evaluate the quality of health services; yet valuable feedback often comes in the form of free-text praise or complaints. To facilitate this type of feedback, Tell Touch allows users to quickly select a service category (e.g., food, cleaning, care), express their attitude towards the service using emojis and then provide details in the form of free-text feedback. Given these features and their use in many ACHs, Tell Touch was selected as the platform to study the effectiveness of collecting and acting on feedback in this context.

3 Methodology This exploratory study aims to investigate the effectiveness of a digital platform for feedback collection and processing in ACH settings. To achieve this, the Tell Touch platform was selected and residential aged care facilities that had implemented this platform were contacted for participation. Eight (8) ACHs agreed to participate in the study. As one of the aims of this study is to assess the effectiveness of a digital platform for collecting and processing feedback in ACHs, we adopted the Technology

Digital Platforms to Support Feedback Processing in Aged Care Homes …

97

Acceptance Model (TAM) as a foundation for our data collection. TAM measures self-reported acceptance of technology by individuals based on three main parameters: user satisfaction, perceived usefulness and perceived ease of use [13, 14]. TAM has been used to measure IT acceptance in various industries including health-related fields, and has been found to be suitable for this purpose in a range of healthcare settings [23]. To evaluate user satisfaction and perceived ease of use, we adapted the TAM questions of Holden et al.. Participants were requested to indicate their agreement with each statement using a 7-point Likert scale. To gain further insights into how Tell Touch meets management needs in ACHs, we asked participants to elaborate on their responses in a 45-min interview. The interview recordings were transcribed and analysed by applying the thematic analysis method [7].

4 Data Analysis 4.1 Participants Six (6) ACHs located in Victoria agreed to participate in this study, providing a potential pool of 39 users of Tell Touch. Of these, eight (8) volunteered to participate in this study. Of the participants all were female and aged over 40 years, seven (87.5%) reported English as their primary language, five (62.5%) reported Australian ethnicity with the remaining three reporting as Chinese, Iranian, and Indian. Six respondents (75%) have lived in Australia and worked in ACHs for more than ten years. Participants had a range of professional titles: ACH manager (n = 3, 37.5%), divisional therapist (n = 1, 12.5%), registered nurse (n = 1, 12.5%), national quality business partner (n = 1, 12.5%) and other (n = 1, 12.5%). Participants were employed by: church-owned (n = 4, 50%) and privately-owned (n = 4, 50%) ACHs. The ACHs locations were reported as suburbs (n = 3, 37.5%), Victoria country (n = 3, 37.5%), central business district CBD (n = 1, 12.5%), and other (n = 1, 12.5%).

4.2 Applying Thematic Analysis Thematic analysis of audio recorded data was undertaken following the guidelines recommended by Braun and Clarke with additional checks for rigour and trustworthiness as recommended by Nowell et al. [29]. To ensure the reliability and validity of the qualitative data analysis, two research assistants independently transcribed the data. The transcriptions were imported into

98

T. Linden and R. Fisher

NVivo, software supporting qualitative data analysis, where each researcher independently read and coded the data. These codes were then categorised into sub-themes, which were subsequently connected to emerging overarching themes. Coding results were discussed and compared until both researchers reached a shared understanding of the themes and agreed on the naming of each theme. To ensure that the interpretation of the coding and themes held, the original raw data was reviewed several days later. During this review process, some themes were renamed and others were collapsed into other themes until the final results satisfied both researchers. By involving two independent coders in the data analysis process, this study aims to enhance the credibility of findings and achieve greater accuracy and consistency in the discovery and interpretation of themes. (To respect the voice of each participant, quotations are unedited.) The Technology Acceptance Model (TAM) focuses on three key aspects of usertechnology relations: user satisfaction with technology, perceived usefulness, and perceived ease of use. To evaluate perceived ease of use, we asked participants direct questions regarding the system’s ease of use, including whether the system was easy to learn, clear, and understandable. Figure 1 depicts the y-axis as number of agreements with each statement (Q1-Q3 depicted as red, blue and green columns respectively) and the x-axis as strength of agreement (in our data the participants indicated stronger agreement by selecting options between 4 and 7 on the Likert scale). All participants indicated that the system was clear and understandable, with half of the participants selecting 7 (indicating they agreed with the statement “a great deal”), and the remaining participants selecting 5 or 6 on the 7-point Likert scale. Similarly, when asked the extent to which they found the system easy to use, 62.5%

Fig. 1 Responses to questions regarding platform user-friendliness

Digital Platforms to Support Feedback Processing in Aged Care Homes …

99

of participants selected 6 or 7, while one person selected 4 and two participants selected 5. Participants were split 50/50 on the extent to which they found the system easy to learn, with 50% of participants rating it over 6 and the other 50% rating it at 4 or 5. Ease of use was similarly rated; with 62% rating ease of use at 6 or 7, and 38% rating it at 4 or 5. Participants elaborated on user-friendliness aspects of Tell Touch as follows: quite user friendly system”, “it’s easy to use”, “it’s quite simple to use”, “It’s very positive and I’m really happy with using this platform”, “my experience have been really good with Tell Touch.

To probe perceived usefulness, questions asked about the extent to which use of Tell Touch increased job productivity, improved respondent’s performance in their job, and enhanced respondent’s effectiveness on the job. Responses were mixed. Three respondents (38%) had positive views rating the statements as 5, 6 and 7. Two respondents rated these statements low at 2 and 3, and three respondents selected the midpoint of 4 (38%) suggesting no strong opinion either way. In one of the interviews one participant stated explicitly that Tell Touch met a need. I found it uumm quite time saving too.

Multiple features of Tell Touch were identified as very useful, one such feature being the email alerts: When someone puts in a feedback, it actually sends me an e-mail to alert me that there’s feedback has been put in and so that’s what I think it’s just brings to my attention immediately. … And then if I there’s a delay it does remind me that there’s a feedback that you have to act on. It’s still open. So that’s what I like about this feature. So you cannot actually miss a feedback.

Seven participants commented on the usefulness of the reporting feature in Tell Touch. Participants found the reports useful for various purposes, including accreditation, identifying problems by category, and facilitating improvement action. It’s really good to run reports because you can go as far as you, you know, I mean year, two years. So very good way of summarizing your data … easy to run reports and see which areas are of main concern; easy to identify trends which probably is required for accreditation purposes. So that’s easy to look into the areas umm which require attention because it’s quite specific. If it’s food related, if it’s care or cleaning or communication related. So it’s differentiated into different categories. So at the end of the month you look at the reports, then you raise your action plan and then you can put an improvement plan as required.

Another feature considered particularly helpful for effective job performance was reminder emails about feedback that had not been actioned and thus needed attention. The ability to easily specify satisfaction or dissatisfaction with the service, and categorise feedback and complaints into service areas (see Fig. 2 and Fig. 3

100

T. Linden and R. Fisher

depicting some of the available reports), streamlined the feedback collection process and facilitated faster processing: Different categories. So there’s mainly four categories. … Tell Touch captures as food, cleaning, care, communication, so I can actually differentiate… So depending on if it’s negative or positive and so that’s what I like about that.

One participant highlighted the ease of sharing system access via a QR code as a noteworthy feature: give them some QR code and... It’s really handy just to take a picture and go through the link and leave us feedback and comments.

To identify specific aspects in which Tell Touch could be useful, the study included a question asking participants to evaluate the extent to which using the platform had improved their interaction with residents and their families. The majority of participants (63%) rated it 4 or higher, with 2 rating it at 6. Three participants rated it from

Fig. 2 Report summarising feedback submissions

Fig. 3 Screenshot of a report summarising feedback by category

Digital Platforms to Support Feedback Processing in Aged Care Homes …

101

1 to 3, indicating they did not believe the system provided any improvement in their interactions with residents or family members. Those who reported improvement in interactions with residents and families described system features that made it easier: The faces, smiley and angry faces. That’s really easy for them to understand and express their feelings about the question I’m asking them. The language. Because we are in a multicultural age care here and sometimes it’s really hard for them to understand even with sign language or using body language. So, I just changed it to their language. This is really, really helpful.

Some interviewees noted a missing feature that would support better interaction with residents and their families, namely the ability to provide follow-up details through Tell Touch. Usually after getting their feedback, I try to understand and send an email to the person who is in charge and send the solution to the residents who made the feedback, but I think this is one-way interaction. I’ve got two or three residents who are really good at using apps and they usually send their feedback without me. But even in those cases, I am not able to have communication with them through Tell Touch, I need to go to their room and have a discussion with them.

Respondents confirmed that feedback helps improve services for residents. They make it easy because I receive first-hand feedback and then I’ll just transfer it to the people in charge and they always try to take it into action and then change it in the way that’s the best.

Timely feedback and response are crucial in deescalating potentially volatile situations between staff and residents or between residents, by allowing for peaceful resolution through prompt action. The prompt action and resolution typically yields fast results and ensures all parties feel heard, thereby reducing the likelihood of complaints to authorities. There are queries or concerns they have so they will bring it to our attention rather than taking it to Commission. To listen and acknowledge, that’s quite important It helps to not escalate the issues and the problems. Because with these kinds of things, we just try to take action as soon as possible.

As the study evaluated a digital feedback platform in environments where users could have physical and mental challenges in using it, participants were asked to discuss how residents provided feedback. The responses ranged from feedback being provided on paper and then entered into the system by staff, to a small number of computer-literate residents providing feedback via their own tablets and the app: Our residents, they do prefer paper, unless I’ve got quite a few residents who can, who got an access to an iPad computer. 50% of the elderly population still struggle to use the simple iPad functionality of Tell Touch.

102

T. Linden and R. Fisher

Residents if they have to write, they do write the paper version form. Staff does help residents to put their feedback in if they can’t do it themselves. If they need assistance, staff does assist them. And if they do tell staff that there’s a feedback, staff usually get an iPad and they just put it on Tell Touch straight away. But if a resident prefers to write, they use a paper version form.

During the study, it was noted that representatives of residents always enter feedback directly into the Tell Touch system. This ensures that even in situations where paper feedback is unavoidable, the processing is still done by the system, which speeds up processing, ensures that no feedback goes unattended, and helps to identify trends in complaints and issues that require attention.

5 Discussion In this study, we aimed to assess the effectiveness of a digital platform used to handle feedback from residents of ACHs, identify the functions and features of such a platform that are important for ACH settings, as well as understand how the digital platform could contribute to improving the quality of care provision and residents’ satisfaction. Overall, our study indicates that staff at ACHs view resident feedback as a helpful tool in improving quality of care. Having a computerised system, like Tell Touch, improves staff efficiency and their ability to do their job more effectively. (1) Effectiveness to support productivity The results of our study confirm that using a digital tool was effective in collecting and managing feedback in ACHs. Digital feedback tools can quickly categorise feedback and generate reports that identify trends in complaints and areas that require attention. Research participants emphasized that having this information in a timely manner enabled the implementation of action plans to address issues before they could escalate. These findings are consistent with previous research conducted in acute hospitals in the UK, which has highlighted how real-time feedback is more likely to be effectively translated into practice, leading to improved outcomes for older adults with complex care needs [21]. (2) Usefulness Usefulness has two aspects in this study: the usefulness of the tool (in this study, Tell Touch) and the usefulness of feedback collected and processed by the tool. Our study confirms that data gathered from residents through this digital tool was useful to care staff enabling them to improve daily care practices, practice listening and acknowledging resident needs, design care plans, prioritise tasks and enhance relationships between staff and residents. Our findings indicate that the digital tool did help support individualised care services for residents on daily issues such as meals, outdoor activities, administration of medication, and other essential needs because it fostered the incorporation of

Digital Platforms to Support Feedback Processing in Aged Care Homes …

103

feedback into practice. The data collection and processing capabilities, notification functionality, and trend-identifying reports were highlighted as useful features of Tell Touch, suggesting they should be standard functions of any digital tool used for feedback processing. This study also demonstrated that a free-text tool is suitable for use within the context of ACHs since elderly residents often prefer to express their thoughts in free text format rather than answering a questionnaire. (3) User-friendliness reflecting multicultural aspects The importance of digital technology user-friendliness is a well-established concept that has been studied and demonstrated across various industries, including in healthcare settings. However, digital technology user-friendliness is especially important in ACHs due to the vulnerability, physical and mental constraints of residents. Thus apps used for feedback collection need to be simple to use and accessible (as per the Disability Discrimination Act 1992). Australia is a culturally diverse society, and its aged care residents come from a variety of linguistic and cultural backgrounds. Therefore it is important that the app for feedback collection provides a language selection option. Tell Touch meets this requirement as it supports communications in 80 languages. Its translation functionality is another deemed essential feature that significantly improves communications between residents and staff in ACHs. Previous research has emphasized the importance of language settings in mobile device apps used in aged care services. [39]. Our study confirmed the importance of this feature for communication purposes. (4) Supporting accreditation ACQ&SC Standard 6 mandates that health care providers have a procedure to handle feedback, with the four key requirements being: 6a) encouragement and support in providing feedback and complaints; 6b) ensuring ease of access for all residents, including those coming from different cultures, linguistic backgrounds and with diminished abilities; 6c) taking timely and adequate action in response to complaints; and 6d) working on continuous improvement of aged care services based on provided feedback (Aged Care Quality and Safety Commission, 2021). To address standard 6 in an effective manner a digital platform needs a rich and effective feedback processing functionality. Tell Touch aims to “enable users of aged care services to be heard while making aged care providers accountable for their actions” (telltouch.com). The feedback processing functionality of Tell Touch was confirmed and commended by research participants who commented on the practicality of reporting functions as well as notification features of the tool. (5) Challenges in ACH settings Past studies raised concerns that ACH residents could have reservations regarding providing negative feedback and complaints [9]. However, our findings show that ACH staff need this feedback to guide them in customising the provided services and making them resident centred. Other concerns relate to the mental and physical ability of residents to provide feedback using apps on electronic devices [5]. However,

104

T. Linden and R. Fisher

our findings demonstrate that ACHs adopt ways of dealing with such constraints, from delegating feedback provision to families and representatives, or using pen and paper in cases where this was the approach preferred by residents. However, there was strong evidence that training and education of able residents allowed them to use tablets and provide feedback via the Tell Touch app. This finding is in line with the past studies showing that touchscreen devices can help overcome barriers in communications with aged care residents as long as sufficient training is provided to residents and staff [20, 28]. (6) Summary of necessary functions and features of a feedback app The following features and functionalities were identified as required in a digital feedback app used in ACH settings. For residents and families: ● Simplicity in presenting app functions using a minimalist approach to reduce cognitive workload for residents and to make feedback provision effortless; ● Free-text feedback entry rather than answering restrictive pre-defined questions; ● Notifications and reminders - a balance needs to be struck between encouraging residents to provide feedback while reminders not being intrusive; ● A setting to select a preferred communication language. The development of AI and its integration into an app will enable immediate translation of the original post into the main communication language. ● For staff: ● Email notifications for new feedback ● Multi-lingual options ● Translation between languages capability ● Categorisation of feedback ● Priority setting for urgent feedback ● Time-based report generation ● Trend identification The list provided is not comprehensive, as it only reflects features specific to ACH settings as discussed by the interviewees. We acknowledge there may be other user interface principles that are applicable to any app or digital platform, regardless of the industry, and did not emerge from this study.

6 Conclusion Over the past decade, quality of care in aged care facilities has been a topic of significant discussion, particularly during the COVID-19 pandemic. Effective collection and processing of resident feedback is crucial to improving the services provided in these facilities. Our study highlights the efficacy of real-time digital feedback systems in improving care delivery and outcomes for residents in ACHs. The streamlined

Digital Platforms to Support Feedback Processing in Aged Care Homes …

105

data collection and analysis processes of such platforms have the potential to meet accreditation requirements and enhance care services in aged care homes. The study revealed that staff readily accepted Tell Touch and had a positive attitude towards the changes facilitated by the use of this digital platform. The tool’s high acceptability and usefulness, combined with the satisfaction of care staff in using it, suggest that digital feedback tools are effective and contribute to providing customised and individualised care by clearly understanding the needs of residents through feedback. Thus, our study supports the view that giving voice to those in care by collecting their feedback meaningfully, and acting on that feedback in a timely manner helps improve services to ACH residents. The study’s identification of key functions and features for effective feedback processing applications in aged care settings can inform the development of digital platforms, contributing to ongoing efforts to enhance the quality of care for ACH residents. The study acknowledges its limitations. Firstly, we investigated only one digital platform as a tool for feedback collection and processing. Further research is needed to examine and compare other existing technologies to gain a better understanding of their strengths and weaknesses as digital feedback handling tools. Secondly, the qualitative approach was suitable for our exploratory objectives. However, the limitation is that findings are not generalisable beyond the specific context in which the data was collected. Future research could assess feedback processing technology using the TAM (or a similar model) with the appropriate sample sizes and across a wider variety of aged care contexts to provide more comprehensive insights. Acknowledgements The authors would like to thank Antony Linden for consulting on user experience (UX) and digital platform evaluation.

References 1. Aged Care Quality and Safety Commission. Quality Standards. Standard 6. Feedback and complaints, Commonwealth of Australia, (2021). https://www.agedcarequality.gov.au/provid ers/standards/standard-6 2. A. Asprey, J.L. Campbell, J. Newbould, S. Cohn, M. Carter, A. Davey, M. Roland, Challenges to the Credibility of Patient Feedback in Primary Healthcare Settings: A Qualitative Study. Br. J. Gen. Pract. 63(608), e200–e208 (2013) 3. K. Bail, D. Gibson, P. Acharya, J. Blackburn, V. Kaak, M. Kozlovskaia, M. Turner, B. Redley, Using health information technology in residential aged care homes: An integrative review to identify service and quality outcomes. Int. J. Med. Informatics 165, 104824 (2022). https://doi. org/10.1016/j.ijmedinf.2022.104824 4. J. Benn, M. Koutantji, L. Wallace, P. Spurgeon, M. Rejman, A. Healey, C. Vincent, Feedback from incident reporting: Information and action to improve patient safety. BM J Qual. Saf. 18(1), 11–21 (2009) 5. D. Boldy, H. Bartlett, Residents’ views and quality improvement in homes for older people. J. Integr. Care 6(5), 200–206 (1998). https://doi.org/10.1108/14769018199800042 6. J.A. Boogaard, H.C. de Vet, M.C. van Soest-Poortvliet, J.R. Anema, W.P. Achterberg, J.T. van der Steen, Effects of two Feedback interventions on End-of-Life outcomes in nursing home

106

7. 8. 9. 10. 11. 12.

13.

14. 15. 16. 17.

18.

19.

20.

21.

22.

23. 24. 25.

T. Linden and R. Fisher residents with dementia: A Cluster-Randomized controlled Three-Armed Trial. Palliat. Med. 32(3), 693–702 (2018). https://doi.org/10.1177/0269216317750071 V. Braun, V. Clarke, Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006). https://doi.org/10.1191/1478088706qp063oa J. C. Brocklehurst and E. Dickinson, Audit and quality management systems for Long-Term care, Continuing Care for Older People, edited by M. J. Denham, Stanley Thornes, 1997 A.M. Chong, Validating the accuracy of the resident satisfaction measure. J. Cross Cult. Gerontol. 18, 203–226 (2003). https://doi.org/10.1023/B:JCCG.0000003090.71630.e9 S.-C. Chou, D.P. Boldy, A.H. Lee, Measuring resident satisfaction in residential aged care. Gerontologist 41(5), 623–631 (2001). https://doi.org/10.1093/geront/41.5.623 S.-C. Chou, D.P. Boldy, A.H. Lee, Resident satisfaction and its components in residential aged care. Gerontologist 42(2), 188–198 (2002). https://doi.org/10.1093/geront/42.2.188 A. Coulter, L. Locock, S. Ziebland and J. Calabrese, Collecting data on patient experience is not enough: They must be used to improve care. Bmj, 348, (2014), https://doi.org/10.1136/ bmj.g2225. F. D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), pp. 319–40, (1989), https://www.researchgate.net/profile/ Michel-Sylvie/publication/344247975_Mobile_Money_decryptage_d’une_succes_story_afr icaine/links/61603646ae47db4e57a80a60/Mobile-Money-decryptage-dune-succes-story-afr icaine.pdf. F. D. Davis, A technology acceptance model for empirically testing new End-User information systems: Theory and results. Mass. Inst. Technol. (1985) Department of Health and Family Services. Standards and Guidelines for Residential Aged Care Services Manual, (1998) A. Edwards, R. Evans, P. White, G. Elwyn, Experiencing Patient-Experience surveys: A qualitative study of the accounts of Gps. Br. J. Gen. Pract. 61(585), e157–e166 (2011) S.N. Etkind, B.A. Daveson, W. Kwok, J. Witt, C. Bausewein, I.J. Higginson, F.E. Murtagh, Capture, Transfer, and feedback of Patient-Centered outcomes data in palliative care populations: Does it make a difference? A systematic review. J. Pain Symptom Manage. 49(3), 611–624 (2015). https://doi.org/10.1016/j.jpainsymman.2014.07.010 S. Gaskin, A. Georgiou, D. Barton, J. Westbrook, Examining the role of information exchange in residential aged care work Practices-a survey of residential aged care facilities. BMC Geriatr. 12(40), 1–11 (2012). https://doi.org/10.1186/1471-2318-12-40 H. Gleeson, A. Calderon, V. Swami, J. Deighton, M. Wolpert, J. Edbrooke-Childs, Systematic review of approaches to using patient experience data for quality improvement in healthcare settings. BMJ Open 6(8), e011907 (2016). https://doi.org/10.1136/bmjopen-2016-011907 A.M.Y. Goh, S.M. Loi, A. Westphal, N.T. Lautenschlager, Person-Centered care and engagement via technology of residents with dementia in aged care facilities. Int. Psychogeriatr. 29(12), 2099–2103 (2017). https://doi.org/10.1017/S1041610217001375 C. Graham, S. Käsbauer, R. Cooper, J. King, S. Sizmur, C. Jenkinson and L. Kelly. An evaluation of a near Real-Time survey for improving patients’ experiences of the relational aspects of care: A Mixed-Methods evaluation. Health Serv. Deliv. Res. 6(15), (2018), https://doi.org/10.3310/ hsdr06150. R.J. Holden, O. Asan, E.M. Wozniak, K.E. Flynn, M.C. Scanlon, Nurses’ perceptions, acceptance, and use of a novel in-Room pediatric icu technology: Testing an expanded technology acceptance Model. BMC Med. Inform. Decis. Mak. 16(145), (2016). https://doi.org/10.1186/ s12911-016-0388-y R.J. Holden, B.-T. Karsh, The technology acceptance model: Its past and its future in health care. J. Biomed. Inform. 43(1), 159–172 (2010) B. Knowles, V.L. Hanson, Older adults’ deployment of ‘distrust.’ ACM transactions on Computer-Human interaction (TOCHI) 25(4), 1–25 (2018). https://doi.org/10.1145/3196490 M. Ko, L. Wagner, J. Spetz. Nursing home implementation of health information technology: Review of the literature finds inadequate investment in preparation, Infrastructure, and Training. INQUIRY: J Health Care Organ, Provis, Financ, 55, 2018. https://doi.org/10.1177/004695801 8778902.

Digital Platforms to Support Feedback Processing in Aged Care Homes …

107

26. C.S. Kruse, M. Mileski, J. Moreno, Mobile health solutions for the aging population: A systematic narrative analysis. J. Telemed. Telecare 23(4), 439–451 (2017). https://doi.org/10.1177/ 1357633X16649790 27. S.J. Lane, N.M. Heddle, E. Arnold, I. Walker, A review of randomized controlled trials comparing the effectiveness of hand held computers with paper methods for data collection. BMC Med. Inform. Decis. Mak. 6(1), 1–10 (2006). https://doi.org/10.1186/1472-6947-6-23 28. S.M. Loi, A. Westphal, N.T. Lautenschlager, An investigation of residential facility staff usage of touchscreen technology. Int. Psychogeriatr. 29(12), 2095–2098 (2017). https://doi.org/10. 1017/S1041610217000515 29. L.S. Nowell, J.M. Norris, D.E. White, N.J. Moules, Thematic analysis: Striving to meet the trustworthiness criteria. Int J Qual Methods 16(1), 1–13 (2017). https://doi.org/10.1177/160 9406917733847 30. J.G. Richter, A. Becker, T. Koch, M. Nixdorf, R. Willers, R. Monser, B. Schacher, R. Alten, C. Specker, M. Schneider, Self-Assessments of patients via tablet Pc in routine patient care: Comparison with standardised paper questionnaires. Ann. Rheum. Dis. 67(12), 1739–1741 (2008). https://doi.org/10.1136/ard.2008.090209 31. P. Ritter, K. Lorig, D. Laurent, K. Matthews, Internet versus mailed questionnaires: A randomized comparison. J. Med. Internet Res. 6(3), e103 (2004). https://doi.org/10.2196/jmir.6. 3.e29 32. Royal Commission into Aged Care Quality and Safety. Final report: Care, dignity and respect, (2021). https://agedcare.royalcommission.gov.au/sites/default/files/2021-03/final-rep ort-executive-summary.pdf 33. K.J. Saleh, D.M. Radosevich, R.A. Kassim, M. Moussa, D. Dykes, H. Bottolfson, T.J. Gioe, H. Robinson, Comparison of commonly used orthopaedic outcome measures using Palm-Top computers and paper surveys. J. Orthop. Res. 20(6), 1146–1151 (2002) 34. K.N. Shah, M.R. Hofmann, R. Schwarzkopf, D. Pourmand, N.N. Bhatia, G. Rafijah, S.S. Bederman, Patient-Reported outcome measures: How do digital tablets stack up to paper forms? A randomized, controlled study. Am. J. Orthop. (Belle Mead N.J.) 45(7), E451–E457 (2016) 35. E. Tawfiq, J. Desai, D. Hyslop, Effects of Results-Based financing of maternal and child health services on patient satisfaction in Afghanistan. J. Health Serv. Res. Policy 24(1), 4–10 (2019). https://doi.org/10.1177/1355819618802944 36. UK Department of Health. Using the commissioning for quality and innovation (Cquin) payment framework: Guidance on new national goals for 2012– 13, (2012). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/att achment_data/file/215049/dh_133859.pdf 37. C. Violato, J. Lockyer, H. Fidler, Multisource feedback: A method of assessing surgical practice. BMJ 326(7388), 546–548 (2003). https://doi.org/10.1136/bmj.326.7388.546 38. J.I. Westbrook, Patient satisfaction: Methodological issues and research findings. Australian health review: a publication of the Australian hospital association 16(1), 75–88 (1993) 39. R. Wilson, D. Cochrane, A. Mihailidis, J. Small, Mobile apps to support Caregiver-Resident communication in Long-Term Care: Systematic search and content analysis. JMIR aging 3(1), e17136 (2020). https://doi.org/10.2196/17136

State of Digital Health Communication Infrastructure in LMICs: Theory, Standards and Factors Affecting Their Implementation Andrew Egwar Alunyu, Mercy Rebekah Amiyo, and Josephine Nabukenya

1 Introduction A robust communication infrastructure is required to support healthcare data sharing required to facilitate patient-centred care. Components that make up the digital health communication infrastructure (DHCI) include software and digital health applications, the hardware, network, security and personnel that use the infrastructure to capture, process, store and or share health data/information [1–4]. Actually, in a digital health information-sharing environment, communications engage people and things including the physical backbone that supports the digital health applications [3]. However, in low and middle-income countries (LMICs) constrained by resources, there are only limited components of the DHCI and are often in a poor/ inadequate state [1, 5, 6]. Furthermore, proper management of these DHCI components is required to realise its full potential in supporting patients and the public. Nevertheless, the skills required to develop, deploy, maintain and use digital health technologies are greatly lacking which has continued to hinder LMICs’ from reaping the expected benefits from their digital health implementations [7]. Several reasons have been given for the poor state of these DHCI components including limited funding, lack of technical skills, inadequate legal and governance frameworks leading to uncoordinated implementations, and limited political support among others [6–8]. These challenges can be categorised into financial, technical A. E. Alunyu (B) Faculty of Engineering, Busitema University, Tororo, Uganda e-mail: [email protected] A. E. Alunyu · M. R. Amiyo · J. Nabukenya Department of Information Systems, Makerere University, Kampala, Uganda e-mail: [email protected] J. Nabukenya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Daimi et al. (eds.), Current and Future Trends in Health and Medical Informatics, Studies in Computational Intelligence 1112, https://doi.org/10.1007/978-3-031-42112-9_6

109

110

A. E. Alunyu et al.

and management/governance problems, both internal and external. The challenges have resulted in diverse implementations of digital health (DH) systems that lack interoperability [6, 8, 9] and therefore are unable to support secure, reliable healthcare data sharing on time [7]. The telecommunications domain has conceptualised the use of standards as a solution to the problem of interoperability of communications systems [10]. Similarly, health systems have adopted the standardisation approach to address such gaps. Standardisation means different things in different domains [11]. While standardisation implies the use of the standard to some, to others, it means adopting the same procedures or methods to harmonise work processes for compatible outputs [11]. Whereas the international standards organisation considers standardisation to be the “activity of establishing, with regard to actual or potential problems, provisions for common and repeated use, aimed at the achievement of the optimum degree of order in a given context” [12]; Rowlands [13] suggest that it should cover development and consistent implementation of the standards and technical documents. Therefore, this study infers that standardisation of digital health (DH) could mean developing and using standards for harmonised processes and interoperable health information system outcomes. Standardisation consists of formulating, issuing and consistently implementing standards [14, 15]. Whereas formulating and issuing standards are standards development activities, implementation covers deploying the standards, using them and monitoring to ensure that every participating entity is compliant. Developing a standard that may be accepted among implementing entities requires adhering to universally acceptable principles of standards development. However, it should be noted that standards development is a complex process. For example, the TU-T [10] argued that DH standardisation is one of the most challenging areas of standardisation. Balancing the sometimes-diverging expectations of all digital health stakeholders and driving consensus is a difficult task requiring negotiation expertise to arrive at a consensus. The difficulty in developing quality standards is exacerbated by colossal legacy systems and technologies that are still in place, massive data quantities in various formats, multiple areas of technologies and complexity caused by competing/overlapping standards initiatives [10, 16]. Different standards development organisations (SDOs) agree on several principles of standardisation, including openness, transparency, representation, impartiality, consensus, market need and net benefit, timeliness, internationality, compliance, coherence, availability, and support [13, 17, 18]. Openness means the standards development processes are open to any interested party on a non-discriminatory basis. Transparency means information on current work (standards development) programs, such as proposals and how to participate, is available to all interested parties. Representation refers to the balanced participation by those significantly affected by a resulting standard (stakeholders). Impartiality is concerned with not privileging or favouring the interests of a particular entity or group of stakeholders. Consensus refers to the general agreement (absence of sustained opposition) on a standard. Market need and net benefit mean a standard is produced only when needed and there are possible benefits. Timeliness refers to the urgent need for a standard. The internationality principle recommends that relevant international standards be

State of Digital Health Communication Infrastructure in LMICs: Theory …

111

used in preference to national and sub-national standards where they exist. Compliance means any standard being produced should comply with relevant regulations, including the natural law of competition. Coherence means standards that are developed should be consistent with developments in other sectors, e.g., the health sector’s DH standards being consistent with ICT standards. Availability refers to the readiness with which such a developed standard is accessible. Finally, support means approaches to disseminating supplementary knowledge associated with a standard should be considered during its development. Any process that satisfies these principles of standardisation is considered suitable to guide a domain’s standards development. Therefore, this study argued that if an LMIC followed and made due consideration to standardisation principles during the development of DH standards, then the developed standard would be suited to guide the standardisation of the DHCI for Uganda’s health system. The use of a standard entail consistent implementation, compliance monitoring and maintenance of the standard [13]. Consistent implementation is achieved through the adoption and consistent use of the standard for its intended purpose. It is claimed that any consistent use of a health standard is ascertained by ensuring effective use across a health system [19]. Achieving a level of standardisation that will deliver interoperability of DH systems “involves both a process for establishing and maintaining the standards and an organisational infrastructure for implementing that process” [14]. There is a need for frameworks and theories that support standards development, implementation and monitoring of compliance that is suited for the resourceconstrained health systems common in LMICs. However, there is scanty literature on frameworks or theories that can be adopted to support digital health standardisation. Besides, existing standardisation frameworks are based on procedures and processes that have been proven to work in high-income countries. Actually, common digital health standards like DICOM, HL7, and FHIR among others were developed and piloted in developed countries, but all countries are being encouraged to adopt them. Authors and some standards bodies have recognised the need to adapt/ contextualise standards having realised that the contextual environment affects their implementation success. To better understand the scope of standardisation of the DHCI in Uganda, an example of a resource-constrained health system, this study first reviewed the literature to determine what concepts and constructs that can be borrowed to construct a comprehensive DHCI standards framework that covers the full scope of standardisation to include standards development, implementation and compliance monitoring. Secondly, the study explored DH stakeholder experiences and views regarding the existence and use of those concepts and constructs in Uganda’s DHCI environment to identify any challenges and gaps in standardisation.

112

A. E. Alunyu et al.

2 Literature Review To better understand DH standardisation in low-resource settings, we reviewed current practices of standardisation in select countries and theories/frameworks of standardisation to inform our exploration of the status of DHCI standardisation in Uganda’s health system.

2.1 Current Practice of Standardisation by the Country’s National Standardisation Bodies Although criteria for selecting best practices in public health include relevancy, community participation, stakeholder collaboration, ethical soundness, replicability, effectiveness, efficiency, and sustainability [20]; this study assumed comprehensive stakeholder participation/collaboration, ethical soundness, and possible replicability of standards practices from countries that have made progress in standardising digital health. We reviewed current standardisation practices in similar peer countries in the region intending to highlight commonalities in practice and gaps in standards’ development, consistent deployment, and effective use. Three high-income, two uppermiddle-income and three lower-middle-income countries (as of 2021 World Bank classification) were reviewed. Table 1 presents a summary of the standardisation practices from this review. Overall, the current standardisation practice in the reviewed countries shows commonalities in the standards development/adoption process. Notably, these countries have adopted similar standardisation processes with cycles that broadly include developing the standards proposal, discussion by a technical committee, seeking public input/contributions, and approving and publishing the standards. However, the review also shows that standardisation activities in the reviewed countries do little to support deployment and consistent implementation and ensure continued compliance/use of the standards. Existing gaps in the standardisation process are consistent in the areas of research and preliminary activities of determining the need and scope of the proposed standard, stakeholder engagement, promotional activities, maintenance activities, criterion, and or tools compliance monitoring strategies, and guidelines on how to deal with non-compliance with the standards.

2.2 Theory and Frameworks for Standardisation The study used (1) Baskin et al’s. [21] six dimensions of standards, (2) Fomin’s Process Model of how successful standards emerge, (3) Three technology adoption theories i.e., Technology Adoption Model (TAM), Technology, Organisation, and

State of Digital Health Communication Infrastructure in LMICs: Theory …

113

Table 1 Current practices of standardisation in eight countries Country (income status) The practice of standardisation to show how they got here: through activities of standards adoption, development and or implementation

What they are doing to ensure effective use

Estonia (high-income)

Involves preparation and determining standards to develop/translate into Estonian ones → Commenting/ providing feedback on and approving standard translations → Publishing/ reprinting the standard

● No data

Gap

● Standards are considered a matter of the state, and difficult to attract stakeholders to participate in the process ● Lack of promotional strategies and funding plan ● Lack of mechanisms/tools to support the continued use of the standards ● Lack of criteria for monitoring use and or dealing with non-compliance

Australia (high-income)

● Involves research on standards to be ● Conformity developed → Standards assessment—compliance development → adherence to a monitoring methodology for standardisation (orchestration of a complex, adaptive standards ecosystem) → Accreditation → Commissioning → Promotion → Continued funding

Gaps

● Lack of a documented standardisation process ● No information on monitoring use or criteria for dealing with a lack of non-compliance

New Zealand (high income)

Involves making a proposal for the needed standard → Forming a committee to draft the standard → Drafting the standards → Consulting the public and building consensus → Committee ballots to approve the standard → Publishing the standard → appointing accreditation bodies

Gaps

● Lack of mechanisms/tools to support the continued use of the standards ● Lack of criteria for monitoring use and or dealing with non-compliance (continued)

● No data

114

A. E. Alunyu et al.

Table 1 (continued) Country (income status) The practice of standardisation to show how they got here: through activities of standards adoption, development and or implementation

What they are doing to ensure effective use

Thailand (upper-middle-income)

A standard work program is published ● Accreditation and every six months → Collaboration conformity assessment are with regional and international delegated standardisation bodies to develop standards → Host standards information centre → Promote country standards → and appoint accreditation bodies

Gaps

● Limited rigour in the standards development/adoption ● Limited human resource capacity and stakeholder engagement in developing the standards ● Lack of standards, promotional strategies, and a funding plan ● Lack of mechanism for enforcing conformity and criteria for dealing with non-compliance

South Africa (upper-middle-income)

Involves conducting a preliminary ● Assess implementors’ investigation regarding the standards conformity with the to be developed → A proposal for the standards standard is developed → A technical committee discusses the standard → Public is consulted to contribute during the standards inquiry stage → Publication of the standards → Conduct promotion activities to facilitate the correct application of standards

Gaps

● It does not stipulate resources required to support the standards development process ● Limited stakeholder engagement as only high-level implementers get to know standards under public review ● Missing a plan for collaborative agreement on stepwise/phased/ planned implementation of the standard ● Lack of promotional strategies and a funding plan ● No data on compliance enforcement

Sri Lanka (lower-middle-income)

● Involves standards development → Publication of the standards → Dissemination of standards information and promote adoption of standards

Gaps

● Lacks guidelines on preliminary activities leading to standards development ● Not clear about the level of stakeholder involvement in the standards development process ● Lack of guidelines on standards review (maintenance) mechanism

● Conformity assessment through testing, inspection and certification

(continued)

State of Digital Health Communication Infrastructure in LMICs: Theory …

115

Table 1 (continued) Country (income status) The practice of standardisation to show how they got here: through activities of standards adoption, development and or implementation

What they are doing to ensure effective use

Kenya (lower-middle-income)

Involves drafting the standards by the ● Testing of goods and technical committee → Public is services that implement the invited to review the draft standard → standard Technical committee improves the standard → Approval—The proposed standard is put up for adoption

Gap

● Limited user involvement in the standards development/ determination process; only high-level implementers get to know the standards are under public review

Tanzania (lower-middle-income)

A proposal for establishing a standard ● No data is made → Establishing the necessity for standards → Preparing a draft standard → Public consultations → Approval → Gazetting the standard

Gaps

● Limited attempt to build consensus among stakeholder groups ● Limited stakeholder engagement and only high-level implementers get to know standards under public review ● No data on measures of compliance enforcement

Environment (TOE), and the Internet Standards Adoption (ISA), and (4) the European Statistical System (ESS) Standardisation Process. Baskin et al. [21] posed pertinent questions that are critical to understanding standardisation activity. The scope of the questions is summarised into broad themes; why seek a standard? What is the category of product or service to be standardised? When in the product cycle to standardise? Which is the appropriate Standards Development Organization (SDO)? How will consensus be reached? and where will the standard be used? This study re-categorises the questions into three main overarching questions: why is there a need for a standard (what is the standard for)? what is the most suitable process for standards development in resource-constrained environments? and how should the standard be applied for maximum impact? These three broad questions give rise to the concepts of setting standards context, standards development process, and standards implementation and monitoring compliance with the standards. These constructs are expected to span the full scope of the standardisation process as advocated by Rowlands [13], who call for the development and consistent implementation of standards and other technical documents. Fomin’s [22] process model of standardisation integrates separate lines of inquiry to standardisation activities, including Simon’s theory of artefact design, Weick’s sense-making concept and Latour’s negotiation in socio-technical networks, organise them into a hierarchically organised web of standardisation events. Their model conceptualises standard development as a process where two or more actors come to agree upon and adhere to a set of technical or operational specifications of an

116

A. E. Alunyu et al.

ICT system, its parts or its functionality, either tacitly or as a result of a formal contract [23]. Since it’s an agreement between parties, it has social and communitydependent aspects [22]. The standard development is conceptualised in the “context” of the design as a set of related activities that lead to the understanding of what is articulated between actors, why, and how they reach agreements due to communications between them [22]. Actors, in this case, the DH stakeholders participate in the activities that include showing a (signal) willingness to develop a standard; participate in formulating the principal concept and scope of DH standards that make sense for them; agree on the exact content and form of the standard; develop a contractual agreement for “gives” and “takes” concerning the standardisation process and its outcomes, and agree upon what “conforming” to the standard specification means. This model presents a rigorous process of standards development that satisfies the principles of standards development, including transparency, openness, and consensus-building. Concepts from the technology adoption theories that were explored include; (1) how the ‘perceived usefulness’ and ‘perceived ease of use’ from TAM [24, 25] can inform the formulation of context-relevant DHCI standards. (2) How organisation characteristics (characteristics and resources of the organisation), technology characteristics (all technologies that are relevant to the organisation and attributes that determine their rate of adoption), and environment characteristics from TOE influence adoption to use of the standards and implementation [26]. These TOE constructs can inform the standards’ adoption and consistent use. (3) ISA was used to study the perception of DH stakeholders in Uganda regarding adopting standards in the country’s health system. According to Hovav [27], Internet standards adoption is a function of the utility of the standard’s characteristics (individual perspective) and the environment in which the adopter operates (community perspective). ISA’s framework acknowledges that besides the features of the standard having high utility (useful features), successful adoption requires a conducive adoption environment [27]. Both dimensions must be of high quality for the standard to be fully adopted. Useful features of a standard may appeal differently to potential adopters. The European Statistical System (ESS) standardisation process has five major stages, including establishing the need for a standard, developing the standard, adopting the standard, disseminating, applying the standard, and maintaining/ reviewing the standard [28]. In addition to standards development, as currently followed by SDOs, ESS has activities that this study considered foundational to inform the standardisation of the DH in LMICs; some of them are critical to success when implementing standards. The activities include establishing the need for a standard, standards dissemination, application, monitoring and conformance assessment. Therefore, adapting these components of the ESS framework can facilitate an LMIC, like Uganda to; (1) produce consensus-based DH standards and (2) support the standards’ implementation and consistent use. Finally, Kern [29] argues that continual changes in the organisation, technology, and innovation warrant periodical reviews. In this case, it would warrant a periodic review of the standards for DHCI. Therefore, establishing and managing the life

State of Digital Health Communication Infrastructure in LMICs: Theory …

117

cycle of DHCI standards should be core elements of the national infrastructure, as recommended by Stroetmann [30]. As evidenced in this review, several countries’ current DH standardisation practices lack rigour, especially for the implementation and compliance monitoring stages. Besides, our literature review did not find a comprehensive framework or theory in the digital health domain to support the full scope of standardisation. Thus, the researcher used concepts from the reviewed Fomin’s Process Model, the technology adoption theories, and ESS to explore DH stakeholder experiences and views in Uganda regards the existence and use of standards relevant to the DHCI.

3 Methods A scoping literature review followed by an exploratory survey of the DHCI standardisation environment in Uganda was conducted. The exploratory study adopted a cross-sectional study design to explore the state of DHCI standardisation in Uganda as a case of resource-constrained health systems in LMICs. We explored the standardisation process to identify factors affecting standardisation of the electronic communication infrastructure that should support health information exchange in Uganda’s health system. The case study explored the four top health system levels in Uganda, i.e., Health Center IVs, District Hospitals, Regional Referral Hospitals and National Referral Hospitals. Both qualitative and quantitative data were collected from purposively selected key respondents from the four top health system levels and the national level. Study Site Selection: Government-owned and private, not-for-profit health facilities were considered in the study. They included one national Referral Hospital (NRH), Three Regional Referral Hospitals (RRH), Three District Hospitals (DH), twenty Health Centers Fours (HC VI), and two private health facilities (a hospital and an HC IV), which were purposively selected from four regions of Uganda. A health facility was considered in this study if; (i) It has implemented some form of eHealth, e.g., electronic medical records, Health Information Systems, Mobile technology for surveillance, (ii) It is an Infectious Disease Institute (IDI) site, (iii) It is located in a rural, peri-urban or urban setting representing the disparity in urban–rural resource differences in Uganda, and (iv) It is among Uganda’s four top health system levels, i.e., NRH, RRH, DH, and HC IVs. Besides, national views regarding the state of eHealth technologies and standards in Uganda were obtained from policymakers at the Ministry of Health, Uganda National Bureau of Standards (UNBS), the Ministry of ICT, other government departments, and healthcare implementing partners in Uganda. Participant Selection: Conditions for selecting a participant to be included in the study were (i) being an implementer and/or supervisor of eHealth at the health facility

118

A. E. Alunyu et al.

level. Those included were In-charge/Medical directors of health facilities and ICT/ Data/Record Officers at health facilities. (ii) Users of eHealth technology at health facilities like the Medical Officer/Clinician, Laboratory Technologist, Nurse, and Pharmacist. (iii) Policymakers and health implementation partners in Uganda. They were drawn from various ministries, departments and authorities (MDAs), partner groups, healthcare trainers, and researchers. Only those that consented to give us interviews were finally interviewed. Field Survey: The study collected both qualitative and quantitative data using interview guides and questionnaires. Semi-structured interviews were used to investigate current challenges to standardising eHealth communication infrastructure and technologies in the top four levels of Uganda’s health system. The choice of qualitative interviews were based on the argument of Hege and Vibeke [31], who suggested that they stand out as a well-suited method to grasp the socio-technical complexity and rapid changes that characterise the e-health sector. To further benefit from the informed and technical expertise of DH policymakers, health development partners, researchers/trainers, eHealth implementers and users at the healthcare level, the study also used focus group (FG) discussion. Whereas national-level participants, officers in charge and ICT officers of health facilities responded to the interview questions, clinicians, nurses, pharmacists and laboratory technologies at health facilities filled in the questionnaires. Facility-level data collection took place in January 2020; and due to the onset of the COVID-19 pandemic, interviews at the national level were conducted via online platforms. Two online platforms were used, i.e., Skype and Zoom and participants chose a preferred option. The interviews only focused on exploring the standardisation process, both the standards adoption, adaption, and development process, and identifying challenges to the process, including implementation and compliance monitoring process. The FG helped filter the challenges that emerged from the interviews. Analysis: Quantitative data collected were analysed using MS Excel. Interview transcripts were systematically reviewed and coded in NVivo 12. Transcripts were coded for standards adoption, standards adaption/contextualisation, standards development, standardisation process/activities, stakeholder participation in the standards process, standards implementation, monitoring compliance with the standards, actors in standards implementation, standards monitoring tools, factors influencing standardisation process (research and development, adoption, adaption, development, implementation and compliance monitoring). Ethical clearance: Makerere School of Public Health’s Institutional Review Board provided ethical approval for this study. Also, the Ministry of Health, the eHealth problem owners in Uganda, granted the researchers permission to collect data from selected healthcare sites. The District Health Officers (DHOs), the Officer-in-charge of study health facilities, and the participants consented to the study.

State of Digital Health Communication Infrastructure in LMICs: Theory …

119

4 Results 4.1 Demographics of Respondents Responses reported in this paper are based on the participation of participants and respondents, as listed in Table 2. Of the expected 140 respondents and 90 study participants, 132 questionnaires were returned, and 56 interviews were completed, constituting a total response rate of 81.7%, which Mugenda and Mugenda [32] suggest is excellent for analysis. The researcher analysed and presented results from the 188 actual responses. Subnational participants and respondents were spread over the regions of Uganda, as shown in Fig. 1. Northern Uganda had a 50% representation of the overall Regional Referral Hospitals (RRH) in the study districts of Uganda. Most of the IDI-supported District Hospitals (DHs) were in Uganda’s West Nile and Western regions, represented by 66.7 and 33.3%, respectively. Discussions include upcountry (rural) experiences of the health facilities characterised by responses from Eastern Uganda, West Nile and Northern Uganda. Central Uganda covered the districts of Wakiso and Kampala, representing the country’s urban and peri-urban settings. On the distribution of Subnational participants/respondents by the type of health facility, Table 3 shows that 66.7% were from HC IVs, 22.7% from referral hospitals and only 10.6% from district hospitals. Participation in the study as represented in Table 2, Fig. 1 and Table 3 reflects views representative of all categories of digital health stakeholders in Uganda, both Table 2 The response/participant rate of the different categories of participants Category of respondent/ participant

Expected responses

Actual responses

Rate

Subnational respondents/participant Facility administrator/ in-charge/medical director

28

19

68.9%

ICT/data/record officers

28

22

75.6%

Clinical medical officers

28

28

100.0% 100.0%

Laboratory

28

28

Nurses

56

51

91.1%

Pharmacists

28

25

89.3%

National participants Policy makers

10

5

50.0%

Health development partners

5

4

80.0%

Implementation partners

13

4

30.8%

HI researchers

6

2

33.3%

Total

230

188

81.7%

120

A. E. Alunyu et al. 66.7% 50.0% 31.6%

16.7%

33.3% 26.3% 16.7%

21.1%

0.0%

16.7%

21.1%

0.0%

Central

Western

Northern

RH

DH

West Nile

HC VI

Fig. 1 Distribution of health facilities by region of Uganda

Table 3 Distribution of respondents by facility type Facility type Valid

Frequency

Per cent

Valid percent

Cumulative percent

HC IV

88

66.7

66.7

66.7

Hospital

14

10.6

10.6

77.3 100.0

RH Total

30

22.7

22.7

132

100.0

100.0

at the national health system level and subnational levels. This study is considered representative of the actual state of digital health standardisation in Uganda at the time of data collection.

4.2 Standardisation Practice in Uganda Two national-level participants, i.e., M04 (NITA-U Standards Officer) and M05 (UNBS Standards Officer), elaborated on developing standards in Uganda, stating that it is inter-sectorial and involves multiple Ministries, Departments and Agencies (MDAs). Figure 2 summarises their response into six main phases: preparatory, development, dissemination, implementation, monitoring, and review. The activities start with determining the needs and priorities that can be addressed through standards, followed by standards’ workgroup engagement to develop the standards and approval process. Once it has been approved, the standards are then declared national standards. For proper implementation and use of a standard, the responsible entity should disseminate and build the capacity (train) in its use. Once implementation is underway, the effects and impacts of the standard can be monitored. This monitoring may trigger the decision to review the standard and subsequently revise, confirm, withdraw the standards, or strengthen its implementation. To better understand the standardisation process, participants were asked about their participation, majority of the respondents (61.4%) disagreed that they were

State of Digital Health Communication Infrastructure in LMICs: Theory … Phase 1: Determination of needs & priorities that can be addresses through standards

Phase 6: Review/ decision on followup: Revise, Confirm, Withdraw standards/ Strengthen implementation

121

Phase 2: Development, Approval & Declearation

Phase 3: Dissemination/ Capacity Building/ Training in the standard

Phase 5: Monitoring of the effects & Impacts of the standard Phase 4: Use/ Implementation of the standards

Fig. 2 Standardisation life cycle [33]

involved (see Fig. 3). Furthermore, 45.4% of the respondents said that the standardisation team is improperly constituted. In the participant’s expressions, the process follows the ‘PUSH’ methodology: “What I have seen before with the MOH [Ministry of Health] is that they use the push system. So, push in a way that we have generated this, of course, they have experts, consultants; they have the involvement of other IPs, so for you on the ground, what you feel whether that will be possible according to your experience some other person has more experience than you to decide for you” (W10, Facility Administrator). This may suggest that facility-level stakeholders are less involved in the process and, therefore, not knowledgeable about the activities of the standards committee. Further interrogation of stakeholders’ involvement in the standardisation process revealed that subnational stakeholders were not fully engaged in the standardisation process. As a result, participants felt left out of the process or even the decision to adopt DH technologies. A participant expressed this view on the need to include health facility-level users in the development/selection of standards and DH technologies, saying, “… I think for something to work in health, you need to involve the The standardisation team meeting is well structured

14.4%

40.2% 45.5%

A HF readiness to adopt the satndards is assessed Standardisation team is properly facilitated

19.7%

Standardisation team is properly constituted I participate in DHCI standardisation process Agree

Neutral

27.3%

15.1%

13.6%

37.9% 34.8%

44.7% 40.2% 34.8%

45.4%

25.0%

Disagree

Fig. 3 Stakeholder engagement in the DHCI standardisation process in Uganda (n = 132)

61.4%

122

A. E. Alunyu et al.

lower cadres upwards” (N06, Medical Director). The lack/limited involvement of subnational levels in the standardisation process seems consistent to warrant a participant to believe that developing standards (SoP) is an MoH responsibility that does not include facility-level stakeholders. Participant W04, ICT Officer, stated, “… most of the SOPs we are using, they nationally used, we are using national guidelines…”. The participant further recognised the need for multiple stakeholder involvement in the standardisation process by saying, “Health sector is sensitive; you can’t come up with your own. So, we follow national guidelines” (W04, ICT Officer); and that the standards development team should have adequate time and resources to complete their standardisation tasks. Respondents indicated that the team was improperly facilitated. Regarding support provided to the MoH for DHCI’s standardisation, only 40.2% agreed. They claimed that although health development partners (HDPs) like World Health Organisation (WHO) and United Nations International Children’s Emergency Fund (UNICEF) often offer support, they are insufficient to support the entire standardisation process. Also, health workers do not possess the required ICT standardisation literacy, knowledge, and experience to participate in the standardisation process. Nevertheless, HDPs provided technical support to the Ministry of Health during the standardisation process, including providing standards and guidance documents as mentioned by participant IP02: “provide standards from other settings/countries, that can be used for benchmarking best practice” (DP02, ICT Specialist). Finally, regarding whether health facility readiness assessment is done before the proposed adoption of DHCI and standards, more respondents (37.9%) agreed, 27.3% disagreed, and 34.8% remained neutral. This may imply some assessment is done but is not extensive enough to be noticed by all health facility workers. Regarding standards implementation and compliance monitoring practices being used in Uganda’s health system, results in Fig. 4 shows that only a small percentage of respondents agreed that there are guidelines for implementing DHCI at the facility (31.8%) and that implementers try to adhere to those guidelines (32.6%). The majority agreed that there is good communication and coordination between DHCI implementers (60.6%) that can be leveraged upon. Although a participant argued that some form of implementation is going on by saying, “…you are blocked if you try to access certain sites which you are not supposed to” (N06, Medical Director), the low agreement on whether implementers adhered to the standard/guidelines when implementing ICT in healthcare is evidenced by a participant who said, “someone goes and develops a system without consulting us and knowing what problem we have and then tries to force us to use the system” (M03, Assistant Commissioner Health Information Management). Regarding monitoring compliance with standards for DHCI, subnational participants indicated they were not involved in the decisions to monitor compliance with standards for DH. Most of the respondents were non-committal on all four questions on compliance monitoring. Therefore, they could not be asked to narrate their experiences or state the activities of DH standards implementation and compliance monitoring. For example, one respondent said, “There is an informal way of monitoring; occasionally, you come across something that has been done wrongly by

State of Digital Health Communication Infrastructure in LMICs: Theory … 34.90% 39.40% 25.80% 30.40% 35.60% 34.10% 34.80% 37.10% 28.10% 33.30% 41.70% 25.00%

There is inter-facility collaboration on DHCI standards' compliance monitoring A committee/personnel is assigned to monitor compliance with DHCI standards at HF There is well established DHCI standards' monitoring structure There are guidelines for monitoring DHCI at the health facility There is good communication and coordination among DHCI implements Implementers adhere to standards when implementing DHCI There are guidelines for implementing DHCI at the health facility Agree

Neutral

123

20.50% 18.90%

22.00%

32.60%

60.60%

45.50%

31.80% 43.20% 25.00%

Disagree

Fig. 4 State of implementation and monitoring compliance with standards for DHCI in Uganda (n = 132)

individuals, but there is no system which constantly monitors compliance” (N06, Medical Director), which means that the respondent could not identify any tools, criteria or mechanisms adopted to monitor DHCI and standards’ implementation and level of compliance with such standards. However, in the absence of an independent arm for monitoring and evaluation of DHCI, HDPs support the Ministry of Health as mentioned by participant W04, ICT Officer: “The government does not have an independent arm attached to monitoring and evaluation. The health development partners bring in their M&E systems” (W04, ICT Officer). Also, a national participant claimed that Uganda’s National Information Technology Authority (NITA-U) conducts assessments of implemented digital systems to ensure they comply with standards. “NITA-U also conducts an assessment to compliance to standards once systems have been implemented” (M04, Standards Officer). But this claim could not be collaborated by any participant from the MoH. Besides, no documented evidence exists regarding the findings of such claimed assessments.

4.3 Factors Affecting Implementation of DHCI Standards in Uganda Overall, the study identified factors that affect development/contextualisation and or implementation of DHCI standards in Uganda’s health system. Identified factors extend the organisation’s preparedness for DHCI implementation to issues of the process followed in developing, implementing and monitoring compliance with the standards (as summarised in Table 4). These factors are explained in subsequent subsections as follows;

124

A. E. Alunyu et al.

Table 4 Factors affecting standards implementation identified during the study Aspects

Factors that influence standards implementation in Uganda

1. Organisational readiness

(i) Inadequate technical expertise and user skills (ii) Weak DH leadership/governance structure (iii) Financial constraints (iv) Unregulated implementations of health information systems (HIS) and other DHCI components not guided by DH standards

2. Standardisation process

(i) The MoH lacks a documented methodology for determining standards (ii) Limited involvement of lower-level carders in determining standards DHCI (iii) Limited sensitisation of facility-level users about the existence and need for DHCI standards (iv) Inadequacy of the reviewed/updated/contextualised standards (v) Poor documentation of DH standards for health facilities

3. Standards implementation process

(i) Sometimes the standards are difficult/confusing to understand for end-users (ii) Limited awareness of standards for ICT being used in healthcare processes (iii) Unreliable facilitating resources like electric power, financial, and human resources

4. Compliance monitoring process

(i) The lack of an agreed-upon level of adherence (ii) Lack of monitoring tool/criteria/mechanism agreed upon by stakeholders (iii) Lack of DH standards’ monitoring structure

4.3.1

Gaps in organisation’s Preparedness to Implement DHCI and Standards

(i) Inadequate Technical Expertise and User Skills: only a few respondents agreed that Uganda had adequate technical expertise in DH. This confirms the views of a participant who said, “You know most people are not yet computer literate. It requires some training” (C04, Facility Administrator). When another participant was asked to comment on computer literacy in the health sector, he/she stated that “People here are not computer literate” (C09, Data Officer), which inadequacy may extend to include relevant knowledge that contributes to the standards development/contextualisation process. Authors have recommended developing a policy on human resources development and skills training in LMICs [34, 35]. Besides, such policy and or training programmes should stipulate the required expertise, skills, competencies, and

State of Digital Health Communication Infrastructure in LMICs: Theory …

125

specialised knowledge required to participate in the standardisation of DHCI (an eHealth standardisation project). Also, it should be clear about investing in human capital development, continuous professional development, and any incentives to motivate health professionals. (ii) Weak DH Leadership/governance structure: One participant (DP01, ICT Specialist) noted a challenge of leadership structure to decide whether DH innovations should be considered. The complexity of multiple innovations led to having multiple systems with different standards that could not be interoperable. In addition, there were challenges for individuals or agencies to move their agenda forward and not abide by the implementation of standards. The weak leadership resulted in siloed applications, as mentioned by M01, MoH’s Health Information Specialist: “Our biggest challenge has been siloed implemented applications at disease level that are not even speaking to each other…. most of these solutions are not also sustainable……this is because HDPs implement systems without following the structures put in place for approval” (M01, MoH’s Health Information Specialist). The siloed systems were also facilitated by a lack of monitoring systems, as noted by participant IR01, Health Informatics Researcher. This finding aligns with the fact that only 32.6% (see Fig. 4) of the respondents agreed that implementers adhered to guidelines when implementing ICT in healthcare. (iii) Financial Constraints: very few respondents at the health facility level agreed that the standards development or review team had adequate resources to develop the DH guidelines. The same was echoed by participant M05 (a UNBS Standards Officer) at the national level that a significant amount of money was needed to implement the standards. Spring and Weiss [36] proposed a solution to this problem of financing the standardisation process. (iv) Unregulated implementations of HISs and other DHCI components not guided by DH standards: In addition, although one participant mentioned that the MoH developed the DH policy to guide DH standards implementation (M03, Assistant Commissioner Health Information Management), there are no guidelines, criteria or standards to guide the MoH and DH stakeholders’ decisions on such implementation. Besides, this was confirmed in a literature review of DH standardisation in LMICs, which found no suitable frameworks low resources countries like Uganda can use to fast-track the standardisation of the DH CI [37]. 4.3.2

Challenges that Relate to Standards Formulation

(i) The MoH lacks a documented methodology for determining standards: or even scrutinising DH applications/technologies to be adopted across the different healthcare sites in Uganda’s healthcare system. In addition, there is no implementation/compliance monitoring guide. However, several authors argue for the development of a mechanism to support standards development, implementation planning, validation, use, and conformance [38–41], including the

126

A. E. Alunyu et al.

all-inclusive and established structure of stakeholders mandated to standardise the DHCI [42, 43]. (ii) Limited involvement of lower-level carders in determining DHCI standards: Only 13.6% (Fig. 3) of the respondents at health facilities mentioned that they participated in deciding the guidelines for ICT to be used at the health facility level. Both national and subnational participants agree that all stakeholders have limited involvement in standardisation activities, including requirements elicitation. Notably, PM05 (UNBS Standards Officer) reported insufficient participation of key stakeholders while conducting review meetings or workshops and proposed vigilance by the Ministry of Health. Only one subnational participant agreed to participate in developing guidelines for ICT in health, stating that “Yes, I have heard a chance to attend some of the meetings. Recently, I was part of the team that came up with the M&E health plan for the country” (W04, ICT Officer). This affects the usage and acceptability of the developed artefacts, hence limited use (and/or abandonment) to support healthcare processes. The limited participation in the standards development process has caused health facility-level stakeholders to have a limited understanding of the standards (W04, ICT Officer), which could lead to little appreciation of the DH standards to support secure health data exchange, and, therefore, potentially low ownership and uptake of the standards [44]. (iii) Limited sensitisation of facility-level users about the existence and need for DHCI standards: In fact, most users were not even aware of the standards for DH, as exemplified by a participant’s statement that “To be honest, guidelines are not actually, like I said followed because not every staff are even aware of the guidelines”—N11, Data Officer. Another participant even suggested that rather than just providing the guidelines, the MoH also need to train users. He said there is “also a need for training of people from down here not just developing the guideline or pushing the information down without training people” (N10, Medical Director). This suggests that health facility participants lack awareness, unlike at the national level, where participants could identify a few standards like DH security and privacy. In addition, there are inadequate ICT skills related to DH among healthcare professionals at health facilities (i.e., the officer In-charge health facilities, clinicians, nurses, laboratory technologists and pharmacists), both in terms of the numbers and skills mix/set. Furthermore, healthcare workers lack training, experience and expertise in the development and/or use of standards for DHCI. (iv) Inadequacy of the reviewed/updated standards: In addition to evidence that Uganda’s MoH has not officially adopted standards to support the electronic sharing and exchange of patient data specifically, one participant affirmed the importance of standards by saying, “Confidently, yes, the guidelines can cover that. However, they seem to be repeated guidelines. I have never seen it before, yet things have changed. They should be, maybe evaluated” (W10, Facility Administrator). This suggests significant improvement to existing guidelines,

State of Digital Health Communication Infrastructure in LMICs: Theory …

127

including updates catering to new technology trends and contextual environments. Besides, to allow widespread appreciation, authors encourage that standards should be written in simple language, structure, and format with clear guidelines ensuring that they are simple, clear and unambiguous [39, 41, 45, 46]. In addition, it should be easily accessible, readable and understandable by domain experts. Also, often there is delayed updating of DH standards “First of all, the updated one is not there” (N11, Data Officer). “…you see, the guideline sometimes may not state fully, and it may not cover everything…” (N11, Data Officer). These responses suggest that some of the standards are not to date. Therefore, they must be reviewed, published, and easily accessible to all healthcare stakeholders, including technology developers. (v) Poor documentation of DH standards for health facilities: Most participants stated there are no guidelines for DHCI. Examples of their statements include; “There are no guidelines” (N02, Medical Director); “There is a lack of guidelines on how to use ICT” (N06, Medical Director); “I have not seen some of the guidelines”—W10, Facility Administrator; and “No, they have not yet developed that” (C04, Facility Administrator) among others. In addition, sometimes, the standards are not exhaustive but provide a limited depth/breadth, leading to a limited understanding of the scope of standards. 4.3.3

Challenges that Relate to Standards Implementation

(i) Sometimes, the standards are difficult/confusing to understand for end-users: This suggests that some phrases in the standard may not be simple and clear enough to be comprehended by all. “At times, they are those with technical terms, but people like me are in place to help people understand the SOPs” (W04, ICT Officer). Therefore, the need to train/sensitise end-users at the facility level about their existence and how they should be followed. (ii) Limited awareness of standards for ICT being used in health: In fact, “people think that they are health workers and they are not interested in computers…you understand. Because somebody is a nurse, it’s about patients…patients. So, they don’t know that they need computers” (N12, Facility Administrator). This negative perception among healthcare workers may negate efforts to standardise DH systems in Uganda. (iii) Unreliable facilitating resources: resources like electricity required to power the DHCI devices and communication network hinder the standardised implementations of the systems. These and other challenges, such as limited finances and inadequate user skills, inhibit the implementation of DHCI. In addition, participants complained about insufficient supply from the main grid and that even where alternative sources/backups (generators, solar, UPS, etc.) exist, these cannot sustain the DH systems for a prolonged period.

128

4.3.4

A. E. Alunyu et al.

Challenges that Relate to Standards’ Compliance Monitoring

The identified gaps relate to the lack of governance structures to monitor compliance and tools for assessing compliance with DHCI standards and/or guidelines. The gaps include; (i) Lack of tool/mechanism that stakeholders have agreed upon to monitor compliance: “…there is no system which constantly monitors compliance” (N06, Medical Director). This lack of an agreed-upon mechanism suggests differentiated metrics for monitoring implementation and compliance with DH standards. (ii) The lack of an agreed-upon level of adherence: that constitutes compliance suggests results are dependent on the varied activities and convenience of the monitoring person (iii) Lack of DH standards’ monitoring structure: Results show that health facility respondents were not aware of the standards monitoring structure. This could mean that the implementation and compliance monitoring team structure and activities are unknown to respondents because they are non-existent. Overall, the study identified several gaps that hamper the establishment of a connected health system through a standardised DHCI. Consequently, participants in the survey identified several weaknesses (listed in Fig. 5) in existing implementations of the DHCI that stem from gaps in the standards development process, implementation lapses, and deficiencies in enforcing and monitoring compliance with the standards or guidelines. Figure 5 shows that more (26.1%) respondents mentioned the digital literacy gap. These DH technology users worry more about their capacity to use existing DHCI and applications effectively. Also, 15.2% of the respondents point to the problem of the electric power required to run the ICT devices and technologies. One participant further highlighted the magnitude of the skills and power problem when he said, “We were supplied with smaller computers with cameras where you can capture real-time Others Security Laxity System Attacks,Theft and Loss System/Data Migration challenge Slow computer systems Rigid system Power problem Poor maintainance Policy implementation gaps Network issues Lack full automation Limited ICT literacy Skills Lack of System Intergration Few devices Difficult to use

4.3% 2.2% 5.4% 1.1% 1.1% 1.1% 15.2% 7.6% 3.3% 9.8% 2.2% 26.1% 5.4% 2.2%

7.6%

Fig. 5 Challenges of DHCI that exist in Uganda’s health system (n = 46)

State of Digital Health Communication Infrastructure in LMICs: Theory …

129

data. Information flow made communication easier. But this died a natural death due to power and capacity of staff to use the system” (WN05, ICT Officer). This study shows that the challenges are similar across health facilities in rural and urban settings, exemplified by the responses from both settings. In addition, the challenges apply across the entire health system levels surveyed, i.e., HC IVs, District Hospitals and regional referral hospitals. However, some health facilities have no connection to the primary electrical power grid. Examples are participants/respondents N12(Facility Administrator) and WN07(a Data Officer), who said, “…the lack of electricity. Power is not there.” and “it has taken long since Amin’s time, there has been no power this side” respectively, the power problem at their health facilities. Other significant issues that emerged included network issues (9.8%), few ICT devices (7.6%), poor maintenance of the CI (7.6%), limited integration of DH applications (5.4%), security laxity leading to system attacks, theft, and loss (5.4%) and policy implementation gaps (3.3%) among others.

5 Discussion The main findings of this exploratory study (1) confirm practices identified by similar studies in LMICs regards DH standardisation and (2) elicit factors that affect the implementation of DHCI standards in resource-constrained settings like Uganda’s health system.

5.1 Practice of DHCI Standardisation in Uganda, an Example of Resource-Constrained Setting A key result from the study shows that Uganda has a standardisation process, although with gaps that limit its success in fast-tracking the already delayed standardisation of DHCI. As was shown in Sect. 2.2 of this chapter, these gaps are consistent with what other countries experience; both countries that have made progress in DH (Estonia, Australia, and New Zealand) and peer countries in the regions (South Africa, Kenya and Tanzania). This study refers to them as standardisation gaps and has developed three main categorisations: (1) gaps that relate to determining the need for a standard i.e., the context of the standards; (2) gaps in the standards formulation, and (3) weaknesses of the standards implementation process. The gaps persist because Uganda does not have a documented procedure for streamlining DH standardisation. Furthermore, countries that have made progress in DH standardisation have not provided resource-constrained countries like Uganda with foundational models or frameworks for contextualising, which could be used to fast-track the formulation of suitable DH standards [47]. Therefore, this study

130

A. E. Alunyu et al.

recommended structuring standardisation activities by developing a process or framework to aid less experienced standards developers to formulate suitable standards for their context. In addition, such a framework should be capable of empowering standards’ implementors to collaborate in monitoring the compliance of digital health stakeholders with the developed DHCI standards. Although, there was a framework, model or method to guide DH standardisation, limited stakeholder involvement in DH decisions and implementations, as reported in this study, might still entail failure. It has been claimed that the potential success of the protracted standards development process lies in stakeholder engagement that obeys the four cardinal principles (out of the 12 principles) of DH standards development, i.e., consensus-based, transparency and openness, balance, and due process [12, 28, 48]. Any process that satisfies these principles of standardisation is considered suitable to guide a domain’s standards development. Overall, stakeholders’ participation is critical to achieving all the above principles. Besides, stakeholder participation in standardisation gains knowledge, influence and network [48]. They gain knowledge of the upcoming standards and the underlying rationales; influence the upcoming standards and their requirements; and network with the other participants, often from different societal positions [48]. Although participants argued that DH governance at health facilities enforced the often-strict procedure of obtaining health data, there was a lack of clarity on structures for implementing standardised DHCI and monitoring compliance with the standards. There should be a strict mechanism that covers the full scope of DH implementation, starting from acquiring the DHCI, applications and technologies to secure the use of the DH systems to support HIE and patient-centred care. In addition, the study findings also show that stakeholders, especially facility-level stakeholders, are not involved in the current practice of standards implementation and compliance monitoring. Yet successful implementation and monitoring require stakeholder involvement.

5.2 Factors Affecting Implementation of DHCI Standards in Uganda The study conceptualised that factors that affect the implementation of any standard start with gaps in standards development through implementation and compliance monitoring. First, an inappropriate standardisation process has hampered the possibility of contextualising DHCI standards for Uganda. Despite the existing UNBS standardisation process, this study identified their weaknesses in fast-tracking the standardisation of DHCI. The existing process lacks a procedure for planning standardisation resources, training, measurement and evaluation plan. In addition, the existing DH environment is riddled with standardisation and other digitisation challenges that make standards development/adoption difficult for LMICs [16, 49]. Therefore,

State of Digital Health Communication Infrastructure in LMICs: Theory …

131

existing processes/methods are unsuitable for fast-tracking DHCI standardisation in LMICs like Uganda [37]. Other factors that affect the standardisation process include limited participation of LMICs in global standards development, insufficient human resources, inadequate technical infrastructure for standards participation and financial resources factors [37, 50, 51]. The lack of a suitable process failed Uganda in adopting or contextualising standards. Second the lack of officially adopted and contextualised standards for DHCI in Uganda’s health system means that existing standards are not clear, used on the Adhoc as designated by NITA-U for all government MDAs. This problem is complicated by limited knowledge of the existence of NITA-U standards/guidelines for the acquisition and use of ICT by government ministries and departments [52]. Third, on factors that affect the implementation of standards for the DHCI in Uganda’s health system, the study identified several gaps, including improper documentation, poor methods of disseminating the standards to health facilities and other standards implementers/users, delayed updating of standards, the complexity of the statements and specifications of standards, making them difficult to read or understand by end-users, limited awareness/enthusiasm over the adoption of standards for ICT to support healthcare processes, lack of tool/mechanism that stakeholders have agreed upon to monitor compliance, lack of agreed-upon level of adherence that constitutes compliance, and lack of guidelines for implementers to monitor themselves. Standards implementation gaps extend from its development to monitoring of compliance. For example, the lack of a standard means we cannot implement and enforce compliance with what is non-existent. An improperly structured standard fails the implementers because it cannot be properly interpreted and applied. Those who attempt to apply the standard interpret it differently and consequently, it is implemented.

6 Conclusion This study confirmed that existing processes are insufficient to fast-track DHCI standardisation and that several factors affect the implementation of existing standards for digital communications in Uganda. To ensure existing and future digital health information systems are effective in supporting patient-centred care and the general public, they must follow standards during data capture, processing, storage and sharing. Empirical results reveal factors that influence potential success in implementing DHCI standards extend conceptualisation, formulation, implementation and mechanisms to ensure compliant use, which this study refers to as standardisation gaps. We argue that for resource-constrained health systems to attain the benefits of digital health, they should address such standardisation problems. Our future works will address the identified gaps by adopting a rigorous process of determining

132

A. E. Alunyu et al.

DHCI standards and collaboratively planning their implementation and compliance monitoring.

References 1. A.E. Alunyu, R. Ssekibuule, J. Nabukenya, Towards adoption of standards for communication infrastructure/technologies in healthcare systems in LMICs: theories, practice and evaluation, in 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020), Valletta, Malta: SCITEPRESS (2020), pp. 735–744. https://doi.org/10.5220/ 0009171907350744 2. A. Gillwald, Communication Infrastructure (Wiley Publishing, 2008). https://doi.org/10.1111/ b.9781405131995.2008.x 3. ITU and UIT, World Telecommunication Development Conference (WTDC-17) (Buenos Aires, Argentina, 2017) 4. W. Yiming, B. Davood, N. Lars, Modeling of communication infrastructure compatible to Nordic 32 power system, in IEEE Power and Energy Society General Meeting (PESGM) (2016), pp. 1–5. https://doi.org/10.1109/PESGM.2016.7741521 5. A.E. Alunyu, R. Ssekibuule, J. Nabukenya, Status of resources for information technology to support health information exchange in resource-constrained settings, in 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020), Valletta, Malta: SCITEPRESS (2020), pp. 463–471. https://doi.org/10.5220/000897000463 0471 6. V.M. Kiberu, M. Mars, R.E. Scott, Barriers and opportunities to implementation of sustainable e-Health programmes in Uganda: a literature review. Afr. J. prim. Health Care Fam. Med. 9(1), 1–10 (2017) 7. A.E. Alunyu et al., Investigating the impediments to accessing reliable, timely and integrated electronic patient data in healthcare sites in Uganda, in The 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021), ONLINE STREAMING: SCITEPRESS (2021), pp. 522–532. https://doi.org/10.5220/001026 6705220532 8. M.A. Quddusi, ‘eHealth technologies: the benefits and challenges of e-health applications, in The Scientific World—Let’s Have a Moment of Science (2019). http://www.scientificwo rldinfo.com/2019/09/the-benefits-and-challenges-of-e-health-technologies.html. Accessed 09 Sep 2022 9. F. Huang, S. Blaschke, H. Lucas, Beyond pilotitis: taking digital health interventions to the national level in China and Uganda. Glob. Health 13, 49 (2017). https://doi.org/10.1186/s12 992-017-0275-z 10. ITU-T, E-health Standards and Interoperability (2012) 11. J. Tate, M. Panteghini, Standardisation–the theory and the practice. Clin. Biochemist. Rev. 28(3), 93–96 (2007) 12. ISO/IEC, Guide 59 ISO and IEC recommended practices for standardization by national bodies, vol. 2019 (2019) 13. D. Rowlands, A Health Interoperability Standards Development, Maintenance and Management Model for Australia (2020) 14. T.Y. Kim, S.A. Matney, Informatics-related standards and standards-setting organizations, in Health Informatics: An Interprofessional Approach, 2nd edn. (3251 Riverport Lane St. Louis, Missouri 63043: Elsevier Inc, 2018) 15. South African Department of Trade and Industry, National Norm for the development of South African National Standards. SABS Standards Division (2017). https://static.pmg.org.za/170 421DraftNationalNormforthedevelopmentof_SANS.pdf

State of Digital Health Communication Infrastructure in LMICs: Theory …

133

16. F. Adebesin, P. Kotze, R. Foster, D. Van Greunen, A review of interoperability standards in e-health and imperatives for their adoption in Africa. S. Afr. Comput. J. 50(1), 55–72 (2013) 17. CEN, ‘B.2.2. “Fast Track” standardization’, eLearning platform (2020). https://www.standa rdspluselearning.eu/b-2-2-fast-track-standardization. Accessed 27 Aug 2022 18. World Trade Organization, Principles for the Development of International Standards, Guides and Recommendations (2000). https://www.wto.org/english/news_e/news19_e/ddgaw_05d ec19_e.htm. Accessed 27 Aug 2022 19. PATH, Health Facility Standards Guide: Assisting stakeholders in creating or improving highquality, country-specific resources that outline health facility infrastructure and medical device standards (2020). https://path.azureedge.net/media/documents/ODT_Health_Facility_Standa rds_Guide_FINAL_10Aug2020.pdf 20. E. Ng, P. de Colombani, Framework for selecting best practices in public health: a systematic literature review. J Pub. Health Res. 4(3), 577 (2015). https://doi.org/10.4081/jphr.2015.577 21. E. Baskin, K. Krechmer, M.H. Sherif, The six dimensions of standards: contribution towards a theory of standardization. Manage. Technol. Sustain. Dev. Eco-Effici. 1998, 53 (1998) 22. V. Fomin, T. Keil, K. Lyytinen, Theorizing about standardization: integrating fragments of process theory in light of telecommunication standardization wars, in Sprouts: Working Papers on Information Environments, Systems and Organizations, vol. 3, no. 1 (2003), pp. 29–60 23. P.A. David, S. Greenstein, The economics of compatibility standards: an introduction to recent research. Econ. Innov. New Technol. 1(1/2), 3–41 (1990) 24. P. Godoe, T. Johansen, Understanding adoption of new technologies: technology readiness and technology acceptance as an integrated concept. J. Eur. Psychol. Stud. 3(1), 38–52 (2012). https://doi.org/10.5334/jeps.aq 25. H. Taherdoost, A review of technology acceptance and adoption models and theories. Procedia Manuf. 22, 960–967 (2018) 26. J. Baker, The Technology–Organization–Environment Framework (2011) 27. A. Hovav, R. Patnayakuni, D. Schuff, A model of Internet standards adoption: the case of IPv6. Inf. Syst. J. 14(3), 265–294 (2004) 28. B. Braaksma et al., Standardisation in the European statistical system, in Conference NTTS (2013) 29. J. Kern, Standardization in health and medical informatics, in Handbook of Research on Informatics in Healthcare and Biomedicine (IGI Global, 2006), pp. 44–50 30. K.A. Stroetmann et al., European countries on their journey towards national eHealth infrastructures (Office Official Publications of the European Communities, Luxembourg, 2011) 31. A.K. Hege, M.T. Vibeke, How to use qualitative interviews in e-health research, in eTELEMED 2015: The Seventh International Conference on eHealth, Telemedicine, and Social Medicine (2015), pp. 20–25 32. O.M. Mugenda, G. Mugenda, Research methods: quantitative and qualitative approaches. Res. Methods (2009) 33. Uganda National Bureau of Standards, What Are Standards? (2016). https://unbs.go.ug//con tent.php?src=what-are-standards?&content. Accessed 24 June 2020 34. A.E. Alunyu, D. Munene, J. Nabukenya, Towards a digital health curriculum for health workforce for the african region: a scoping review. J. Health Inf. Afr. 7(1), 38–54 (2020) 35. R. Pankomera, D. van Greunen, A model for implementing sustainable mHealth applications in a resource-constrained setting: a case of Malawi. Electron. J. Inf. Syst. Dev. Ctries. 84(2), e12019 (2018). https://doi.org/10.1002/isd2.12019 36. M.B. Spring, M.B. Weiss, Financing the standards development process. Stand. Policy Inf. Infrastruct. 289 (1995) 37. A.E. Alunyu, J. Nabukenya, A conceptual model for adaptation of ehealth standards by low and middle-income countries. J. Health Inf. Afr. 5(2), 10–16 (2018). https://doi.org/10.12856/ JHIA-2018-v5-i2-199 38. eHealth Ontario, Standards Selection Guide (Ontario, 2013) 39. D.B. Baker, J.B. Perlin, J. Halamka, Evaluating and classifying the readiness of technology specifications for national standardization. J. Am. Med. Inform. Assoc. 22, 738–743 (2015). https://doi.org/10.1136/amiajnl-2014-002802

134

A. E. Alunyu et al.

40. P. Reynolds, J.L. Berg, H. Schumny, An analysis of the information technology standardization process (North Holland Amsterdam, 1990), p. 433 41. M.B. Weiss, M.B. Spring, Selected intellectual property issues in standardization, in Information Technology Standards and Standardization: A Global Perspective (IGI Global, 2000), pp. 63–79. 42. J.P. Hopkinson, Development and Implementation of Security Standards 43. G. Robert, T. Greenhalgh, F. MacFarlane, R. Peacock, Organisational factors influencing technology adoption and assimilation in the NHS: a systematic literature review, in Report for the National Institute for Health Research Service Delivery and Organisation programme (2009) 44. W.F. Boh, C. Soh, S. Yeo, Standards development and diffusion: a case study of RosettaNet. Commun. ACM 50(12), 57–62 (2007). https://doi.org/10.1145/1323688.1323695 45. Electronic Communications Committee (ECC), Regulating Interoperability (2007). https:// docdb.cept.org/download/d558e1c6-a660/ECCREP107.PDF 46. M.B. Weiss, The standards development process: a view from political theory. Stand. View 1(2), 35–41 (1993) 47. E. Alvarez, J.N. Lavis, M. Brouwers, L. Schwartz, Developing a workbook to support the contextualisation of global health systems guidance: a case study identifying steps and critical factors for success in this process at WHO. Health Res. Policy Syst. 16(1), 19 (2018). https:// doi.org/10.1186/s12961-018-0297-x 48. N. Madelung, K.B. Andersen, An introduction to formal standardization and the work on ICT standardization in ISO/IEC–JTC1. J. ICT Stand. 1(1), 1–24 (2013) 49. K. Ndlovu, R.E. Scott, M. Mars, Interoperability opportunities and challenges in linking mhealth applications and eRecord systems: Botswana as an exemplar. BMC Med. Inform. Decis. Mak. 21(1), 246 (2021). https://doi.org/10.1186/s12911-021-01606-7 50. F. Adebesin, P. Kotzé, D.V. Greunen, R. Foster, Barriers & challenges to the adoption of E-Health standards in Africa, in South African Institute of Computer Scientists and Information Technologists (2013). http://sacj.cs.uct.ac.za http://researchspace.csir.co.za/dspace/han dle/10204/6910 51. W.E. Hammond, Standards for Global Health Information Systems (Academic Press, 2017), pp. 94–108 52. A. Kiwanuka et al., Establishing the state of practice about data standards in monitoring healthcare interventions for HIV in Uganda’s EMR-based health information systems. in 14th International Conference on Health Informatics (2021), pp. 200–211. https://doi.org/10.5220/001 0264302000211

Management of Healthcare and Medical Information Systems

Unpacking Privacy Calculus and Interplay of Data Privacy and Healthcare: Paths Towards Safeguarding Patient Empowerment Nazmus Sakib, Hari Sai Jogesh Veeramalla, Nisarga Allu Raghu Naidu, Fahim Islam Anik, Lakshman Reddy Vanga, Maria Valero, and Eklas Hossain

1 Introduction Health informatics, also known as health information systems, employs information technology to arrange and examine medical records to enhance patient care. Health informatics studies of the tools, techniques, and systems used in collecting, archiving, retrieving, and using information in health and medicine. Information and communication systems, computer technology, and medical terminology are examples of tools. Healthcare informatics gives electronic access to medical records to patients, physicians, nurses, hospital administrators, insurance providers, and health information techs [1]. They must keep and retrieve all pertinent patient data, medical procedures, and the final results. In addition, they develop communication protocols inside the infrastructure of the workplaces. Since the 1950s, computing and information technology have been essential to the healthcare and life sciences sectors. With the introduction of mobile computing, cloud platforms, machine learning, and analytics over the past ten years or more, the digital transformation of healthcare on the Internet has been explosive and quick. The delivery of healthcare services and the development of applications that link patients and healthcare professionals have all been made possible by the Internet. On top of cloud computing, mobile computing, N. Sakib (B) · H. S. J. Veeramalla · N. A. R. Naidu · L. R. Vanga · M. Valero Department of Information Technology, Kennesaw State University, Georgia, USA e-mail: [email protected] F. I. Anik Department of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh E. Hossain Department of Electrical and Computer Engineering, Boise State University, Idaho, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Daimi et al. (eds.), Current and Future Trends in Health and Medical Informatics, Studies in Computational Intelligence 1112, https://doi.org/10.1007/978-3-031-42112-9_7

137

138

N. Sakib et al.

2018-2025 Data-Compound Annual Growth Rate (CAGR) Global Data Sphere Media & Entertainment Financial Service Manufacturing HealthCare

0%

8%

16%

24%

32%

40%

Fig. 1 a Compound Annual Growth Rate (CAGR) data (2018–2025) [5]

the Internet of Things (IoT), and wearable devices dispersed beyond geopolitical and socioeconomic borders, digital healthcare ecosystems have developed [2]. It is impossible to overstate the value of data analytics in healthcare. Vast amounts of operational and clinical data are now available to the healthcare ecosystem, opening new avenues for relevant clinical intelligence. The rapid adoption rate of healthcare analytics is driven by the need to manage large and varied data sets, shifting legal requirements, and rising innovation in fields like population health management, value-based care, and precision medicine. Healthcare analytics skills are heavily used by organizations to support innovative procedures and value-based care plans [3]. “Every day, 750 quadrillion bytes of data are created in health care,” according to a recent statement from the NIHCM Foundation [4]. By tracking, analyzing, and storing this vast quantity of data about humans, new gadgets, applications, and monitoring technologies are digitizing people. The smartphone in your pocket, cloud computing, artificial intelligence, and the wearable on your wrist are all excellent examples. Healthcare data is created at exponentially pace every second and is mined for insightful information. The healthcare sector now creates the global data volumen at about 30%. The compound annual growth rate of healthcare data will be36% by 2025. This is 6% quicker than the industrial sector, 10% quicker than the financial sector, and 11% quicker than the media & entertainment sector, as shown in Fig. 1a [5]. The volume of data generated by the healthcare industry every second is virtually unfathomable. Consider the Statista prediction from 2018 that global data production might reach 2,314 exabytes by 2020. Healthcare was anticipated to create 2,314 exabytes before Covid-19 was on anyone’s radar, according to cloud storage service Backblaze. It’s safe to assume that the estimate was low and that healthcare is now generating even more data than the organizations responsible for protecting it anticipated or was prepared to handle after a record-breaking year for telehealth adoption, contact tracing, outbreak tracking, virus testing, remote work, and medical research [6]. The most common method of recording healthcare activities is through electronic health records (EHR). Despite the fact that the development of EHR began many years ago, there are still active research efforts related to it. Searching PubMed for “electronic health record” reveals an increase in articles from year to year up until the present.

Unpacking Privacy Calculus and Interplay of Data Privacy …

139

Therefore, it is essential to have robust data privacy regulations in place to safeguard patient privacy and maintain trust in the healthcare system. This chapter focuses on four core topics: ● Understanding patient data and its’ uses. ● Data privacy implications centered around the current modern healthcare landscape. ● Privacy calculus hinders patient empowerment and hampers healthcare management efficacy collectively in the healthcare ecosystem. ● Healthcare regulations to address privacy calculus. Understanding patient data and its uses is essential in the modern healthcare landscape, as it forms the foundation for effective healthcare management and personalized treatment plans. Patient data encompasses a wide range of information, including medical history, test results, imaging scans, genetic data, and lifestyle factors. By analyzing this data, healthcare providers can gain valuable insights into a patient’s health condition, identify potential risk factors, and make informed decisions regarding diagnosis and treatment options. However, the collection and utilization of patient data raise significant data privacy implications. With the advancement of digital health technologies and electronic health records, there is an increased risk of data breaches and unauthorized access to sensitive patient information. This brings us to the second core topic discussed in this chapter: data privacy implications centered around the current modern healthcare landscape. The modern healthcare landscape is characterized by extensive data sharing and interoperability, which raises concerns about patient privacy. Health data is valuable and can be exploited for various purposes, including targeted marketing, insurance discrimination, or even identity theft. patient privacy becomes crucial to maintain trust and ensure the secure exchange of healthcare information. It necessitates the implementation of robust data security measures, access controls, encryption, and compliance with data protection regulations. One key challenge in achieving effective data privacy is privacy calculus. Patients often face the dilemma of balancing the potential benefits of sharing their personal health information with the risks of privacy breaches or misuse. Privacy calculus hinders patient empowerment as individuals may withhold vital information due to privacy concerns, leading to incomplete or inaccurate medical histories. This hampers healthcare management efficacy collectively in the healthcare ecosystem, as accurate and comprehensive data is vital for making informed decisions, coordinating care, and improving patient outcomes. To address privacy calculus and protect patient privacy, healthcare regulations play a crucial role. Governments and regulatory bodies have enacted laws such as the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. These regulations establish guidelines for data protection, consent requirements, breach notifications, and the rights of patients regarding their personal health information. By enforcing these regulations and fostering a culture of privacy and data security, healthcare organizations can build trust with patients and ensure responsible data handling practices. It is note that this chapters’ focus is limited to US healthcare system and solutions developed by US regulators.

140

N. Sakib et al.

2 Understanding Patient Data and Its’ Uses 2.1 Clinical Uses of Patient Data Electronic Health Records are digital representations of a patient’s medical history. The health record is collected, made, and stored electronically in an electronic health record. The electronic health record will enhance the clinical recording, quality control, monitoring of healthcare usage, billing and coding, and portability of health information. Achieving greater healthcare efficiency, significant improvements in quality and safety, and reduced consumer healthcare costs are all advantages of an electronic health record [7]. EHRs in the US were first created primarily to assist clinical workflow, billing, and personal health data may be used for purposes beyond providing direct patient care, such as research and education, in accordance with the secondary use of EHRs. Ironically, the major reason medical records were first retained was for didactic purposes, which is what is referred to as “secondary use.“ Research can bring up medical ethical, political, technological, and social challenges since these reasons were not the primary motivation for data generation and because the EHR contains sensitive information [8]. The use of EHR in research is fraught with difficulties, including those related to data validity and quality, comprehensive data capture, system heterogeneity, and system expertise. EHRs and the ability to exchange health information electronically can help you provide higher quality and safer care for patients [9]. Here are a few listed below: A. Benefits for Clinical Care: • Access to complete medical histories, lab results, and medication information enables informed clinical decisions. • Seamless sharing of patient information enhances care coordination and treatment planning. • Availability of comprehensive patient data aids in accurate diagnoses and reduces medical errors. B. Benefits for Institutions: • Streamlined coding and billing processes improve accuracy, efficiency, and financial outcomes. • Incorporation of security measures ensures patient data confidentiality and reduces the risk of data breaches. • Automation of administrative tasks, such as appointment scheduling and prescription refills, frees up time for healthcare providers, promoting improved work-life balance. C. Benefits for Patient Population: • Increased time spent with patients leads to improved quality of care and patient satisfaction. • Reduction in paper-based records decreases errors and unnecessary duplicate tests, resulting in cost savings for healthcare institutions and better access to care for patients.

Unpacking Privacy Calculus and Interplay of Data Privacy …

141

D. Benefits for Clinicians and Professionals: • Streamlined workflows and automation of routine tasks increase productivity and support the achievement of institutional goals. • Advanced data processing enables novel investigations such as analysis of disease comorbidities, drug-to-drug interactions, and patient medication compliance monitoring, improving understanding and treatment outcomes. Electronic health information exchanges may be divided into three categories: directed exchanges, query-based exchanges, and consumer-mediated exchanges. This provides doctors with important information about a patient’s medical history, previous and present prescription medications, allergies, family history, and other pertinent data that may be needed to diagnose and treat a patient properly. They are goal-oriented and patient-centered. Conversely, EHRs are transactional and visit catered. Despite these differences, EHRs gather a substantial amount of data that is relevant to patient registries. Just a few of the duties that EHRs may assist with include data gathering, data purification, and data storage. The data collected in an EHR might be more valuable if there is a patient registry. For more than 20 years, there has been interest in using EHRs as an electronic data source (eSource) for traditionally controlled randomized clinical research. There are many advantages of and applications of EHR, as shown in Fig. 2. A wide variety of other electronic data sources, like as patient-reported outcomes, diaries, and wearable devices, are included in the term “eSource,” which refers to data sources in electronic form [10]. It also includes the reuse of EHR data. The majority of doctors who use EHRs claimed that their use has enhanced patient care in general (78%) and made it simpler for them to access a patient’s information from a distance (81%), as well as alerting them to probable prescription mistakes (65%) and significant test results (62 percent). Between 30 and 50 percent of medical professionals asserted that the use of an EHR was associated with therapeutic benefits, such as providing suggested therapy, directing the appropriate testing, and improving patient communication [11]. Electronic Health Records (EHRs) cannot be overemphasized as invaluable patient data repositories within the domain of clinical research due to their numerous advantages and applications. Nevertheless, it is essential to recognize that the longevity and utility of collected patient data are intricately intertwined with time. Initially, one of the primary purposes of EHR data is to meticulously document and preserve patients’ complete medical histories. This attention to detail provides healthcare professionals with continuous access to a plethora of information, thereby facilitating the delivery of comprehensive care and treatment. Second, clinical data collected in conjunction with particular episodes, such as those related to diagnosis and monitoring, may ultimately be discarded or consolidated. Nonetheless, this information considerably contributes to the historical context of patient records, enhancing the overall richness and profundity of the collected data. Thirdly, data collection within EHRs may be motivated by distinct research objectives or regulatory requirements, which are frequently an integral part of clinical trials. In such instances, the data’s usefulness extends over a predetermined period of time, necessitating careful maintenance and periodic evaluation. The clinical observations documented

142

N. Sakib et al.

Fig. 2 Applications of EHR

and monitored in EHRs play a crucial role in epidemiological studies designed to screen and monitor population health. Consequently, ensuring the efficacy and precision of these studies requires the long-term storage of these data. Clearly, the longevity and utility of patient data within EHRs depend on the particular contextual factors and objectives at hand. The importance of time in determining the worth of EHR data cannot be overstated, emphasizing the need for prudent management and interpretation.

2.2 Consumer Uses of Patient Data The utilization of patient data, both in clinical and consumer contexts, holds significant importance in the healthcare industry. On the one hand, clinical uses of patient data enable healthcare professionals to make well-informed decisions regarding diagnosis and treatment. Access to comprehensive medical history, symptoms, laboratory results, imaging, and other relevant data allows for accurate assessment and improved patient outcomes. On the other hand, consumer uses of patient data empower patients

Unpacking Privacy Calculus and Interplay of Data Privacy …

143

to play an active role in their healthcare journey, by enabling them to make informed decisions about their care and take measures to better manage their own health. By leveraging both clinical and consumer uses of patient data, healthcare providers can enhance patient engagement, improve overall patient satisfaction and drive positive healthcare outcomes. The Consumer Health Informatics Working Group of the American Medical Informatics Association and the Nursing Informatics Interest Group of the International Medical Informatics Association both define a health information consumer as someone who looks for information on health promotion, disease prevention, treatment of specific conditions, and management of various health conditions and chronic illnesses [12]. Healthcare consumer choice has been promoted as a means of raising the standard and cost of healthcare. The idea is that people will be able to make the best decisions for themselves and their families when they have access to complete and accurate information about the quality and cost of health insurance plans, physicians, hospitals, and services, as well as appropriate tools for using that information as shown in Fig. 3 [13]. Individuals who have access to their electronic health records can utilize cutting-edge software to better manage their health and act as information brokers between healthcare professionals [14]. A most recent article states that, “According to the 2022 Health Care Insights Study, customers want more intimate, tailored interactions with providers that produce holistic results.” According to CVS Health President and CEO Karen S. Lynch, “The pandemic impacted practically everything about our environment, including the way many people see the significance of their health.” According to the study’s findings, both patients and healthcare professionals believe that better participation and communication lead to better health outcomes. 81% of consumers believe it is crucial that their primary care provider be aware of their patient’s overall happiness and life satisfaction levels, as well as how they cope with challenging emotions and stress, whether their aim is to reduce daily stress levels or improve overall well-being [15]. When quality and pricing information is linked to incentives and presented in an easy-to-understand way, patients are more likely to behave like educated customers as seen in Fig. 4. Even among people with chronic diseases, recent polls reveal that just a quarter are aware that information like that contained in quality report cards is available to them, and only one in ten utilize such information to compare hospitals or doctors. Additionally, patients may now get estimates of their real projected out-of-pocket expenses that consider the design of their health plans’ benefit programs and their out-of-pocket expenses for the year to date (for example, whether or not they have satisfied their deducible) [13].

2.3 Uses of Patient Data in Research and Analytics The healthcare industry places significant importance on the utilization of patient data in both clinical and consumer contexts. Any form of patient data is hugely valuable for research. The patient data in research and analytics can be used to improve individual care, understand more about disease risks and causes, improve diagnosis,

144

N. Sakib et al.

Fig. 3 Well-informed consumer choices leading to Higher-Value health care in the US [13]

Fig. 4 Shift of traditional medical informatics from health professionals to consumers [16]

develop new treatments and prevent disease, and improve patient safety [17]. Patient records are a very significant research resource. The results help researchers identify approaches to improve the efficiency of clinical processes and other healthcare operations. The potential of data analytics to improve cardiovascular quality of care and patient outcomes is tremendous and can improve cardiovascular care. The data may also be used to evaluate the effectiveness of treatments and interventions in situations when performing a clinical trial is not practical, such as monitoring the safety of using prescription medicine during pregnancy. The rising usage of electronic records provides opportunities to address new research questions by integrating data from extremely large numbers of patients and linking diverse datasets [18]. Analyzing patient data can lower readmission rates, reduce errors and better identify at-risk populations. Data analytics is used in health business management to identify staffing issues and recruit, hire, train, and retain healthcare workers. Data analytics is being applied to manage labor costs in healthcare settings while simultaneously improving the quality-of-care patients receive and the efficiency of service provision. Clinical

Unpacking Privacy Calculus and Interplay of Data Privacy …

145

data is either collected during the course of ongoing patient care or as part of a formal clinical trial program [17]. Different types of clinical data are summarized below: ● Electronic health records: EHR, often referred as EMR (Electronic Medical Record).This data collected includes administrative and demographic information, diagnosis, treatment, prescription, drugs, laboratory test, psychologic monitoring data, hospitalization and patient insurance [19]. ● Administrative Data: Often associated with HER, these are primarily discharge data reported to the government. ● Claims Data: Consists of Insurance claims between insured patients and healthcare delivery system. ● Patient / Disease Registries: This keeps track of a narrow range of key data for certain chronic conditions such as Cancer, Diabetes, Heart Disease and Asthma [20]. ● Health Surveys: To provide an accurate evaluation of the population’s health, national surveys of the most common chronic conditions are generally conducted to provide prevalence estimates. National surveys are one of the few types of data collected specifically for research purposes. ● Clinical Trials Data: Information on publicly and privately supported clinical studies from around the world. ● Vital Records: These records are maintained by state and local governments, which include births, deaths, marriage, divorce and fatal deaths, cause of death and details of birth. ● Surveillance: This is an ongoing systematic collection, analysis and interpretation of data closely integrated with timely dissemination of these data to those responsible for preventing and controlling disease and injury. WHO and many other institutions operate databases and automated electronic reporting systems to track and monitor outbreaks of specific diseases, like HIV. As already discussed, the modern healthcare landscape has revolutionized how healthcare is delivered, making it more efficient, accessible, and personalized. However, it has also introduced significant data privacy implications. Healthcare providers, insurance companies, and other entities collect enormous quantities of sensitive patient data that can be used for various purposes. This data includes medical records, confidential information, and financial information. The increasing use of electronic health records (EHRs), wearable devices, telemedicine, and mobile health applications, also adds to the danger of data breaches, hacking, and unauthorized access. The potential misuse of this data can lead to discrimination, financial fraud, identity theft, and reputational harm.

146

N. Sakib et al.

3 Data Privacy Implications Centered Around Current Modern Healthcare Landscape 3.1 Impact of Big Data in Patient Empowerment and Improving Healthcare Management efficacy in Healthcare Ecosystem Integrating and analyzing of different types of data generated within the healthcare ecosystem forms the basis of big data analytics, which provides valuable insights into disease patterns, treatment effectiveness, and patient outcomes. Large healthcare institutions like hospitals may use big data to acquire a comprehensive picture of the patient experience. With the use of big data technologies, care teams can now aggregate data previously scattered among several clinics, hospitals, and specialist offices. Big data may be able to aggregate patient data, facilitating swift and accurate communication among healthcare experts. Large healthcare institutions like hospitals may use big data to acquire a comprehensive picture of patient experience. With the use of big data technologies, care teams are now able to aggregate data that was previously scattered among several clinics, hospitals, and specialist offices. Big data can integrate patient information, allowing patients and physicians to communicate quickly and precisely while being informed by the patient’s whole medical history [21]. It has traditionally been expensive and time-consuming to gather significant amounts of data for use in medical applications. The most advanced technology available today can electronically collect data and put it into an easily readable format [17, 22]. Healthcare professionals today have a choice of options for creating data-driven healthcare solutions to enhance patient outcomes: ● Enabling patients with easily accessible medical records to interact with their own health histories. ● Giving providers updates on patients’ health so they can more quickly evaluate treatment options saving the patients’ money and time. ● By reducing administrative procedures and assisting managers in making knowledgeable decisions about the distribution of cash and resources both inside and between healthcare institutions, we can increase access to high-quality care. ● Using data-driven results to identify and address medical problems more quickly.

3.2 Burgeoning Growth of Data Comes with Serious Privacy Threats The unprecedented growth of data in various fields, including healthcare, has brought forth significant privacy threats that need to be addressed. Clinical, operating, and financial models are changing dramatically and fundamentally due to the digitalization of health and patient data, and this change will continue for the foreseeable future.

Unpacking Privacy Calculus and Interplay of Data Privacy …

147

Healthcare institutions discovered that a bottom-up, reactive strategy for setting security and privacy needs is insufficient to safeguard the institution and its patients [23]. At the recent 2017 Annual Meeting of the American College of Medical Genetics and Genomics (ACMG), Sophia Genetics, a leader in data-driven medicine, declared that African hospitals had adopted its artificial intelligence to improve patient care on the continent, following regions in Europe, Canada, Australia, Russia, and Latin America [24]. As new SOPHIA customers, they join a broader network of 260 institutions in 46 countries that exchange clinical insights regarding patient cases and patient demographics. This knowledge base of biomedical results fuels the acceleration of diagnoses and care. Automations have improved patient care efficiency and reduced costs, but as healthcare data has increased, the risk of security and privacy breaches has also increased [25]. The audit also uncovered the following: ● 325 significant PHI breaches that exposed 16,612,985 individual patient information. ● 3,620,000 patient records were compromised in the single worst incident of the year. ● Unauthorized access or disclosure was implicated in 40% of significant breach events.

3.3 Major Data Privacy Implications Centered Around Current Modern Healthcare Landscape The modern healthcare landscape is increasingly prone to data privacy implications, including unauthorized access, data breaches, and exploitation, which threaten the privacy and security of patients’ sensitive medical information. The term “data privacy implications” refers to the possible repercussions or effects of the processing, acquisition, storage, sharing, and use of personal or sensitive data. The introduction of new technologies, the implementation of data protection regulations, data breaches, and the actions of organizations and individuals handling data can all have such consequences. The implications of data privacy emphasize the significance of safeguarding personal data and respecting individuals’ privacy rights. There are different types of cybersecurity incidents or attacks that can occur in the context of healthcare systems or organizations with implications for data privacy.

3.3.1

Snooping Attack that Lead to Compromised Patient Data

A snooping attack occurs when a person or computer effectively assumes the identity of another by fabricating data in order to obtain an unfair advantage. This type of assault violates the privacy of patients directly by allowing unauthorized individuals to access and potentially misuse sensitive personal health information. It

148

N. Sakib et al.

undermines data privacy rights and can cause significant damage if the compromised information is used for identity theft, fraud, or other malevolent purposes. [60] Dental Care Alliance, a third-party vendor, has lately started informing hundreds of its customers that a nearly one-month-long system attack may have compromised 1 million patients’ protected health information and payment card data. More than 320 linked practices in 20 states are served by DCA, a vendor that offers practice support services. After being reported, the breach has overtaken the Blackbaud ransomware assault as the second-largest event to affect the healthcare industry in 2020. The inquiry is ongoing, and DCA is still reviewing the data that was impacted by the incident. The letter claims that on October 11, DCA personnel discovered unusual behavior within its surroundings and immediately started an investigation with the aid of outside forensics experts. From September 18 to October 13, according to the first investigation, hackers accessed the company’s network. The potentially compromised information may include patient names, contact information, dental diagnoses, treatment information, patient account numbers, billing information, dentist names, bank account numbers, and health insurance information. According to the lawsuit, DCA failed to implement the reasonable cybersecurity policies and standards required to safeguard consumers’ protected health information, which led to the breach [26, 27].

3.3.2

Direct Patient Data Breach

When information is taken from a system without the owner’s knowledge or consent, the situation is referred to as a data breach. A data leak might affect both small and large businesses. Credit card numbers, customer data, trade secrets, and information pertaining to national security are just a few examples of sensitive, proprietary, or confidential information that may be included in stolen data [28]. The largest eyeglasses company in the world, luxottica, had many of its online portals attacked in a hack that exposed the personal information of over 800,000 patients. Many well-known eyewear companies, like ray-ban, are owned by luxottica, which also produces designer eyewear for numerous well-known fashion labels. The appointment scheduling service was breached on August 5, 2020, according to a breach notice released by luxottica, and the cybercriminals may have been able to access the personal and protected health information of patients of its business partners. The issue was discovered by luxottica on august 9, 2020, and immediate action was taken to stop future unauthorized access. The inquiry revealed that the hackers may have accessed and stolen personal and sensitive health information [29]. Names, contact information, appointment dates and times, health insurance policy numbers, appointment notes, doctor’s notes, and information related to eye care treatment, such as medical history, diagnostic tests, and prescriptions, were among the many types of data that may have been exposed. The attackers get patient and consumer data from the appointment scheduling program and system [26, 27].

Unpacking Privacy Calculus and Interplay of Data Privacy …

3.3.3

149

Poor Encryption Led to Data Breach

Inadequate encryption practices can expose patient data to unauthorized access, jeopardizing data confidentiality. If encryption mechanisms are insufficient or inadequately implemented, assailants may be able to circumvent encryption and obtain access to sensitive patient data, putting patient privacy and confidentiality at risk. There are several incidents to support the claim. About 654,000 present and past members of Oregon’s Medicaid coordinated-care group, the health share of Oregon, have been informed that some of their protected health information (PHI) was saved on a laptop that was stolen from its transportation partner, gridworks. Gridworks was hired to oversee the health share ride-to-care program, which gave participants non-emergency transportation. There was no encryption on the Gridworks laptop. As a result, PHI was saved on the laptop computer, including names, addresses, phone numbers, dates of birth, health share ids, Medicaid numbers, and social security numbers. In November 2019, a break-in at the Gridworks office resulted in the theft of a laptop. On January 2, 2020, gridworks informed health share that a laptop had been stolen. Beginning on February 5, health share began notifying everyone whose phi was saved on the laptop. A complimentary year of credit monitoring and identity theft protection services has been made available to those who are affected. Health Share will extend its vendor security assessment program in reaction to the compromise, and measures have been made to guarantee that only the absolute minimal amount of patient information is communicated to its suppliers. Additionally, training policies have been improved. According to the breach site, 654,362 people were harmed by the hack, as seen in Fig. 5 [30].

Fig. 5 Different potential healthcare data sources vulnerable to hacking [30]

150

3.3.4

N. Sakib et al.

Ransomware Attack

An organization or user’s access to files on their computer might be restricted by ransomware, a type of virus. Cyber attackers lock up these data and demand a ransom in exchange for the decryption key, putting businesses in a situation where paying the ransom is the quickest and least expensive method to get back access to their files as seen in Fig. 6 [31]. Inadequate encryption practices can allow unauthorized access to patient data, jeopardizing data confidentiality. If encryption mechanisms are insufficient or poorly implemented, attackers may be able to circumvent encryption and gain access to sensitive patient data, placing the privacy and confidentiality of patients at risk. According to a top-class actions post from august 15, Florida orthopedic institute, a Tampa-based company, has agreed to pay $4 million to resolve claims that it did not adequately safeguard customers from a 2020 ransomware assault. 640,000 patients were first informed by the clinic in June 2020 that a data breach may have exposed their protected health information. Two months prior, ransomware attacks on the practice’s servers’ encrypted data allowed hackers to access a server and get access. Florida orthopedic institute claims that the hackers may have accessed names, birthdates, social security numbers, medical information, insurance information, and other health information. On June 30, 2020, a class action lawsuit was filed alleging that the clinic had improperly secured protected health information. The lawsuit claimed $99 million on behalf of the patients and former patients impacted by the breach. Despite agreeing to set up a $4 million compensation fund to resolve the charges, Florida Orthopaedic Institute has not admitted any wrongdoing [32].

Fig. 6 Ransomware attack on users [31]

Unpacking Privacy Calculus and Interplay of Data Privacy …

3.3.5

151

Malware Led to Ransomware and Data Breach

Malware assaults include any form of malicious software intended to harm or destroy a computer, server, client, computer network, and/or infrastructure without the knowledge of the end user [33]. Malware can infect networks and devices and is made with the intention of negatively affecting those devices, networks, and/or their users. Eight Magellan health affiliates and healthcare organizations reported breaches resulting from the event to the Department of Health and Human Services this week, revealing the scope of the ransomware assault that affected Arizona-based Magellan health in April. 365,000 patients were reportedly impacted, according to the breach reporting tools. The Fortune 500 business was allegedly the target of a sophisticated cyberattack in April, during which hackers stole data before releasing the ransomware payload. The attackers were able to enter the system five days before the ransomware assault by using a social engineering phishing operation that pretended to be a Magellan customer. According to the inquiry, the system that was compromised was initially compromised by malware that could capture employee login information and passwords. Health-related information such as account information for health insurance plans and details about treatments were among the patient data that was exposed during the incident. The assault was limited to a single business computer, compromising sensitive patient data like Social Security and W-2 numbers, taxpayer and employee ids, and a wealth of current personnel information [34–36].

3.3.6

Data Breaches by Illegal Access

Infringing on patients’ privacy rights, illegal access to healthcare systems enables unauthorized individuals to access patient data. Sensitive patient information may be exposed when data breaches occur due to unauthorized access, leading to potential privacy violations and the misuse of personal health data. A data breach that exposed some of the patients’ personal information in September was recently reported to aspen pointe in Colorado Springs by 295,617 of its patients. The supplier of mental health services found that between September 12 and 22, illegal access had been made to its network. Aspen Pointe launched an inquiry after learning that the issue resulted in the exposure of patient data, including names, dates of birth, social security numbers, Medicaid id numbers, and illness codes. The security incident, which Aspen pointe notified HHS on November 19, affected 295,617 people [37]. In addition to a $1 million insurance reimbursement policy, the healthcare provider is providing impacted individuals with 12 months of free identity theft treatments.There haven’t been any reports of identity theft, fraud, or inappropriate use of patient information, according to Aspen pointe’s alternative breach notice, and there hasn’t been any proof that any patient data was taken by the attackers. The office for civil rights of the department of health and human services received the breach notification, which states that the assault may have exposed the protected health information of 295,617 individuals. After the incident, aspen pointe conducted password modifications, boosted monitoring, and rolled out firewall improvements to improve security [38].

152

3.3.7

N. Sakib et al.

Phishing Emails Lead to Unauthorized Access

When hackers send harmful emails intended to deceive recipients into falling for a scam, this is known as phishing. Usually, the goal is to entice consumers to divulge sensitive information such as system logins, financial information, or other data [39]. Infringing on the privacy rights of patients, illicit access to healthcare systems allows unauthorized individuals to access patient information. Data breaches caused by unauthorized access may expose sensitive patient information, leading to potential privacy violations and misuse of personal health data. Three of BJC healthcare’s workers replied to phishing emails, and as a result, an unauthorized person gained access to their email accounts, the company has said. The email accounts were promptly locked once suspicious behavior was found in them on March 6, 2020. An examination was carried out by a reputable computer forensics company, and it turned out that the three accounts had only been briefly visited on March 6 overall. There was no way to know if the attacker had accessed or gotten their hands on patient data. Upon closer inspection, it was discovered that the accounts held information on patients from 19 BJC and related institutions. Depending on the patient, protected health information in emails and attachments might have been included [40]. On May 5, 2020, the non-profit hospital system in St. Louis informed the HHS office for civil rights of a hack of its email system that affected 287,876 people. Three email accounts were hacked in March 2020 as a consequence of answers to phishing emails, according to the investigation [41]. Table 1 summarizes different incidents pertaining to data breaches.

4 Privacy Calculus: Impacts on Patient Empowerment and Healthcare Management Data privacy is an essential patient concern because it directly pertains to the preservation and management of their personal health information. However, the understanding and perception of data privacy by patients can vary. While some patients may have a strong grasp of data privacy and its implications, the vast majority may not be completely informed or aware of the specifics surrounding the use and preservation of their data. This lack of cognizance may be the result of a number of factors, including limited access to information, the complexity of privacy policies, and inadequate communication from healthcare providers [49]. To ensure patients understand data privacy, healthcare organizations and regulatory bodies employ various guidelines and practices. These include: 1.Informed Consent: Patients are typically required to provide informed consent, which entails understanding how their data will be collected, utilized, and disseminated. This procedure permits patients to determine the seclusion and use of their data [50]. 2. Privacy Policies: The privacy policies of healthcare organizations define how patient data will be handled, including data acquisition, storage, sharing, and protection procedures. These policies should

Unpacking Privacy Calculus and Interplay of Data Privacy …

153

Table 1 Different incidents pertaining to data breaches Incidents

Types of breaches

Information compromised

Dental care alliance

Snooping attack

Have compromised 1 million patients’ protected health information and payment card data [42]

Luxottica

Direct patient data breach

The attackers get patient and consumer data from the appointment scheduling program and system [43]

Oregon’s rthopae coordinated-care group

Poor encryption led to data According to the breach site, breach 654,362 people were harmed by the hack [44]

Florida rthopaedic institute

Ransomware attack

Exposed their protected health information of 640,000 patients [45]

Magellan health

Malware and Ransomware attack

Sensitive patient data like Social Security and W-2 numbers, taxpayer and employee ids [46]

Aspenpointe

Data breach by illegal access

Affected 295,617 people, exposure of patient data, including names, dates of birth, social security numbers, Medicaid id numbers, and illness codes [47]

BJC health system

Phishing mails lead to unauthorized access

Affected 287,876 individuals and compromising information of treatments, medications, Social Security numbers, and health insurance data, among other sensitive information [48]

be readily accessible and written in plain English. 3. Notice of Privacy Practices: Patients are frequently given a Notice of Privacy Practices that explains their rights regarding their data and how the healthcare provider will manage their information. It includes information about data exchange, individual rights, and privacy protection measures. 4. Education and Communication: Patients must be educated about data privacy by healthcare organizations and professionals. To ensure patients are well-informed about how their data is used and protected, they should communicate privacy practices, respond to patient queries, and provide educational materials. 5. Data Breach Notifications: In the event of a data breach that may compromise patient data, healthcare organizations are required to notify affected individuals. This helps patients understand the risks to their privacy and encourages them to take appropriate measures to protect themselves [50]. Preserving the privacy of patient data empowers individuals in several ways: 1. Control over Personal Information: Patients feel more in control of their personal health information when they have confidence in the security measures encircling

154

N. Sakib et al.

their data. This control enables them to make informed judgments regarding the sharing and protection of their data [51]. 2. Trust in Healthcare Providers: Privacy protection inspires confidence in healthcare providers and the healthcare ecosystem as a whole. Patients who trust that their data will be managed responsibly are more likely to communicate openly with their healthcare providers and share accurate information, resulting in improved healthcare outcomes. 3. Personalized Care and Treatment: By preserving data privacy, patients can feel more comfortable disclosing sensitive health information. This enables healthcare providers to have a comprehensive understanding of their patients’ health profiles, leading to personalized care and treatment plans that better address individual needs. 4. Research and Innovation: Patients who comprehend the significance of data privacy may be more willing to contribute their data to research studies. When patients have confidence that their data will be protected, anonymized, and used responsibly, medical research and innovation can advance. Furthermore, patients’ awareness of what they are consenting to and the potential hazards associated with their data is a crucial component of any discussion regarding data privacy. In many instances, patients consent to the release of their data for specific purposes or to accomplish specific goals, such as participation in a research study or receiving personalized treatment. However, it is essential to recognize that unintended discoveries or uses of their data may occur, raising privacy concerns. For instance, the analysis of patient data may inadvertently disclose information outside of the intended scope, such as the patient’s location while connected to a cardiac monitor. Institutional ethics committees and regulatory frameworks frequently have in place guidelines and safeguards to prevent such misappropriations of patient data. These safeguards are designed to ensure that data acquisition, analysis, and disclosure adhere to ethical standards and respect patient confidentiality. It is essential to note, however, that these safeguards may not always address all potential hazards or accidental information disclosures. Informing patients about the potential risks and unintended consequences associated with their data is essential for maintaining transparency and nurturing patient confidence. As part of the informed consent process, healthcare organizations and researchers are obligated to convey these risks in a manner that is effective, explicit, and easily understood. This includes informing patients of the categories of data being collected, the purposes for which it will be used, and any unintended uses or disclosures that may occur during data processing or analysis. Nonetheless, it is essential to recognize that patients’ comprehension of the technology used to process their data and their privacy expectations may be limited or misinformed. This may be due to the complexity of the involved technologies, the use of technical jargon, or a dearth of patient-accessible educational resources. Healthcare providers and researchers should make a concerted effort to bridge this gap by providing clear explanations, responding to patient questions, and offering educational materials that help patients better understand the technologies used, associated risks, and privacy rights [51, 52].

Unpacking Privacy Calculus and Interplay of Data Privacy …

155

4.1 Privacy Calculus in Current Modern Healthcare Landscape In the context of data privacy implications in healthcare, privacy calculus plays a crucial role in patients’ decisions to share their sensitive medical information with healthcare providers and researchers, while also considering the potential risks to their privacy and security. The privacy calculus defines the choice to release data as the balance between the advantages and disadvantages of disclosing personal information. To maximize the value of another party possessing personal information, this trade-off is made. In healthcare, the utility maximization concept aims to find a balance between the danger of highly private and sensitive information being shared with potentially undesirable parties and the desire to obtain the highest quality of treatment. The perceived danger will rise as the value of the released information rises, and respondents will be less inclined to post data as a result. On the other side, people are more ready to divulge personal information when they believe there is less danger associated. The basic premise is not true if individuals are unaware of the dangers of sharing data. People do not expect a reward since they do not fully understand the danger, hence advantages are not required to convince them to provide data [53]. According to the privacy calculus stream of research, people’s strong positive judgments of a particular behavior that they see as useful can offset their substantial privacy concerns as shown in Fig. 7. Our objective is to discover the aspects that, in addition to privacy concerns, contribute to the establishment of an overall favorable attitude toward EHRs from the rich perspective of privacy calculus. The privacy worries against EHR may be greatly reduced by both trust and perceived privacy control, and the privacy calculus components offer a major competitive effect on views toward EHR. The study [54], viewed via the prism of the privacy calculus, illustrates a comparable route to overcome the severely detrimental impact of privacy issues in the context of health care and EHRs, as well as how to lessen the privacy worries: (1) We can significantly reduce privacy concerns by providing users with advanced technology that allows them to control their privacy preferences on their own, and by fostering trust through smart regulation and technology. (2) We can overcome the negativity by persuading people to see the other very important factors in the equation the advantages, the convenience, and the opportunity for technological advancements when implementing EHR [54]. In the view of patients, the ability to navigate the healthcare system, firsthand knowledge of the ailment being investigated, and a willingness to share those experiences are the patient’s most valuable contributions. Additional traits would include an understanding of research and the research methodologies used as well as a desire to contribute to the research team effort. The knowledge that is taught in current training programs is prioritized over the skills, dispositions, and character traits needed to work well in a research team. Patients who struggle with their ability to cooperate, work in a team, or come to a consensus are particularly concerning, but not all research projects require the development of training programs for all patients. The authors in [55] refers to patient

156

N. Sakib et al.

Inherent Risks Perceived Risks Handled Risks

Data Disclosure Expected Benefits

Fig. 7 Correlation between privacy calculus factors [53]

engagement enterprise as being considerably different from patient grassroots efforts to influence healthcare changes. There are worries that a big portion of choosing and educating patients and members of the public is done to make sure people in these jobs are cooperative and eager to support a massive research sector [55]. According to researchers, in order to pick the appropriate course of action for their study, POR and POR-related or patient-involved researchers need to be aware of how to incorporate patients, manage project logistics, and collaborate with others. As a result, the researcher uses their interpersonal, managerial, financial, and communication skills in addition to their research skills. These findings are supported by a report that was just published by a team of 18 seasoned academics who talked about the “lessons learned” from conducting partnered research. These researchers came to the conclusion that excellent communication, inclusiveness, and respects for all are essential for a project’s success, as is ensuring that everyone who participates is financially supported [55].

4.2 Privacy Calculus and Healthcare Management Efficacy According to the privacy Calculus theory, patients rationally weigh the potential benefits they get from disclosing given information they consider personal and compare this to potential risks that will come to them following the disclosure of the information [56]. They are ready to disclose the information if they consider the benefit outweighs the risks. However, if they happen to consider the risk of weight more than the benefits, it becomes hard for them to disclose their information [56]. Although this might sound logical and rational, theory and practices end up hindering patient empowerment and significantly hampers healthcare management efficacy, as indicated in the following five cases:

Unpacking Privacy Calculus and Interplay of Data Privacy …

4.2.1

157

It is Impossible to Empower Patients Without Knowing Their Information

To empower the community and families, it is critical to understand the families’ histories and way of life in detail [57]. This is, however, only possible if the family or the community is willing to give this information to the healthcare staff in operation. It is also impossible to quantify the benefits such a community or a family could get from such an initiative. As such, as per the Privacy Calculus theory, it is more likely that the people with the information that would be enough to help the healthcare professions understand the problem that the family of the community is facing and hence be able to formulate the right solutions to it tackles it will not see it justifiable. They cannot weigh the benefits they could get and compare them with the risks that the community or the family will have due to such an action [57]. As a result of this fact, it becomes possible to empower the community and solve the existing problems that such a community would be facing [57]. This is one of the reasons why there are some communities in some parts of the world, including the United States, that are still living in poverty and poor health because it did not become willing to share their private information with the healthcare bodies or institutions for fear of the need to protects its privacy and security. Among such communities are indigenous communities around the world, whose research indicates that they have the highest illiteracy rates and are more likely to be adversely affected by pandemics compared to the non-traditional people in the world [57]. For instance, the life expectancy of the Indigenous tribe in the United States is below the average life expectancy rate of the American population.

4.2.2

Health Insurance Management Difficulties Due to Privacy Calculus

To have an adequate health insurance program, the organization or the government looking forward to providing the service to the population must have the knowledge and information about the population individually [65]. This comes about due to the difference in needs and healthcare issues of the people. For instance, some healthcare insurance programs do not cover preexisting conditions [57]. Nevertheless, the fact that people have to weigh the benefits and risks of offering important healthcare information means they have to fully understand how they would benefit from an insurance program to reveal their healthcare information. Given that, in practice, the insurance programs majorly rely on the ability of people to subscribe to it and fund it to receive the services offered by the insurance cover the community [57]. The people might need to see its importance or benefits. The act of people not disclosing their preexisting conditions by fearing the consequences that they could face by doing so and the fact that some people will not consider it necessary to subscribe to the existing health insurance cover negative affects the management of the healthcare system as well as hinders the empowerment of the people living in a given society. This shows that the privacy calculus tends to bring adverse and unprecedented societal effects.

158

N. Sakib et al.

Privacy Concern

Perceived Privacy Risks

Intent to Disclose Information

Actual Information Disclosure

Perceived benefits

Fig. 8 Factors possible of affecting one’s perception [42]

4.2.3

Negativity Bias

Another case where privacy calculus could be proved to be hindering the empowerment of people and management of the healthcare system is when dealing with people with negativity biasedness [58]. This includes people that have a negative point of view on life. Such people are more likely to see the risk than the benefits and overvalue the negative effects of sharing information with the healthcare institution or staff [59]. They are usually more concerned with their privacy and security and the need to protect their reputation at the expense of their health, which is dangerous to them and the medical profession. For instance, negatively biased people would not want to reveal sensitive information about their health status or lifestyle histories for fear of ruining their reputation [58]. Given the privacy calculus theory, people tend to weigh the benefits and the risks they get from revealing their information before they do so. However, people’s personalities significantly affect these weighing and analysis processes [60]. Those with negative points of view and sensitive to criticisms will likely not be in a position to offer their personal information hence making the diagnosing process slower and more expensive than it would have been. They, therefore, limit the accuracy and efficiency of healthcare management processes. The concept is illustrated in Fig. 8.

4.2.4

Fear of Technology

To adequately capture data, tools from the field of information technology have been applied. However, the privacy calculus requires that before the technology is implemented, it has to be analyzed and ascertained that it is beginning about positive effects on the patients [61]. The patients will therefore want to understand the system and how it will store the data they are giving. They will want to know the people who can access that data. Due to the increase in the level of knowledge and skills, including the skills that cyber hackers have, the patients might consider it risky to offer their data to the technology in the fear that the system will be hackable and

Unpacking Privacy Calculus and Interplay of Data Privacy …

159

hence allowing people they do not exist conder appropriate to access their data [61]. This will make it hard for the managers to quickly adopt new information technology systems designed to improve the healthcare management system. They will have to convince their customers and consumers about the security of their systems before such patients can agree or start to feel comfortable giving their information. As such, the process of information technology adoption will be slowed down and made more expensive.

4.2.5

Patients Will not Want Their Information to Be Shared with Third Parties

In a case where the disease of the health issue affecting a given patient becomes too challenging, it is the rationale for medical doctors and health practitioners to look for ways they could help the patient better [58]. This includes looking for external help. Looking for this help will require them to share important health details of the patients with the new healthcare provider to bring about maximum positive effects to the patients rather than start afresh. However, according to the privacy calculus theory, the patient must analyze and identify the benefits he will receive from the new healthcare provider justify the risk of them sharing their private information with the new healthcare provider [58]. This fact makes the process of looking for a medical solution more complicated than it ought to be. The privacy Calculus makes the process of healthcare provision rigid and inflexible given the act the patients want to ensure that the benefits they derive from any of their acts of delivering critical information are more than the risks. Table 2 summarizes the discussion. Table 2 Different cases pertaining to privacy calculus and healthcare management efficacy Case

Effect

Empowering patients without knowing their information

Impossible

Health insurance management

Difficulties due to privacy calculus

Negativity bias

Affects a patient’s point of view

Fear of technology

Make management of healthcare institutions hard

Patients will Not want their information to be shared with third parties

Make treatments and empowerment processes harder

160

N. Sakib et al.

Table 3 Summary of impact of healthcare regulations in addressing privacy calculus HIPAA, CHIP, HRRP, EMATALA, PSQIA

Legislation effectively shields individuals against improper use of our private electronic health records Legislation effectively controls the collection, use, and protection of electronic health records Regulations in place today can address misuse of electronic health records

4.2.6

Unintended Intrusions on Patient Data Privacy: Ethical Challenges in Advancing Clinical Procedures

It is crucial to address both intentional and inadvertent intrusions into patient data privacy resulting from advances in clinical procedures and protocols. These accidental discoveries can pose ethical challenges, as they expose sensitive information without malice. One such instance is the matching procedure in kidney exchange programs, which can result in the inadvertent disclosure of familial biological ties. This article examines the complex issue of inadvertent data privacy breaches in clinical settings and the need for careful consideration and ethical guidelines to navigate this emergent landscape [62]. Data processing and analysis advancements have made it possible for healthcare professionals to extract valuable insights from patient data. Nonetheless, as clinical procedures become more sophisticated, they may inadvertently uncover information that is not relevant to their intended purpose. In kidney exchange programs, for instance, the matching procedure involves analyzing genetic markers to reduce organ rejection risk. Unfortunately, this procedure may inadvertently reveal unanticipated biological ties or the absence thereof, potentially compromising the privacy of both donors and recipients [63]. Unintended intrusions on patient data privacy raise complex ethical concerns. While the primary objective of clinical procedures is to improve patient outcomes, the inadvertent disclosure of sensitive information raises concerns regarding autonomy, assent, and the right to privacy. Patients and their families may not anticipate or agree to the unintended effects of data analysis, especially when it reveals previously unknown familial relationships. Such revelations can have profound emotional and social repercussions, leading to a reevaluation of personal identities and influencing the dynamics of families [64]. The rising likelihood of accidental discoveries necessitates stringent ethical guidelines to address these privacy concerns. Currently, the majority of ethical directives in healthcare are primarily concerned with intentional violations of privacy, leaving unintentional intrusions unregulated and controversial. It is essential to develop exhaustive frameworks that incorporate the ethical dimensions of unintentional data privacy breaches. These guidelines should emphasize the significance of informed consent, openness, and patient education regarding the potential unintended consequences of data analysis.

Unpacking Privacy Calculus and Interplay of Data Privacy …

161

Healthcare organizations must actively engage and empower patients to address the challenges posed by unintended data privacy breaches. Transparent communication is of the utmost importance, ensuring that patients comprehend the purpose, risks, and potential unintended discoveries of clinical procedures. Beyond generic privacy policies, patient education should include specific information about data processing techniques and their prospective privacy implications. By empowering patients with knowledge, healthcare providers can cultivate trust, encourage active participation, and preserve patient autonomy. To address unintended breaches of patient data privacy, healthcare professionals, researchers, ethicists, and regulatory bodies must collaborate. To develop exhaustive guidelines that strike a balance between the benefits of advancing clinical procedures and safeguarding patient privacy, it is essential to involve multiple stakeholders. To ensure ethical and inclusive decision-making, this collaboration should take interdisciplinary perspectives into account and incorporate public input [65].

5 Healthcare Regulations to Address Privacy Calculus 5.1 Common Healthcare Regulations& Its Objectives Privacy calculus hinders patient empowerment in healthcare as it creates a potential conflict between patients’ desire for control over their medical data and the objectives of common healthcare regulations that aim to ensure data privacy and security. Regulation is a crucial component of healthcare and health insurance. Health programs must serve to promote public health and welfare, which is the responsibility of regulatory organizations, as well as to safeguard healthcare consumers from health hazards and guarantee a secure working environment for healthcare professionals [66]. Hence, regulations are important to impose and monitor healthcare by making sure healthcare organizations and facilities adhere to public health regulations and offer all patients and system users safe treatment [67]. There few common Healthcare regulations and the important ones:

5.1.1

Health Insurance Portability and Accountability Act (HIPAA)

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law that mandates the development of international guidelines to safeguard sensitive patient health information from being disclosed without the patient’s knowledge or agreement. The HIPAA Security Rule protects a subset of information covered by the Privacy Rule. This regulation stresses how the individual’s health information is being used [68]. The objectives of HIPAA [69]:

162

N. Sakib et al.

● The Privacy Rule establishes the parameters for when, how, and why PHI may be used and shared. Identify potential dangers to the information’s security and take precautions against them. ● The Security Rule sets standards to protect ePHI. ● Protect against expected illegal uses or disclosures that the regulation does not permit to confirm the workforce’s adherence. The primary goal of the law is to preserve patient privacy and security, make it simpler for consumers to maintain their health insurance, and assist the healthcare sector in reducing administrative expenses.

5.1.2

Health Information Technology for Economic and Clinical Health (HITECH) Act

The American Recovery and Reinvestment Act of 2009 includes the Health Information Technology for Economic and Clinical Health Act (ARRA). To encourage the use of electronic health records (EHR) and related technology in the US, the HITECH Act was developed. As part of the American Recovery and Reinvestment Act of 2009 (ARRA), a package to stimulate the economy, President Obama signed HITECH into law on February 17, 2009 [70]. The objectives of HITECH [71]: ● To set standards for the interoperability of electronic health records. ● To establish a national network for service providers to exchange electronic data. The five objectives of the HITECH Act have been compared to the five objectives of the US healthcare system: enhance quality, safety, and efficiency; include patients in their treatment; improve care coordination; raise population health status, and guarantee privacy and security [70].

5.1.3

Emergency Medical Treatment & Labour Act (EMTALA)

The Emergency Medical Treatment and Labor Act (EMTALA), passed by the US Congress in 1986, was a part of the Consolidated Omnibus Budget Reconciliation Act (COBRA) of the same year. In accordance with what is known as a federal “antidumping act,” hospitals are not allowed to transfer patients to other facilities, deny care to patients, or limit their access to care based on their insurance status or ability to pay. It has also had a big impact on a many people since the law was passed [71]. The objectives of EMTALA: ● Performing a medical check on all patients. ● Any individuals with urgent medical conditions should be stabilized. ● As required, transfer or accept the proper patients.

Unpacking Privacy Calculus and Interplay of Data Privacy …

163

Fig. 9 EMTALA requirements

EMTALA mandates that all patients, regardless of their capacity to pay, insurance status, national origin, race, creed, or color, be screened and treated for emergent medical issues. The requirements can be seen in Fig. 9.

5.1.4

Patient Safety and Quality Improvement Act (PSQIA)

A voluntary reporting system is set up under the Patient Safety and Quality Improvement Act of 2005 (PSQIA), which aims to improve the data available to evaluate and address patient safety and healthcare quality concerns. Patient safety information, also known as patient safety work product, is protected by Federal privilege and secrecy laws under PSQIA in order to promote the reporting and study of medical mistakes [72]. The objectives of PSQIA: Patient safety information, also known as patient safety work product, is protected by Federal privilege and secrecy laws under PSQIA in order to promote the reporting and study of medical mistakes. For breaches of patient safety confidentially, the PSQIA empowers HHS to impose civil monetary penalties. The goal of PSQIA: The Patient Safety and Quality Improvement Act of 2005 was passed in response to information showing avoidable medical mistakes raise healthcare costs by billions of dollars and cause death, disability, and other negative outcomes. The Act’s goal is to provide a framework under which medical professionals may freely share information regarding avoidable adverse occurrences, learn from their mistakes, and prevent similar ones from happening again [73].

5.1.5

Hospital Readmissions Reduction Program (HRRP)

Hospital readmission rates have been promoted as a quality indicator as well as a way to change the cost-growth trajectory of healthcare. Hospital Readmission Reduction Program (HRRP) was created in 2012 by the Affordable Care Act (ACA).

164

N. Sakib et al.

In accordance with this policy, hospitals are subject to financial penalties if their 30day readmission rates for acute myocardial infarction, heart failure, and pneumonia are greater than projected based on risk standardization [74]. The objectives of HRRP: ● Urges medical facilities to enhance care coordination and communication in order to better involve patients and caregivers in discharge plans and, as a result, cut down on needless readmissions. ● By associating payment with hospital treatment quality, the program helps achieve the national objective of enhancing Americans’ access to health care [75]. The goals of HRRP: To better involve patients and caregivers in discharge plans and lessen needless readmissions, hospitals are encouraged to improve communication and care coordination.

5.1.6

Children’s Health Insurance Program (CHIP)

As part of the Balanced Budget Act of 1997, the State Children’s Health Insurance Program (SCHIP) was established by the 105th Congress sixteen years ago in response to the needs of the 10 million American children who lacked health insurance. The Children’s Health Insurance Program, a part of the Social Security Act’s Title XXI, was enacted. The objectives of CHIP: ● Provide insurance to kids who don’t qualify for Medicaid but couldn’t otherwise get it through a family plan. ● To use funding from the federal government to either expand their Medicaid program, start a new program, or do both at once. ● Making it easier for children to obtain coverage. The goal of CHIP: The primary objective of CHIP was to offer federal funding to states so they could develop programs exclusively for kids from families whose earnings were over the Medicaid eligibility limits but not enough to qualify for private health insurance [76].

5.2 Impact of Healthcare Regulations in Addressing Privacy Calculus Healthcare regulations that prioritize patient data privacy and security can help alleviate privacy calculus concerns by providing clear guidelines for data collection, use, and sharing, while also empowering patients with greater control over their personal health information. The General Data Protection Regulation (GDPR) is more important since it directly affects several elements of the privacy calculus. The GDPR is a requirement for all businesses, even global corporations. The six key components of GDPR include privacy by design, data portability, right to access, right to

Unpacking Privacy Calculus and Interplay of Data Privacy …

165

be forgotten, and the function of a data protection officer. Each of them contributes significantly. The notion of privacy calculus will become increasingly important as a result of the new data protection legislation as individuals become more aware of what is done with and how their data is handled [53]. The HIPAA Privacy Rule protects individually identifiable health information, which is often referred to as protected health information (PHI). The Security Rule, which is a portion of the data covered by the Privacy Rule, protects any electronically generated, acquired, retained, or transmitted personally identifiable health information [77]. According to the Security Rule, this data is referred to as “electronically protected health information” (e-PHI). In theory, it gives patients the freedom to go at their own medical records, request a copy of them, and request changes. It allows individuals more control over the use and disclosure of their health information [78]. The Privacy Rule does not apply to identifiable personal health information owned or maintained by organizations other than covered businesses. Covered Entities are not allowed to use or disclose PHI unless specifically authorized or required to do so by the Privacy Rule. Unauthorized disclosure of PHI by a covered entity for treatment, payment, and managing the business of providing healthcare is permitted. For additional uses and disclosures, the Privacy Rule normally requires the person’s written agreement, which is an “authorization” that must follow certain content criteria. Following that, the Privacy Rule establishes a number of exceptions to this general rule that allow covered entities to use and disclose PHI in particular situations without the person’s consent. Healthcare regulations based on privacy calculus are shown in Fig. 10. As an example, the Privacy Rule allows the following situations in which PHI may be published without the subject’s consent: ● To promote public health as mandated by local, state, and federal legislation. ● To government organizations for health oversight tasks such audits, inspections, civil, criminal, or administrative processes, as well as other tasks required for the supervision of the healthcare system to law enforcement officials. ● For legal and administrative actions, if the request for information is issued by a court order. ● For research purposes.

Consumer

Health care Regulations that are to be followed

Patients Data, Test Reports, Scan Reports, Details, etc.

Organizations, Hospitals, Research Institutions, etc.

Fig. 10 Healthcare regulations based on privacy calculus

Approved

Dissemination of information

Approval or Denial Denied

Restriction to sharing

166

N. Sakib et al.

According to HITECH, healthcare organizations must, to the degree possible, restrict their PHI use, disclosure, or request to a small number of data sets or just what is absolutely necessary. Organizations have always been required to share only the information essential to carry out a specific task under the HIPAA privacy laws. According to HITECH, the covered organization disclosing the PHI must determine whether the disclosure is minimally necessary. Organizations disclose information when they transfer it or share it with outside parties. According to HIPAA, disclosure is the act of releasing, transferring, granting access to, or otherwise disclosing information to a party other than the entity that originally held it. Although HITECH does not alter this definition, it does alter how such disclosures are accounted for by entities that use electronic health records [79]. Here are few listed based on privacy concerns [54]: ● A corporation should never use a customer’s personal information for any other purpose than for which they were originally provided. ● Healthcare organizations should never sell to other healthcare organizations any personal information stored in their computer databases. ● Health care organizations should never divulge patient information to other healthcare organizations unless the patient who gave the information has given their consent. ● Health care entities should not use personal information for any purpose unless it has been authorized by the individuals who provided the information. ● Health care organizations ought to put more time and effort into guarding against illegal access to personal data. ● Regardless of the cost, all personal data stored in computer databases should have its accuracy double-checked. ● Health care organizations should spend more time and effort ensuring that the personal data in their databases is accurate. The U.S. Department of Health and Human Services Office for Civil Rights warns that anybody who intentionally acquires or disseminates personally identifiable health information violating the Privacy Rule may be punished with a fine of up to $50,000 or perhaps a year in jail (OCR). Criminal penalties increase to $100,000 and up to five years in jail if the wrongdoing involves false pretenses; to $250,000 and up to ten years in jail if it involves the intention to sell, transfer, or utilize identifiable health information for profit, gain, or intentional injury. Criminal prosecutions are subject to the Department of Justice’s jurisdiction under the Privacy Rule [80].

Unpacking Privacy Calculus and Interplay of Data Privacy …

167

6 Conclusion Data-driven solutions have facilitated significant advancements in patient care, medical research, and healthcare administration in the modern healthcare environment. Nonetheless, the exponential development of patient data presents significant privacy risks, such as unauthorized access, data intrusions, and privacy violations. This chapter has emphasized the significance of stringent data privacy regulations to protect patient privacy and preserve confidence in the healthcare system. In the healthcare ecosystem, privacy calculus is a crucial concept that hinders patient empowerment and healthcare management efficiency. The five case studies presented in this chapter demonstrate the various ways in which privacy considerations can impact patients, healthcare professionals, and researchers. These instances illustrate the need for a more nuanced comprehension of privacy calculus and its implications for healthcare stakeholders. In addition, this chapter has covered some prevalent healthcare regulations and their primary objectives. While these regulations have made substantial progress in addressing privacy concerns in the healthcare industry, they must be continuously updated and adapted to keep up with technological advances and evolving privacy risks. Moreover, this chapter argues that healthcare regulations can play a significant role in addressing privacy calculus by fostering transparency, accountability, and patient empowerment in the healthcare ecosystem. The chapter also accentuates the importance of privacy protection in the healthcare industry and outlines several steps that can be taken to secure patient information while advancing healthcare research and administration. Stakeholders can ensure that patient data is used ethically and responsibly in pursuing enhanced healthcare outcomes by promoting privacy-conscious practices, encouraging patient empowerment, and instituting robust data privacy regulations.

References 1. G.A. Hassan, Health Care Informatics. International Journal of Internet Education 18(1), 39–43 (2019). https://doi.org/10.21608/IJIE.2019.98636 2. Computers and Healthcare 1950 to present. http://dlthede.net/informatics/chap01introni/hea lthcare_computers.html. Accessed 07 Apr. 2023 3. Top 3 Considerations in Building a Data-driven Healthcare Organization. https://www.sol vedge.com/data-driven-healthcare. Accessed 07 Apr. 2023 4. Patient-centrism: The data-driven trend to watch in healthcare transformation. Fierce Healthcare. https://www.fiercehealthcare.com/regulatory/10-data-driven-trends-healthcare-tra nsformation. Accessed 07 Apr. 2023 5. RBC Capital Markets. The healthcare data explosion. https://www.rbccm.com/en/gib/health care/episode/the_healthcare_data_explosion. Accessed 07 Apr. 2023 6. C. Suter-Crazzolara, Better patient outcomes through mining of biomedical big data, Frontiers in ICT, 5, no. DEC, (2018), https://doi.org/10.3389/FICT.2018.00030/FULL 7. T. Seymour, D. Frantsvog, T. Graeber, Electronic Health Records (EHR). Am. J. Health Sci. (AJHS) 3(3), 201–210 (2012). https://doi.org/10.19030/AJHS.V3I3.7139

168

N. Sakib et al.

8. E. Kim, S.M. Rubinstein, K.T. Nead, A.P. Wojcieszynski, P.E. Gabriel, J.L. Warner, The evolving use of electronic health records (EHR) for research. Semin. Radiat Oncol 29(4), 354–361 (2019). https://doi.org/10.1016/J.SEMRADONC.2019.05.010 9. What are the advantages of electronic health records? HealthIT.gov. https://www.healthit.gov/ faq/what-are-advantages-electronic-health-records. Accessed 07 Apr 2023 10. A.H. Nordo et al., Use of EHRs data for clinical research: Historical progress and current applications. Learn. Health Syst. 3(1), e10076 (2019). https://doi.org/10.1002/LRH2.10076 11. J. King, V. Patel, E.W. Jamoom, and M.F. Furukawa, Clinical benefits of electronic health record use: national findings, Health Serv. Res. 49(1pt2) pp. 392–404, (2014). https://doi.org/ 10.1111/1475-6773.12135 12. D. Lewis, B.L. Chang, C.P. Friedman, Consumer health informatics, Consum. Health Inform., pp. 1–7, (2005). https://doi.org/10.1007/0-387-27652-1_1 13. R.J. Blendon, K. Hunt, J. M. Benson, C. Fleischfresser, T. Buhr, Trends: Understanding the American public’s health priorities: A 2006 perspective, Health Aff. 25(6), (2006). https://doi. org/10.1377/HLTHAFF.25.W508 14. Trends in consumer access and use of electronic health information. HealthIT.gov. https:// www.healthit.gov/data/data-briefs/trends-consumer-access-and-use-electronic-health-inform ation. Accessed 07 Apr (2023) 15. Consumers want a more connected, personal health care experience, new CVS Health study reveals. CVS Health. https://www.cvshealth.com/news/innovation/consumers-want-a-moreconnected-personal-health-care-experience.html. Accessed 07 Apr 07, 2023). 16. G. Eysenbach, Consumer health informatics. BMJ 320(7251), 1713–1716 (2000). https://doi. org/10.1136/BMJ.320.7251.1713 17. S. Dash, S. K. Shakyawar, M. Sharma, and S. Kaushik, “Big data in healthcare: management, analysis and future prospects. J. Big Data, 6(1), (2019), https://doi.org/10.1186/S40537-0190217-0 18. Using predictive analytics in health care. Deloitte Insights. https://www2.deloitte.com/us/ en/insights/topics/analytics/predictive-analytics-health-care-value-risks.html Accessed 07 Apr (2023) 19. C. Maloy, Library guides: Data resources in the health sciences: Clinical data. Accessed: 07 Apr 2023. Available: https://guides.lib.uw.edu/hsl/data/findclin 20. “Surveys”. 21. Data subject access requests. IT Governance UK. https://www.itgovernance.co.uk/data-sub ject-access-requests Accessed 07 Apr 2023 22. W. Raghupathi and V. Raghupathi, Big data analytics in healthcare: Promise and potential. Health Inf. Sci. Syst. 2(1), 2014, https://doi.org/10.1186/2047-2501-2-3 23. K. Abouelmehdi, A. Beni-Hessane, H. Khaloufi, Big healthcare data: preserving security and privacy. J. Big Data 5(1), 1–18 (2018). https://doi.org/10.1186/S40537-017-0110-7/TABLES/5 24. African hospitals adopt SOPHiA artificial intelligence to trigger continent-wide healthcare leapfrogging movement—SOPHiA GENETICS. https://www.sophiagenetics.com/press-rel eases/african-hospitals-adopt-sophia-artificial-intelligence-to-trigger-continent-wide-health care-leapfrogging-movement/ Aaccessed 07 Apr 2023 25. D. Agrawal, D.C. Rockey, Privacy threats when seeking online health information. JAMA Intern. Med. 173(19), 1838–1840 (2013). https://doi.org/10.1001/JAMAINTERNMED.2013. 7795 26. UPDATE: The 10 biggest healthcare data breaches of 2020. https://healthitsecurity.com/news/ the-10-biggest-healthcare-data-breaches-of-2020. Accessed 07 Apr 2023 27. Biggest healthcare data breaches reported this year, So Far. https://healthitsecurity.com/fea tures/biggest-healthcare-data-breaches-reported-this-year-so-far. Accessed 01 Nov 2022 28. Data Breach—Definition. https://www.trendmicro.com/vinfo/us/security/definition/databreach. Accessed 07 Apr 2023 29. Luxottica data breach impacts 829,454 individuals in the United States. https://www.hipaajour nal.com/luxottica-data-breach-impacts-829454-individuals-in-the-united-states/. Accessed 07 Apr 2023

Unpacking Privacy Calculus and Interplay of Data Privacy …

169

30. Computer theft xxposes personal, health data of 654K oregon patients. https://healthitsecurity. com/news/computer-theft-exposes-personal-health-data-of-654k-oregon-patients. Accessed 07 Apr 2023 31. What is ransomware attack types, protection & removal imperva. https://www.imperva.com/ learn/application-security/ransomware/. Accessed 07 Apr 2023 32. Florida orthopaedic institute to pay $4M to settle claims it did not protect patient data. https://www.beckersspine.com/orthopedic/54991-florida-orthopaedic-instituteto-pay-4m-to-settle-claims-it-did-not-protect-patient-data.html. Accessed 07 Apr 2023 33. What is a Malware Attack?—Definition. https://www.cyberark.com/what-is/malware/. Accessed 07 Apr 2023 34. Data breaches. Telehealth and telecare aware. https://telecareaware.com/tag/data-breaches/. Accessed 12 Apr 202 35. R.E. Holtfreter, A. Harrington, Data breach trends in the United States. J Financ Crime 22(2), 242–260 (2015). https://doi.org/10.1108/JFC-09-2013-0055/FULL/XML 36. A. H. Seh et al., Healthcare data breaches: Insights and implications, Healthcare 2020, 8(2). p. 133, (2020), https://doi.org/10.3390/HEALTHCARE8020133 37. Colorado health provider alerts 295,617 patients of data breach. https://www.beckershospital review.com/cybersecurity/colorado-health-provider-alerts-295-617-patients-of-data-breach. html. Accessed 07 Apr 2023 38. Fraud in healthcare. Bass, Berry and Sims PLC. https://www.bassberry.com/fraudinhealt hcare/?gclid=Cj0KCQjw27mhBhC9ARIsAIFsETHXARa_8y4r4gfa4Ul6aTQ3icgIv9YHy 0L5ssu9-WGucfkrQ7Prq0kaAtVbEALw_wcB. Accessed 07 Apr 2023 39. What Is Phishing?—Definition, Types of attacks and more. Proofpoint US. https://www.pro ofpoint.com/us/threat-reference/phishing. Accessed 07 Apr 2023 40. Misconfigured server at BJC HealthCare exposed patient data for more than 8 months. Fierce Healthcare. https://www.fiercehealthcare.com/tech/bjc-healthcare-data-breach-serverconfiguration. Accessed 07 Apr 2023. 41. HHS office for civil rights settles HIPAA investigation with arizona hospital system following cybersecurity hacking. HHS.gov.” https://www.hhs.gov/about/news/2023/02/02/hhs-officefor-civil-rights-settles-hipaa-investigation-with-arizona-hospital-system.html. Accessed 07 Apr 2023 42. Dental care alliance data breach impacts more than 1 Million patients. https://www.hipaajour nal.com/dental-care-alliance-data-breach-impacts-more-than-1-million-patients/. Accessed 07 Apr 2023 43. UPDATE: Luxottica data leaked by hackers after ransomware attack. https://healthitsecurity. com/news/luxottica-data-leaked-by-hackers-after-ransomware-attack-breach. Accessed 07 Apr 2023 44. Health share of oregon discloses data breach, theft of member PII. ZDNET. https://www.zdnet. com/article/health-share-of-oregon-discloses-data-breach-theft-of-member-pii/. Accessed 07 Apr 2023 45. Florida orthopaedic institute reaches $4M settlement over data breach. https://healthitsecurity. com/news/florida-orthopaedic-institute-reaches-4m-settlement-over-data-breach. Accessed 07 Apr 2023 46. Magellan health data breach victim tally reaches 365K patients. https://healthitsecurity.com/ news/magellan-health-data-breach-victim-tally-reaches-365k-patients. Accessed 07 Apr 2023 47. Aspenpointe data breach: 295,617 patients’ data exposed!” https://www.stealthlabs.com/ news/american-healthcare-provider-experiences-cyberattack-295617-patients-data-exposed/. Accessed 07 Apr 2023 48. BJC healthcare settles data breach lawsuit stemming from 2020 phishing attack. https://www. hipaajournal.com/bjc-healthcare-settles-data-breach-lawsuit-stemming-from-2020-phishingattack/. Accessed 07 Apr 2023 49. N. Shen et al., Understanding the patient privacy perspective on health information exchange: A systematic review. Int. J. Med. Inform. 125, 1–12 (2019). https://doi.org/10.1016/J.IJMEDINF. 2019.01.014

170

N. Sakib et al.

50. A. H. Seh et al., Healthcare data breaches: Insights and implications, Healthcare, 8(2), (2020), https://doi.org/10.3390/HEALTHCARE8020133. 51. B.E. Iott, C. Campos-Castillo, D.L. Anthony, Trust and privacy: How patient trust in providers is related to privacy behaviors and attitudes, AMIA Annual Symposium Proceedings, 2019, p. 487, (2019), Accessed 02 Jun 2023. Available: /pmc/articles/PMC7153104/ 52. M. Madadin et al., Patients’ awareness of their rights: an insight from a teaching hospital in Saudi Arabia. Acta. Biomed. 94, 2023059 (2023). https://doi.org/10.23750/abm.v94i2.14110 53. S. Kruthoff, 11 th IBA Bachelor Thesis Conference, (2018) 54. T. Dinev, V. Albano, H. Xu, A. D’Atri, and P. Hart, Individuals’ attitudes towards electronic health records: A privacy calculus perspective, pp. 19–50, (2016). https://doi.org/10.1007/9783-319-23294-2_2 55. N. Frisch et al., Patient-oriented research competencies in health (PORCH) for researchers, patients, healthcare providers, and decision-makers: Results of a scoping review. Res Involv Engagem 6(1), 1–14 (2020). https://doi.org/10.1186/S40900-020-0180-0/FIGURES/1 56. M. S. Rahman, Does privacy matters when we are Sick? An extended privacy calculus model for healthcare technology adoption behavior, 2019 10th International Conference on Information and Communication Systems (ICICS), pp. 41–46, (2019). https://doi.org/10.1109/IACS.2019. 8809175 57. A. Gutierrez, S. O’leary, N. P. Rana, Y. K. Dwivedi, and T. Calle, “Using privacy calculus theory to explore entrepreneurial directions in mobile location-based advertising: Identifying intrusiveness as the critical risk factor 58. S. Chatterjee, R. Chaudhuri, D. Vrontis, E. Siachou, Examining the dark side of human resource analytics: an empirical investigation using the privacy calculus approach. Int. J. Manpow. 43(1), 52–74 (2022). https://doi.org/10.1108/IJM-02-2021-0087/FULL/PDF 59. K. Plangger, M. Montecchi, Thinking Beyond Privacy Calculus: Investigating Reactions to Customer Surveillance. J. Interact. Mark. 50, 32–44 (2020). https://doi.org/10.1016/j.intmar. 2019.10.004 60. M. Jozani, E. Ayaburi, M. Ko, and K. K. R. Choo, “Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective, Comput. Human. Behav, 107, (2020), https://doi.org/10.1016/J.CHB.2020.106260 61. A. Gutierrez, S. O’Leary, N.P. Rana, Y.K. Dwivedi, T. Calle, Using privacy calculus theory to explore entrepreneurial directions in mobile location-based advertising. Comput. Human. Behav. 95, 295–306 (2019). https://doi.org/10.1016/J.CHB.2018.09.015 62. B. Murdoch, Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics 22(1), 1–5 (2021). https://doi.org/10.1186/S12910-021-00687-3/ PEER-REVIEW 63. S.V.G. Subrahmanya et al., The role of data science in healthcare advancements: applications, benefits, and future prospects. Ir. J. Med. Sci. 191(4), 1473–1483 (2022). https://doi.org/10. 1007/S11845-021-02730-Z/FIGURES/5 64. F.F. Ozair, N. Jamshed, A. Sharma, P. Aggarwal, Ethical issues in electronic health records: A general overview. Perspect Clin Res 6(2), 73 (2015). https://doi.org/10.4103/2229-3485. 153997 65. Y. Bombard et al., Engaging patients to improve quality of care: A systematic review. Implement. Sci. 13(1), 1–22 (2018). https://doi.org/10.1186/S13012-018-0784-Z/TABLES/5 66. 8 Health Care Regulations in United States | Regis College. https://online.regiscollege.edu/ blog/8-important-regulations-united-states-health-care/. Accessed 07 Apr 2023 67. Healthcare compliance: Everything you need to know. https://www.phoenix.edu/blog/what-ishealthcare-compliance.html. Accessed 07 Apr 2023 68. Health insurance portability and accountability act of 1996 (HIPAA). CDC. https://www.cdc. gov/phlp/publications/topic/hipaa.html. Accessed 07 Apr 2023 69. HIPAA for Dummies—2023 Update. https://www.hipaaguide.net/hipaa-for-dummies/. Accessed 07 Apr 2023 70. What is HITECH (Health information technology for economic and clinical health) act of 2009? Definition from TechTarget. https://www.techtarget.com/searchhealthit/definition/HIT ECH-Act. Accessed 07 Apr 2023

Unpacking Privacy Calculus and Interplay of Data Privacy …

171

71. HITECH act enforcement interim final rule, HHS.gov. https://www.hhs.gov/hipaa/for-profes sionals/special-topics/hitech-act-enforcement-interim-final-rule/index.html. Accessed 07 Apr 2023 72. Patient safety and quality improvement act of 2005 statute and Rule. HHS.gov. https://www.hhs. gov/hipaa/for-professionals/patient-safety/statute-and-rule/index.html. Accessed 07 Apr 2023 73. Patient safety and quality improvement act of 2005. News & Resources, Dorsey. https://www. dorsey.com/newsresources/publications/2008/03/patient-safety-and-quality-improvementact-of-2005. Accessed 07 Apr 2023 74. C.K. McIlvennan, Z.J. Eapen, L.A. Allen, Hospital Readmissions Reduction Program. Circulation 131(20), 1796–1803 (2015). https://doi.org/10.1161/CIRCULATIONAHA.114. 010270 75. Hospital readmissions reduction program (HRRP). CMS. https://www.cms.gov/Medicare/ Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program. Accessed 07 Apr 2023 76. A.D. Racine et al., Children’s Health Insurance Program (CHIP): Accomplishments, Challenges, and Policy Recommendations. Pediatrics 133(3), e784–e793 (2014). https://doi.org/ 10.1542/PEDS.2013-4059 77. N. Homer et al., HIPAA, the privacy rule, and Its application to health research, PLoS Genet, 4(8), (2009), https://doi.org/10.1371/JOURNAL.PGEN.1000167 78. Summary of the HIPAA security rule. HHS.gov. https://www.hhs.gov/hipaa/for-professionals/ security/laws-regulations/index.html. Accessed 07 Apr 2023 79. Miaoulis and W.M, Access, use, and disclosure: HITECH’s impact on the HIPAA touchstones, J AHIMA, 81(3), pp. 38–39; 64, (2010), Accessed 07 Apr 2023. Available: http://bok.ahima. org/doc?oid=98645 80. Penalties for violating HIPAA. American dental association. https://www.ada.org/resources/ practice/legal-and-regulatory/hipaa/penalties-for-violating-hipaa. Accessed 07 Apr 2023

Effects of Caregiver Support in the Adoption of Assistive Technologies for Online Patient Health Self-management Reza Aria, Norm Archer, Vikraman Baskaran, and Bharat Shah

1 Introduction According to the United States National Center for Chronic Disease Prevention and Health Promotion [11] “Chronic diseases are defined broadly as conditions that last one year or more and require ongoing medical attention or limit activities of daily living or both. Chronic diseases such as heart disease, cancer, and diabetes are the leading causes of death and disability in the United States. They are also leading drivers of the nation’s $3.8 trillion in annual health care costs.” The Chronic Disease Center adds, “Six in ten adults in the US have a chronic disease and four in ten have two or more”. These are indications of the critical nature of both the economic and medical nature of chronic illness in the population of the US and other countries worldwide. Living a normal life in a relaxed and familiar environment at home is usually the hope and the goal of people suffering from serious chronic illnesses. This can be achieved if patients and/or their caregivers are willing to take on an active role by self-managing chronic care for patients in their homes where they will feel more R. Aria University of Texas, Dallas, USA e-mail: [email protected] N. Archer McMaster University, Hamilton, Canada e-mail: [email protected] V. Baskaran (B) Mercer University, Macon, USA e-mail: [email protected] B. Shah Toronto Metropolitan University, Toronto, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Daimi et al. (eds.), Current and Future Trends in Health and Medical Informatics, Studies in Computational Intelligence 1112, https://doi.org/10.1007/978-3-031-42112-9_8

173

174

R. Aria et al.

at ease and comfortable than in a healthcare setting such as, a clinic, hospital, long term care facility [12, 24]. Many chronic diseases are the leading cause of death but are preventable. Tobacco smoking, excessive alcohol consumption, physical inactivity, and unhealthy eating have all been identified as modifiable risk factors for chronic diseases [2, 36]. The main goal of chronic health self-management is to manage, improve and elevate patients’ health-related behavior and encourage them to change their lifestyles, thereby improving their health status and quality of life. Not incidentally, such undertakings will also help to reduce healthcare system resource utilization [36]. Studies have shown that in collaboration with healthcare providers, health self-management and chronic disease self-management can play a key role in chronic disease preventative care. It is clearly in the best interests of both the patient and the healthcare system to encourage self-management of both preventive and chronic health behaviors [36]. Interventions involving self-management of chronically ill patients have been reported frequently. There have been a growing number of studies of technologysupported self-management interventions. Some of these studies demonstrated improvements in patient outcomes and quality of life. A recent systematic review collates such technology-assisted interventions, their attributes, and outcomes [31]. Mobile health (mHealth) is a critical assistive technology that has also attracted a great deal of interest in self-management among patients with chronic diseases. A study of five reviews in this field examined 30 mHealth interventions of this type [64]. 14 of these studies showed effective results. Significantly positive outcomes were reported in 8 interventions of the remaining 17. However, a recently conducted randomized controlled trial (RCT) of over 4000 self-managed chronic patients did not find conclusive evidence to support telephone-based health coaching interventions [18]. Many technology-supported self-management interventions for chronically ill patients also involve informal caregivers. Informal caregivers are unpaid family members, neighbors, or friends helping to care for an individual who, because of chronic or acute illness, needs help to manage personal tasks such as dressing, taking medications, or dealing with other personal issues [56]. A systematic review of such support strategies [1] classified their results into 131 direct patient care strategies and 5 health system strategies. No reviews of community-policy strategies were found, but 4 original papers related to patient and family engagement, and 4 papers discussed patient and family engagement at the health system or community-policy levels. These results present relatively strong evidence that both informal and formal caregivers play an important role in the support of chronically ill patients. It can be safely presumed that health self-management continues to move towards the active use of technology to support chronically ill patients and it is becoming more important to understand the role caregivers (especially informal caregivers) play in selecting the technological support. In fact, quite often these caregivers find that their roles often require them to take on the responsibility of using such technologies. Few published studies have mentioned the contributions of informal caregivers in helping chronically ill patients in choosing which technological support to use. Therefore, we have chosen to study how informal caregivers can influence patient

Effects of Caregiver Support in the Adoption of Assistive …

175

attitudes toward the adoption of technological support systems for chronic disease self-management. The objective of this study and our research question is to determine what, if any, differences exist between the perceptions of patients with chronic disease who have caregiver assistance and those who have no such assistance, concerning whether they intend to adopt online support for self-managing their disease. This study developed a statistical data analysis model (structural equation model (SEM)), to represent the perceptions of patients. With this tool, a researcher begins with a model that specifies how a set of variables are related to each other. These relationships are then developed into a set of mathematical equations that include the variables being considered. We used the model to analyze and compare data gathered from representative individuals in two distinct groups from different settings: An Internet panel group and an on-site in-person group. This model involves constructs that represent patient perceptions of caregiver support and its relationships with effort expectancy, performance expectancy, and hedonic motivation toward behavioral intentions to adopt the use of a chronic disease health self-management system. The innovation in this publication comes from its unique perspective on how enabled caregivers can psychologically and physically support patients in the adoption of new assistive technologies to help them throughout their care process, as well as showing how a mobile/desktop application can be an enabler of both caregivers and patients. The following sections discuss the nature of chronic disease self-management and how the proposed system has the functionality to support the needs of patients with chronic disease. This is followed by a description of SEM and the resulting analysis of the data gathered during the study.

2 Materials and Methods This study, focuses specifically on four related components [46]: (1) Self-monitoring: “ trend progression will detect the health status of the patient by using current conditions, patient information history, health status history, a progression of the disease, and well-trained knowledge accordingly to clinical data, lifestyle data, and experience of the healthcare professionals based mobile application.” [48], (2) Self-care: “a process of maintaining health through treatment adherence and health-promoting practices (self-care maintenance, behavior and condition monitoring (self-care monitoring, and managing signs and symptoms when they occur (self-care management [32]”, (3) Adherence: “a complex concept that goes beyond the dosage or the use of inhalation devices, and a number of variables are involved in determining adherence, from the clinical aspects of the disease to the patient’s confidence in the doctor’s expertise and the level of social support experienced by the patient”[48]”, and (4) Decision Support: “any computer-based system that aids in clinical decision-making” [33], etc.). The current study is a continuation of other related studies [5, 6] that were completed recently. While the first study [5] reviewed the effectiveness of a video

176

R. Aria et al.

clip for patient education through an online self-management support application, the second study [6] focused on the role of specific non-human support and sustainability elements to ensure continuity and sustainability of care for the patients. The current study shows how a caregiver can assist patients suffering from chronic conditions to undertake the tasks associated with self-care and self-management of their condition(s). In addition, caregivers can help and/or encourage patients to adopt assistive technologies. This is critical to healthcare providers, health policymakers, and technology developers [29, 39, 49, 57, 60]. To perform this study, a self-management support application (the system) capable of supporting patients with peripheral arterial disease (PAD) was designed and developed. PAD is a chronic atherosclerotic cardiovascular disease that occurs primarily in the lower limbs of adults over the age of 60, often co-morbid with Type 2 diabetes mellitus [17]. The presence of PAD is characterized by an ankle brachial index (ABI) measurement 1,

n=1

then the fractal dimension of the interpolation function is the unique real solution of N

|sn |anD−1 = 1.

n=1

If we want to create functions or curves with a specific fractal dimension, then we use the formula N

|sn | = N D−1

n=1

by using equidistant interpolation points.

4 Application to Medical Imaging Diagnostic ultrasound relies on the reflection of the ultrasound beam on the multiple surfaces the wave encounters as it travels through the tissues. From some areas there is little or no reflection and from others strong. Thus, we distinguish areas without reflection or non-echoic and others with reflection or echoic. Non-echoic areas are those containing watery material (bladder, amniotic fluid, heart cavities or renal pelvis). The echogenic areas are the fetal spine, the myometrium, etc. In order to examine a pregnancy sonographically, in addition to orientation in space, one must proceed based on a predetermined order in the examination of the fetus. It is also important to know what to look for in a fetus based on gestational age, as well as to know which area of the body he is examining. In the initial pregnancy transvaginal ultrasound with high frequencies of 5–7.5 MHz is performed which improves the diagnostic details, but at the expense of the limited depth of field. Transabdominal ultrasound is performed with 3–5 MHz heads and provides a wide field. Between the fifth and seventh week, transvaginal probe with transducers 5–7.5 will show a gestational sac, yolk sac, fetus (in that order). When the gestational sac becomes visible at 5 weeks, it will appear empty and we may not be able to separate it from the pseudo sac of an ectopic pregnancy (the normal sac has a rounder shape, is located at the bottom of the uterus, and is surrounded by an echogenic ring); see Table 1. The first trimester of pregnancy includes the most important phases of the intrauterine life of the fetus. In the fifth week of pregnancy, only an unusually thick endometrium is discernible. In these early stages it is possible to assume that there is a sac which in fact may be the pseudo sac of the ectopic pregnancy or any accumulation

228

V. Drakopoulos and P. Manousopoulos

Table 1 Gestational age in relation to abdominal and transvaginal sonographic findings and plasma levels of β-hCG Gestational age in terms of LMP

Abdominal indications

Transvaginal indications

β-hCG

< 5 weeks

None

Gestational sac

1800

5–6 weeks

Gestational sac

Gestational sac, yolk sac

1800–3500

7 weeks

5–10 mm embryo

As in the transabdominal, with cardiac activity

> 20,000

of mucus in the endometrium which is usually seen in the middle of the cycle. In the sixth week, the sac with a thick wall is clearly visible, showing a stronger reflection than the myometrium that surrounds it and sometimes with a different amount of fluid inside it without reflections. The implantation site of the sac is precisely determined. In more than half of the cases inside the sacs by the sixth week there is some reflection. This can come from the newly formed fetal pole or from a blood clot inside the sac. In the seventh week the fetal pole becomes 1 cm in length and the fetal heart can be seen beating. The yolk sac appears as a thin-walled ring less than 1 cm in diameter, sometimes in another location within the embryo sac. In the second and third trimesters during the first phase of the examination, the heart of the fetus, active movements, the amount of amniotic fluid and the position of the placenta are observed. Locating the placenta requires correct orientation in order to assess the area it occupies and the wall to which it is attached. At the same time, the face (lips, upper and lower jaw, the nose with the cheeks, the fetus’s cheeks and forehead), skull-brain, spine, chest, abdomen, urinary system, genitals and finally upper and lower limbs are observed. The mathematical modelling of the ultrasound findings is useful for their evaluation as well as the subsequent diagnosis. Specifically, in the pregnancy context, it can assist in examining the normal fetus development as well as diagnosing potential relevant problems. We propose the use of fractal interpolation for the modelling of the ultrasound findings. The advantage of this approach is that it can be used for both the physical measurement of the findings as well as the calculation of their fractal dimension. Indeed, it is well known in the literature that the fractal dimension can be used for medical purposes including diagnosis; see [6, 9, 11, 19, 24, 25, 27]. An example of an ultrasound is shown in Fig. 4 where the position of the fetus is marked with small circles. The outline of the relevant area is presented in Fig. 5, while its modelling with an AFIC is shown in Fig. 6. For the construction of the AFIC, 1/30 of the total points were used as interpolation points (marked as red points in the Figure); they have been chosen equidistantly from the extracted outline. We notice that AFIC (drawn in cyan) closely follows the original contour (drawn in yellow) despite the very sparse selection of interference points. This suggests that it is possible to achieve appreciable compression ratio with low error while preserving all the existing medical information within the data. Moreover, it is possible to directly calculate the fractal dimension of the contour from the AFIC. This is very important

Obstetric Ultrasound Modelling and Analysis with Fractal Interpolation …

229

as the fractal dimension can be used for categorizing medical images as well as for diagnosis. With the method of fractal interpolation, we are provided with details that become visible after magnification under any scale. We studied the case of a 28-year-old primiparous woman who presented to the emergency department with vaginal bleeding. Transvaginal ultrasound revealed that it was a singleton pregnancy of 8 weeks. The question was raised as to whether the vaginal bleeding was the result of detachment of the trophoblast or some other pathology. It was very important to see if there was a swelling of the membranes from blood collection, which could lead to spontaneous abortion and would determine the plan of our therapeutic approach. With the help of the FIFs. We succeeded in locating and sufficiently magnifying the suspicious area, while extracting information about the surfaces under study. No detachment was found, so the woman was not admitted to the clinic, bed rest and simple monitoring of the bleeding was recommended, which stopped the next day. The pregnancy was completed at 39 weeks, when a viable female was born with a body weight of 3,110g.

Fig. 4 Eight-week ultrasound

230

V. Drakopoulos and P. Manousopoulos

Fig. 5 Area of attachment of the trophoblast to the maternal wall

Fig. 6 The outline of the area on the ultrasound and the corresponding AFIC

5 Conclusions The results show that the proposed method can be useful for the identification, reconstruction and analysis of sonogram (pregnancy ultrasound) structural elements. It follows from the above that it is very important—depending on the gestational age and the problem that the obstetrician is called upon to face each time—to have detailed

Obstetric Ultrasound Modelling and Analysis with Fractal Interpolation …

231

information regarding the surface or structure of an organ. Threatening pathologies such as placental abruption or skull and genital malformations make the need for quantification of texture information imperative. Many times, the diagnosis is based on the sonographer’s ability to imagine the shape and texture of the various organs of the fetus, while after five to six examinations acute mental fatigue occurs which leads to diagnostic errors and deviations from the correct assessment. To avoid such situations, we intend to extend our research to 3D ultrasound using fractal interpolation surfaces in the future. Acknowledgements The authors would like to thank Athanasia Karamani, M.D. for her helpful advice on various technical and medical issues examined in this article.

References 1. M. Ali, T.G. Clarkson, Using linear fractal interpolation functions to compress video images. Fractals 2(3), 417–421 (1994) 2. M. Ali, X. Meng, T.G. Clarkson, J.G. Taylor, “Using fractal interpolation function encoded ultrasonic signals to train a neural network”, IEE, 5/1–5/3 (1995) 3. M.F. Barnsley, Fractal functions and interpolation. Constr. Approx. 2, 303–329 (1986) 4. M.F. Barnsley, Fractals everywhere, 3rd ed., Dover Publications, Inc. (2012) 5. M.F. Barnsley, S. Demko, Iterated function systems and the global construction of fractals. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 399(1817), 243–275 (1985) 6. O.I. Craciunescu, S.K. Das, J.M. Poulson, T.V. Samulski, Three-dimensional tumor perfusion reconstruction using fractal interpolation functions. IEEE Trans. Biomed. Eng. 48(4), 462–473 (2001) 7. V. Drakopoulos, Fractal-based image encoding and compression techniques, Commun. – Scientific Letters of the University of Žilina 15(3), 48–55 (2013) 8. K.J. Falconer, Fractal geometry: mathematical foundations and applications, 3rd edn. (Wiley, Chichester, 2014) 9. D.P. Ferro, M.A. Falconi, R.L. Adam, M.M. Ortega, C.P. Lima, C.A. de Souza, I. Lorand-Metze, K. Metze, Fractal characteristics of May-Grünwald-Giemsa stained chromatin are independent prognostic factors for survival in multiple myeloma. PLoS ONE 6(6), e20706 (2011) 10. J.E. Hutchinson, Fractals and self similarity. Indiana Univ. Math. J. 30(5), 713–747 (1981) 11. F.E. Lennon, G.C. Cianci, N.A. Cipriani, T.A. Hensing, H.J. Zhang, C.T. Chen, S.D. Murgu, E.E. Vokes, M.W. Vannier, R. Salgia, Lung cancer-a fractal viewpoint. Nat. Rev. Clin. Oncol. 12(11), 664–675 (2015) 12. P. Manousopoulos, V. Drakopoulos, T. Theoharis, Curve fitting by fractal interpolation. Trans. Comput. Sci. 1, 85–103 (2008) 13. P. Manousopoulos, V. Drakopoulos, T. Theoharis, Parameter identification of 1D fractal interpolation functions using bounding volumes. J. Comput. Appl. Math. 233(4), 1063–1082 (2009) 14. P. Manousopoulos, V. Drakopoulos, T. Theoharis, Parameter identification of 1D recurrent fractal interpolation functions with applications to imaging and signal processing. J. Mathem. Imaging Vision 40(2), 162–170 (2011) 15. P. Manousopoulos, V. Drakopoulos, T. Theoharis, P. Stavrou, Effective representation of 2D and 3D data using fractal interpolation, in Proceedings of International Conference on Cyberworlds, pp. 457–464. IEEE Computer Society (2007) 16. P.R. Massopust, Fractal functions, fractal surfaces and wavelets (Academic Press, San Diego, CA, 1994)

232

V. Drakopoulos and P. Manousopoulos

17. D.S. Mazel, M.H. Hayes, Hidden-variable fractal interpolation of discrete sequences, in Proc. Int. Conf. Acoust., Speech, Signal Processing, pp. 3393–3396 (1991) 18. D.S. Mazel, M.H. Hayes, Using iterated function systems to model discrete sequences. IEEE Trans. Signal Process. 40(7), 1724–1734 (1992) 19. D.S. Mazel, Representation of discrete sequences with three-dimensional iterated function systems. IEEE Trans. Signal Process. 42(11), 3269–3271 (1994) 20. K. Metze, R. Adam, J.B. Florindo, The fractal dimension of chromatin—a potential molecular marker for carcinogenesis, tumor progression and prognosis. Expert Rev. Mol. Diagn. 19(4), 299–312 (2019) 21. C.M. P˘acurar, B.R. Necula, An analysis of COVID-19 spread based on fractal interpolation and fractal dimension. Chaos, Solitons Fractals 139, 110073 (2020) 22. A.I. Penn, M.H. Loew, Estimating fractal dimension of medical images, Proceedings of SPIE— The International Society for Optical Engineering, 2710 (1996) 23. A.I. Penn, M.H. Loew, Fractal dimension of low-resolution medical images, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 1163–1165 (1996) 24. A.I. Penn, M.H. Loew, Estimating fractal dimension with fractal interpolation function models. IEEE Trans. Med. Imaging 16(6), 930–937 (1997) 25. A.I. Penn, L. Bolinger, M.D. Schnall, M.H. Loew, Discrimination of MR images of breast masses with fractal-interpolation function models. Acad. Radiol. 6(3), 156–163 (1999) 26. D.P. Popescu, C. Flueraru, Y. Mao, S. Chang, M.G. Sowa, Signal attenuation and box-counting fractal analysis of optical coherence tomography images of arterial tissue. Biomed. Opt. Express 1(1), 268–277 (2010) 27. J.R. Price, M.H. Hayes, Resampling and reconstruction with fractal interpolation functions. IEEE Signal Process. Letters 5(9), 228–230 (1998) 28. S. T˘alu, Fractal analysis of normal retinal vascular network. Oftalmologia 55(4), 11–16 (2011) 29. J.L. Véhel, K. Daoudi, E. Lutton, Fractal modeling of speech signals. Fractals 2(3), 379–382 (1994)

Healthcare/Medical Networking and Care Sharing

Predicting the Relationship Between Meal Frequency and Type 2 Diabetes: Empirical Study Using Machine and Deep Learning Yiman Hunag, Farnaz Farid, and Basem Suleiman

1 Introduction Diabetes is a metabolic disorder characterized by long-term high blood sugar levels [12]. As shown by International Diabetes Federation, diabetes can be published as fasting blood glucose .≤100 mg/dL (.≤5.5 mmol/L), HbA1c.