The Digital Transformation of the Healthcare System: Healthcare 5.0 9781032393346, 9781032413754, 9781003357810

Table of contents :
Cover
Half Title
Title Page
Copyright Page
CONTENTS
List of tables
Preface
About the author
1. The birth of the healthcare system
1.1. The origins of healthcare fundamentals
1.2. Healthcare system in chains
2. Changes in the healthcare organization to prepare for the digital era
2.1. How paternalism replaced individual sovereignty
2.2. Why a greater reliance on individual sovereignty brings a revolutionary change from treatment to prevention
2.3. Why individual sovereignty can be accelerated in the healthcare system of the digital era
2.4. QALY journey in the healthcare sector
2.5. From a decision-maker perspective to a holistic view on health
2.6. Why a holistic perspective on health will become the revolutionary change from treatment to prevention
2.7. Why adoption of a holistic axiom can be accelerated in the digital transformation
3. How to change the mindset to embrace opportunities of the digital revolution in healthcare?
3.1. How to switch the mindset to individual sovereignty and holistic health
3.2. Mindset shift towards individual sovereignty
3.3. Mindset shift towards holistic health
3.4. Why the digital revolution will accelerate a mindset shift in healthcare
3.5. How the digital revolution will accelerate the mindset shift
3.6. How can the digital revolution change the mindset to individual sovereignty?
3.6.1. How can the digital revolution introduce more cognitive trust?
3.7. How can the digital revolution change the mindset to holistic health?
4. Are we ready for healthcare 5.0?
4.1. Why healthcare 5.0?
4.2. Governing rules of healthcare 5.0
4.2.1. What kind of governing rule does individual sovereignty imply?
4.2.2. What does a governing rule axiom of holistic approach to healthcare imply?
4.3. Description of healthcare 5.0
4.3.1. Healthcare 5.0 – from the power of knowing to the power of action
4.3.2. Healthcare 5.0 – Google health engine
4.3.3. Healthcare 5.0 – repository of preventive measures
4.3.4. Healthcare 5.0 – ambulatory care
4.3.5. Healthcare 5.0 – hospital care
4.3.6. Healthcare 5.0 – safety first!
4.3.7. Healthcare 5.0 – financing model
4.3.8. Healthcare 5.0 – identifying unmet medical needs
4.3.9. Healthcare 5.0 – ensuring an outcome-based payment model
4.3.10. Healthcare 5.0 – the role of patients' community
4.3.11. Healthcare 5.0 – pricing and reimbursement of medical procedures
4.3.12. Healthcare 5.0 – healthcare insurance
4.3.13. Healthcare 5.0 – digital health accounts
4.3.14. Healthcare 5.0 – standardization to mitigate the risk of inadequate care across the globe
4.3.15. Healthcare 5.0 – collaborative synergy between the real and digital world to mitigate the risks of miscare of the disadvantaged
4.3.16. Healthcare 5.0 – data-sharing culture
5. Case study
5.1. Study objective, questionnaire and methodological approach
5.1.1. Part I
5.1.2. Part II Methodological approach
5.1.3. Part III
5.1.4. Part IV
5.2. Results
5.2.1. Part I
5.2.2. Part II
5.2.3. Part III
5.2.4. Part IV
5.3. Discussion
Appendix
Questionnaire
Concluding remarks
Index

Citation preview

THE DIGITAL TRANSFORMATION OF THE HEALTHCARE SYSTEM Healthcare 5.0

THE DIGITAL TRANSFORMATION OF THE HEALTHCARE SYSTEM

This book examines how the digital revolution has reorganized the model of healthcare during the COVID-19 pandemic and argues for a continued paradigm shift to digital healthcare. Katarzyna Kolasa sets the vision of healthcare 5.0 that relieves the burden on limited healthcare resources and creates better health outcomes by switching the focus from treatment to prediction and prevention. She advocates for a patient-centric ecosystem that empowers patients to take control of their health via new knowledge-based technologies such as next-generation sequencing (NGS), nanotechnology, artificial intelligence and digital therapeutics. Highlighting the mindset shift needed to transform healthcare and outlining in detail a futuristic vision of healthcare 5.0, this book will be of interest to academics and professionals of health policy, health economics and digital health. Katarzyna Kolasa is the founder of the first Master’s program in Health Economics and Big Data (HEBDA), the first edition of which was financed by EU Power Grant 2018. In partnership with the Polish Medical Research Agency, Deloitte Digital and the Polish Central Hospital of the Ministry of Interior Affairs, she established the first Digital Health Start Me Up, a half-year introduction program into digital health dedicated to startups and healthcare experts. She is also the founder of the Global Special Interest Group Digital Health at ISPOR. Passionate about Big Data, Katarzyna led the first project of machine learning adaptation for the optimal allocation of CT scanners granted by the Polish Ministry of Health.

THE DIGITAL TRANSFORMATION OF THE HEALTHCARE SYSTEM Healthcare 5.0

Katarzyna Kolasa

Designed cover image: © Getty Images First published 2024 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 Katarzyna Kolasa The right of Katarzyna Kolasa to be identified as author of this work has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-032-39334-6 (hbk) ISBN: 978-1-032-41375-4 (pbk) ISBN: 978-1-003-35781-0 (ebk) DOI: 10.4324/b23291 Typeset in Bembo by KnowledgeWorks Global Ltd.

CONTENTS

List of tables Preface About the author 1 The birth of the healthcare system

viii ix xvi 1

1.1 The origins of healthcare fundamentals  1 1.2 Healthcare system in chains  2 2 Changes in the healthcare organization to prepare for the digital era 2.1 How paternalism replaced individual sovereignty  9 2.2 Why a greater reliance on individual sovereignty brings a revolutionary change from treatment to prevention  13 2.3 Why individual sovereignty can be accelerated in the healthcare system of the digital era  17 2.4 QALY journey in the healthcare sector  21 2.5 From a decision-maker perspective to a holistic view on health  23 2.6 Why a holistic perspective on health will become the revolutionary change from treatment to prevention  25 2.7 Why adoption of a holistic axiom can be accelerated in the digital transformation  28

9

vi  Contents

3 How to change the mindset to embrace opportunities of the digital revolution in healthcare?

39

3.1 How to switch the mindset to individual sovereignty and holistic health  39 3.2 Mindset shift towards individual sovereignty  41 3.3 Mindset shift towards holistic health  44 3.4 Why the digital revolution will accelerate a mindset shift in healthcare  49 3.5 How the digital revolution will accelerate the mindset shift 51 3.6 How can the digital revolution change the mindset to individual sovereignty?  52 3.6.1 How can the digital revolution introduce more cognitive trust?  55 3.7 How can the digital revolution change the mindset to holistic health?  57 4 Are we ready for healthcare 5.0? 4.1 Why healthcare 5.0?  69 4.2 Governing rules of healthcare 5.0  73 4.2.1 What kind of governing rule does individual sovereignty imply?  73 4.2.2 What does a governing rule axiom of holistic approach to healthcare imply?  74 4.3 Description of healthcare 5.0  76 4.3.1 Healthcare 5.0 – from the power of knowing to the power of action  76 4.3.2 Healthcare 5.0 – Google health engine  76 4.3.3 Healthcare 5.0 – repository of preventive measures 78 4.3.4 Healthcare 5.0 – ambulatory care  80 4.3.5 Healthcare 5.0 – hospital care  85 4.3.6 Healthcare 5.0 – safety first!  90 4.3.7 Healthcare 5.0 – financing model  93 4.3.8 Healthcare 5.0 – identifying unmet medical needs 95 4.3.9 Healthcare 5.0 – ensuring an outcome-based payment model  96 4.3.10 Healthcare 5.0 – the role of patients’ community 97

69

Contents vii

4.3.11 Healthcare 5.0 – pricing and reimbursement of medical procedures  98 4.3.12 Healthcare 5.0 – healthcare insurance  103 4.3.13 Healthcare 5.0 – digital health accounts  105 4.3.14 Healthcare 5.0 – standardization to mitigate the risk of inadequate care across the globe  110 4.3.15 Healthcare 5.0 – collaborative synergy between the real and digital world to mitigate the risks of miscare of the disadvantaged  111 4.3.16 Healthcare 5.0 – data-sharing culture  112 5 Case study

127

5.1 Study objective, questionnaire and methodological approach 127 5.1.1 Part I  128 5.1.2 Part II Methodological approach  128 5.1.3 Part III  130 5.1.4 Part IV  130 5.2 Results 130 5.2.1 Part I  132 5.2.2 Part II  136 5.2.3 Part III  139 5.2.4 Part IV  149 5.3 Discussion 149 Appendix 157 Questionnaire 157 163 Concluding remarks Index168

TABLES

5.1 Digital/analogue model 130 5.2 Description of digital healthcare models 131 5.3 Sociodemographic characteristics of the participants 132 5.4 Financial independence of the participants 133 5.5 Health self-assessment of the participants 134 5.6 Healthcare system satisfaction evaluation 135 5.7 Monthly financial contribution awareness 136 5.8 Access to free healthcare prevention programs  136 5.9 Healthcare out-of-pocket payments as part of disposable income  137 5.10 Healthcare system selection – sociodemographic characteristics138 5.11 Allocation of responders across the scenarios along marginal trade-off and relative difference estimation 139 5.12 Probability of selection of the digital model for different MTO139 5.13 Coefficients of the logistic regression analysis of the probability of choosing the digital model 140 5.14 Characteristics of the most preferred model – AI-driven VA instead of GP 141 5.15 Communication with the VA 142 5.16 Evaluation of 24/7 health monitoring 143 5.17 Diagnostic tests – delivery selection rating 144 5.18 Preference toward specialized care globally vs. country-specific rating 145 5.19 Medication delivery rating 146 5.20 Selection of hospital care – virtual vs. physical rating 147 5.21 Willingness to change the healthcare system when it is free 148 5.22 Willingness to change the healthcare system when payment is required 150 5.23 Data-sharing willingness among participants 151

PREFACE

January 10, 2022, Sopot Poland The impact of the digital revolution on the organization of the healthcare system has received little attention. Since the publication of Kenneth Arrow’s paper titled Uncertainty and the Welfare Economics of Medical Care more than 60 years ago, the guiding principles of medical care have been firmly established on the rationale of information asymmetry between doctors and patients. The idea of a free market economy was replaced with a third-party agent’s responsibility for healthcare resource redistribution. Paternalism and the decision-maker’s perspective became key factors of the healthcare system’s organization. The intention of this book is to address the question of how far the digital transformation will redefine these guiding principles. Will the tsunami of data replace the rationale of information asymmetry with patient empowerment? What is the future of family doctors in the era of virtual artificial intelligence (AI) assistants? Will the Internet of Things (IoT) change the borders of the healthcare system? Are we able to objectively quantify the value of machine learning (ML) algorithms predicting medical incidences in the system defined by limited payer’s budgets? Do we still need third-party payers in the era of consumer-driven healthcare markets? Such questions may be further multiplied with the arrival of innovative digital solutions that can’t be categorized in the old financing system focused mainly on the role of healthcare professionals acting on behalf of patients. My contribution to the topic of digital transformation in the field of medicine is to discuss the changes of healthcare system organization to allow faster adoption of digital solutions. The question is, what innovative digital health technologies await and will we be able to evaluate the value of innovation and successfully implement it into clinical practice? Will the general public embrace the digital health revolution? Are we ready for consumer market healthcare?

x  Preface

Should we trust the digital innovation? Will we accept greater responsibility for our own well-being? Is it time for a virtual healthcare ecosystem? Will clinicians accept the superiority of data-driven decision-making instead of their own intuitional judgement? START -Does the healthcare system work? It does not. -Why? -Well, you know we are living longer, and every new technology gets more expensive. That’s why we cannot afford everything … -Ok, yes, that’s true. But wait, WHY? Really? Let me explain … Many scientific publications and reports written by health economists start with the following sentence: “It is challenging to strike the balance between limited financial resources and unlimited healthcare needs.” I have said it too until recently, when I started to ask myself whether it really holds true in the era of digital healthcare. Do we still have to experience limited financial resources in a reality driven by the growing number of digital solutions that can help us predict, prevent and, consequently, save healthcare resources? The digital transformation now meets the analogue history of many clinical successes, but we still see plenty of examples of inefficiencies and access denials due to a limited healthcare budget. The World Health Organization (WHO) estimates that total healthcare spending rose on average by 6% and 4% in low/ middle and high-income countries, respectively, between 2000 and 2016.1 Average healthcare spending is as high as 10% GDP with US healthcare costs as high as 18% GDP. Limited healthcare financial resources are undeniable facts. Looking retrospectively, we need to acknowledge that the healthcare system’s mode of operation has been mainly reactive. For those who don’t agree, I would like to recall some basic statistics. Overall public and private expenditure on preventive care accounted for 2.8% of total health expenditures in 2018 in the EU.2 It was slightly higher in the USA, but still, it is an astonishingly low number.3 Does it look weird to you as well? Haven’t we focused enough attention on prevention? Has the healthcare system waited until disease onset to render support? I believe that in the search for answers, we should start with the basic discussion about the guiding principles that define healthcare system organization. The underlying assumption of the information asymmetry led to the empowerment of clinicians who became decision-makers, imposing the requirement on patients to be compliant with their clinical advice. Since the medical professional mission has always been designed to treat, the healthcare system organization is mainly focused on providing access to effective and safe treatments, less so on prevention. It is the structure that was set up in order to ensure doctors and nurses are equipped with the right tools to treat patients. The healthcare system

Preface xi

organization is centred around the relationship between healthcare professionals and patients. The preventive measures dedicated solely to healthy individuals have been usually considered as simply lifestyle modalities keeping them outside of healthcare system. Due to the notion of the unpredictability of disease, we introduced the idea of third-party agents that can insure individuals against catastrophic healthcare payments when the worst happens. Following the solidarity principle, we defined individual financial contribution based on income status without any financial incentives for those who are proactive in health self-care. Eventually, we disconnected prevention from healthcare financing too. Still, in recent years, a growing body of evidence has been emerging that indicates how much that prevention matters. For example, it has been proven that the modifiable behaviours and community exposures played a significant role in 60% of preventable deaths.4 According to the WHO, it was revealed that non-communicable diseases, such as heart disease and stroke, diabetes, cancer, chronic lung diseases, low-back pain and poor mental health constituted up to 85% of the global burden of disease across different European settings in 2013.5 At the same, we do know that up to 32% of the deaths of certain types of cancers are preventable. In addition to that, as many as 36% of premature deaths due to circulatory diseases (mainly heart attack and stroke) could be prevented thanks to early detection.6 Has the time arrived to change the role of prevention in our lives? Will the digital era revolutionize the way we organize healthcare? It is undeniable that the growing wealth of data allows us to quantify the health risks more accurately. Already today, we are able to test for 143 cancer predisposing genes. There are more than 8,900 algorithms to predict health outcomes published so far. It seems as if the digital era introduced a third partner to the relationship between healthcare professionals and patients. It is data. Does it mean the digital transformation will change the mode of healthcare system organization from reactive to proactive? Shall we believe that the digital era has brought us a unique opportunity to depart from the notion of limited resources if we switch gears from treatment to prevention? If data are limitless, should we still be concerned with limited financial resources? I have been working in health economics for more than 20 years in both the pharmaceutical and MedTech industries across different geographical settings. I have never experienced such revolutionary changes as we observe today. If someone doubts my enthusiasm, let me kindly bring to your intention the basic statistics about the number of patents granted each year in the EU. In 2021, the top two categories were medical technology and digital technology (14,000 approximately in each of these two categories), while pharmaceuticals only had 8,500 patents (https://www.epo.org/about-us/annual-reports-statistics/ statistics/2020/statistics/patent-applications.html#tab3).7 Most of the pharmaceutical companies have already established digital health units. According to CB Insights, the investments into digital health startups reached US$57 billion

xii  Preface

in 2021, double compared to 2020.8 It is a clear indication that the data are the driver of change in healthcare. In this book, I will try to present the vision of a healthcare ecosystem tailored to our own medical needs, desires and expectations. It is a data-driven healthcare system that mitigates the risk of disease onset and disease consequences. Its key objective is to prevent and treat early enough. In order to make that vision a reality, I think that we need to open our mind to the idea of a new organization of the healthcare system first. I will elaborate further on how to capture the opportunities provided by the digital revolution to abandon the notion of information asymmetry and change from clinician to patient empowerment. The digital revolution will eventually introduce everyone into the decision-maker seat, making him or her responsible for his/her own destiny. The abandonment of paternalism with the changing role of governmental agencies into the educator and regulator would be the optimal framework for a real evidence-driven patient-centric healthcare system. I believe that the transformation of the healthcare organization to the digital era requires a mindset change. In fact, it is not the lack of innovation that is the hurdle, but rather slow adoption of available innovative data-driven solutions that keeps us in the limbo of limited healthcare resources. Therefore, to change the mindset, it is necessary for the healthcare system to redefine itself and adopt innovation quickly and smartly. To lead my discourse on the relevant level, I applied Kuhn’s approach, known as paradigm shift, outlined in The Structure of Scientific Revolutions. I will elaborate on why I believe we are experiencing revolution defined by Kuhn as paradigm shift, and, most importantly, what exactly we need to change to shift our mind to a new healthcare system. There are many ongoing debates about the dysfunction observed in the healthcare sector; however, there is still limited research conducted on the systematic level to focus on the overall concept and structure of the healthcare system revolution in the digital era. That’s the gap I am hoping to fill in with my scientific discourse here. Following Kuhn’s logic, I will choose the pathway to paradigm shift through the discovery of anomalies that “cannot, despite repeated effort, be aligned with professional expectation.” The growing awareness of these anomalies finally “subvert the existing tradition of scientific practice—then begin the extraordinary investigations that lead the profession at last to a new set of commitments, a new basis for the practice of science.” As a result, a new paradigm often is born in the form of crisis that leads to revolution and finally born of a new approach to the reality. This is the purpose of this book. I fully admit that my aspirations are bold. Looking to the future vision of the healthcare system, I use my health economics lenses to define the readiness for faster and smarter adoption of digital health innovation. I think the change is real. The future is bright if we have the courage to make that revolutionary jump into this new digital reality.

Preface xiii

A new mindset is the key success factor for a paradigm shift of healthcare. We need to replace air-polluting diesel cars with electric self-driving vehicles. I will argue as well that we do not need a governmental agency to steer our healthcare ecosystem anymore. We can select our third-party agent, as we choose a car manufacturer, that helps us design our own health objective function equipped with a set of various ML algorithms. If we fuel our cars with our own data, we will be able to plan and conduct the journey throughout our lifespan on our own terms. We need, however, a framework of safeguards that ensure whether the digital solution we choose is safe and reliable and provides the required accuracy. It is not easy, as it does require a paradigm shift or, more precisely, a mindset shift and, eventually, healthcare redefinition. As Kuhn notes himself, it is the unstable process of formation of new scientific fundamentals. In the first period after the formation of the new paradigm happens, an overlap between the old and new paradigms may occur. There might a number of new paradigms competing against each other. As Kuhn notes, these paradigms are “incommensurable” and there exists no objective way of assessing their relative merits against each other. Still, one finally becomes strong enough to provide a new source of the methods and approaches to finding solutions. Consequently, new science is established. In this book, I will try to propose my own vision of the healthcare system. I hope it will encourage others to propose other versions of a paradigm shift. My ambition is to present a new healthcare model for a smart digital society that targets primarily “how to prevent” and only secondly “how to treat.” This new healthcare system would be defined on new guiding principles such as individual sovereignty and the holistic meaning of health. It is not a healthcare system defined by outpatient clinics and hospital buildings. I call this healthcare 5.0 to emphasize that it is a virtual ecosystem fuelled by data arriving from multiple digital endpoints to the personalized ML algorithm, which is calibrated to the health risk profile and holistic definition of health of each individual. It is designed for empowered patients ready to use digital health solutions with the support of a virtual assistant daily. It is meant for a smart digital society of the future without geographical and physical boundaries. The understanding of how the healthcare is to evolve to version 5.0 is important not only for decision-makers and clinicians to embrace their new roles in the digital reality, but even, most importantly, for developers of digital health technologies. My vision is of a consumer healthcare market. However, we will ensure the structure of safeguards that will define a set of very specific requirements towards the measurement accuracy, safety, adoptability, interoperability as well as user-friendliness and others. The majority are going to be new requirements not observed in other commodities markets. The empowerment of patients as consumers will produce not only greater price competition, but also greater developers responsibility compared to other non-health-related commodities. We need to prepare ourselves for the new future and understand patients’ needs

xiv  Preface

and opportunities generated within the digital healthcare ecosystem. Therefore, hopefully, this book will be useful for health technologies’ developers to understand the specific requirements of the present and future healthcare market focused on patients’ needs as well. There are five chapters: In the first chapter, I offer a short introduction to explain the scientific framework of healthcare organizations and review the origins of key principles centered around the principle of information asymmetry. In the second chapter, I will justify my selection of two renewed axioms that could define the healthcare organization of the digital era: individual sovereignty and holistic approach to health. I will deduct WHY exactly the non-existence of these two axioms is the source of the current crisis with healthcare systems. At the same time, I will explain HOW to make them the source of revolutionary change towards the digital revolution and shift the focus from treatment to prevention. I will justify my position with examples of studies as well as observations of recent events. In the third chapter, I will describe the paradigm change from treatment to prevention and explain the mindset shift that needed to make the digital revolution real for healthcare. In the fourth chapter, I will describe my futuristic vision of healthcare 5.0 and highlight key opportunities for digital health developers where the change is likely to happen. In the last chapter, I will describe the objective and methodology and present findings from the pilot study applying the social welfare function and direct questions as the proof of concept for healthcare 5.0. It is a proof of concept with 100 representatives of generation Y and Z from Poland. The choice of Central Eastern Europe will provide a very special insight into the mindset of those who were born in the era of smartphones as well as the mindset of those who are observing healthcare challenges in a greater scale than in more affluent Western European countries. It is the generation of potential beneficiaries of healthcare 5.0.

NOTES 1 Xu K, et al. Public Spending on Health: A Closer Look at Global Trends. 2018. 2 Eurostat. “3% of Healthcare Expenditure Spent on Preventive Care,” Eurostat, 18 Jan. 2021, https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20210118-1 3 Vandebrouck, L. “What the U.S. Gets Right about Healthcare,” MedCity News, 13 Mar. 2020, https://medcitynews.com/2020/03/what-the-u-s-gets-right-abouthealthcare/ 4 McGinnis, JM, et al. “The Case for More Active Policy Attention to Health Promotion,” Health Affairs (Millwood), Mar.–Apr. 2002, pp. 78–93. 5 Merkur, S, et al. “Promoting Health, Preventing Disease: Is There an Economic Case?,” World Health Organization, 2013, https://www.euro.who.int/__data/assets/ pdf_file/0004/235966/e96956.pdf 6 “Health at a Glance 2019: OECD Indicators,” OECD Publishing, Paris, https://doi. org/10.1787/3b4fdbf2-en

Preface xv

7 “Trends in Patenting 2020,” European Patent Office, https://documents.epo.org/ projects/babylon/eponet.nsf/0/837DBDFC91C99042C12586950032FDBD/$FILE/ epo_patent_index_2020_infographic_en.pdf 8 “How Health Care Is Turning into a Consumer Product,” The Economist, 15 Jan. 2022, https://www.economist.com/business/how-health-care-is-turninginto-a-consumer-product/21807114

BIBLIOGRAPHY Eurostat. “3% of Healthcare Expenditure Spent on Preventive Care.” Eurostat, 18 Jan. 2021, https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20210118-1 OECD (2019), “Health at a Glance 2019: OECD Indicators.” OECD Publishing, Paris. https://doi.org/10.1787/3b4fdbf2-en “How Health Care Is Turning into a Consumer Product.” The Economist, 15 Jan. 2022, https://www.economist.com/business/how-health-care-is-turning-into-aconsumer-product/21807114 McGinnis, JM, et al. “The Case for More Active Policy Attention to Health Promotion.” Health Affairs (Millwood), Mar.–Apr. 2002, pp. 78–93. Merkur, S, et al. “Promoting Health, Preventing Disease: Is There an Economic Case?” World Health Organization, 2013, https://www.euro.who.int/__data/assets/pdf_ file/0004/235966/e96956.pdf “Trends in Patenting 2020.” European Patent Office, https://www.epo.org/about-us/ annual-reports-statistics/statistics/2020/patenting-trends. html, accessed June 2022 Vandebrouck, L. “What the U.S. Gets Right about Healthcare.” MedCity News, 13 Mar. 2020, https://medcitynews.com/2020/03/what-the-u-s-gets-right-about-healthcare/ Xu, K, et al. Public Spending on Health: A Closer Look at Global Trends, Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO.

ABOUT THE AUTHOR

Katarzyna Kolasa, PhD • P  rofessor of Health Economics at Kozminski University, Warsaw, Poland • ISPOR Digital Health SIG Founder and past Chair • ISPOR Course Leader of the Role of Digital Endpoints in the Value Generation for Health Technologies • Leader of Digital Health Start Me Up at Kozminski University Driven with a passion for health economics, Katarzyna has more than 25 years of academic and industry experience in the field of health economics. She is VP HEOR at Parexel, leading the innovative modelling team and digital health projects. Katarzyna leads Digital Health Start Me Up, a six-month program for digital health transformation leaders at Kozminski University. She is also the founder of the International Master Program Health Economics & Big Data (HEBDA), financed by EU Power Grant at the Kozminski University. Katarzyna is a past Chair and founder of the Global Special Interest Group Digital Health at ISPOR (The Professional Society for Health Economics and Outcomes Research). Among other activities, she organized and moderated ISPOR SIG webinars Data Privacy as a Hurdle or Enabler for Digital Health Implementation? and The Legal Framework and Digital Endpoint Adoption: The How, What and Why. She is also the founder and course leader for the ISPOR short course, “The Role of Digital Endpoints in the Value Generation for Health Technologies.” Katarzyna has authored more than 50 JCR publications and has spoken at more than 50 scientific conferences worldwide.

About the author xvii

Her PhD was completed through courses at the University of York, University of Lund, and University of Bergen as well as during the International Doctoral Courses in the Health Economics and Policy organized by the Swiss School of Public Health. Her habilitation was dedicated towards the optimal allocation of scarce healthcare resources. In the past, Katarzyna held various regional and global leadership positions where she gained experience with the pricing and reimbursement challenges in both pharmaceutical and medtech industries worldwide. At AstraZeneca and BiogenIdec, Katarzyna held global and European positions, respectively. At Bristol Myers Squibb and Lundbeck, she led Market Access teams in the Central Eastern European (CEE) and Nordic regions. After being East and Central European Senior HEOR Director at GE Healthcare, she supported various medtech companies with Real World Evidence (RWE) generation and reimbursement submissions for the BD’s medical devices as well as digital health solutions globally for over four years. Passionate about Big Data, she led the project of machine learning adaptation in search for the optimal allocation of CT scanners granted by the Polish Ministry of Health as well.

1 THE BIRTH OF THE HEALTHCARE SYSTEM

1.1  The origins of healthcare fundamentals The scientific explanation of the current healthcare system organization was described by Kenneth Arrow in his famous paper from 1962 titled Uncertainty and the Welfare Economics of Medical Care.1 This chapter introduces a new scientific field – health economics. Its separation from economics was based on the understanding of its distinctive features related to health with the underlying assumption of information asymmetry. Among other explanations of the specific nature of healthcare systems, there are three arguments worth highlighting in Arrow’s discourse. First, it was outlined how the demand for healthcare is truly a proxy of demand for health. Arrow assumed that the need for healthcare is difficult to plan, hence the claim of unpredictability and the importance of third-party agents. Second, he assigned a specific role to clinicians that should “depart from profit motive” and search for the best treatment for patients based on ethical grounds. Third, he pointed towards the uncertainty of treatment outcomes is due to limited knowledge with respect to how a given health technology will impact the health of patients. Arrow’s description of the distinctive features of today’s healthcare system explains the driving forces behind the healthcare system organization irrespective of geographical, cultural or societal settings. In fact, Arrow’s discourse gives us a clear justification of two main principles of health economics: paternalism and the decision-makers perspective, which I call axioms to highlight their importance. I believe that these two conditions truly define today’s paradigm of the healthcare system and will become the origin of crisis in the digital era. The crisis towards revolution is defined by Kuhn as paradigm shift2 that will lead to a new model of healthcare based on new axioms. But let us first travel back to the roots of health economics to understand the full picture of how paternalism and DOI: 10.4324/b23291-1

2  The birth of the healthcare system

the decision-maker perspective were born and why these two axioms define the current healthcare system organization described by Arrow in the 1960s.

1.2  Healthcare system in chains The science of economics has always been about efficiency. From a theoretical standpoint, the challenge is that efficiency can be defined with different levels of advancement. Technical efficiency is achieved when production is organized to minimize the inputs required to produce a given output. Cost-effectiveness efficiency is achieved when production is organized to minimize the cost of producing a given output. Allocative efficiency is achieved when resources are produced and allocated to produce the “optimal” level of each output and to distribute the outputs in line with the value consumers place on them. The quest for efficiency means, in fact, the search for the last one, i.e., optimal resource allocation. Looking retrospectively, there have been several attempts to work efficiency into the decision-making process, all of which are truly relevant and important for the understanding of the origin of health economics. First, it was classical economics that were born during the Industrial Revolution thanks to Adam Smith, who is considered the greatest propagator of the free market, which regulates itself with a free pricing policy.3 Price was perceived only through the lens of the  production cost. Alongside classical economics, the classical utilitarian approach was launched by Jeremy Bentham and John Stuart Mill.4 The concept of utility was introduced. It was defined as happiness generated from consumption. Assuming the welfare of all individuals could be aggregated in order to estimate the total societal welfare, everyone had the same weight (same voice) in collective decisions. Essentially, the pursuit for efficiency was focused on the maximization of the sum of utilities. The distribution of resources was not of concern at the time. With the launch of the concept of neoclassical economics by Thorstein Veblen, a new approach to efficiency was developed.5 Price was defined from the perspective of the marginal usefulness of the goods purchased. People’s preferences became the determinant of price. It was believed that people act according to their rational preferences independently on the basis of full and relevant information. Within the field of neoclassical economics, there were three different approaches towards efficiency proposed. All of them were extremely important to clarify the notion of health economics. First was “old welfare economics” that assumed measured utility (interpersonal comparability), so the optimal policy was simply set to maximize the sum of the utility of all individuals. With the development of ordinal utility theory (without interpersonal comparability), a second approach of “new welfare economics” was proposed. It replaced the earlier maximization of the sum of utility with Pareto optimality. The author of that approach is Vilfredo Pareto, who presented it in his book

The birth of the healthcare system 3

Manual of Political Economy in 1906.6 He believed that an allocation of goods was optimal when there was no possibility of redistribution in a way where at least one individual would be better off while no other individual ends up worse off. In other words, it states that resources are allocated efficiently only if it is not possible to improve one person’s utility (health) without decreasing another person’s utility (health). Pareto economics concentrated on the search for allocative efficiency under the conditions of a given initial allocation and limited resources. The analysis of Pareto optimality failed to provide a “unique optimum solution,” which represents maximum social welfare. There are many solutions that are optimum based on Pareto criteria. Varying initial allocations can lead to different optimal options, and there is no objective way to compare these alternatives. As a matter of fact, it is impossible to compare a given optimal solution for a specific initial allocation with the non-optimal solution of a different initial allocation. Consequently, the Pareto approach does not provide the opportunity to compare any alternative scenarios and leaves equity consideration outside the search of efficiency defined from the perspective of the optimal allocation. The third form of neoclassical economics tried to address exactly these limitations: the Bergson–Samuelson social welfare function.7 This function estimates the sum of all the individual utility functions based on the assumption of cardinal utility. The Bergson–Samuelson social welfare function is characterized by two assumptions. First, everyone has some subjective measure of well-being that depends on every welfare-relevant dimension, including individual consumption of goods and services. Second, it sets a mechanism on the aggregation of individual well-being functions into a collective function. As such, it allows us to rank each alternative policy defined by different assumptions. This leads to the ranking of multiple options provided some explicit value judgement criteria are established. A social welfare function can be attained by common consensus or it may be forced upon the society. The value judgements accepted by the society can be applied, so both maximization understood by Pareto optimality and distributional considerations are considered. So, both efficiency and equity are achieved simultaneously. For the development of health economics, not only did the quest for optimal allocation play an important role, but also key assumptions of neoclassical welfare were considered significant8: • •

Utility maximization: Individuals can rank the options and choose the most preferred among them according to defined notions of consistency. Individual sovereignty: Individuals are the best judges of their own welfare; any assessment of individual welfare should be based on a person’s own judgement – individual sovereignty asserts that individuals are the best judges of their own welfare; that any assessment of individual welfare should be based on a person’s own judgment. It rejects paternalism, the notion that a third party may know better than the individuals themselves what is best for them.

4  The birth of the healthcare system

•

•

Consequentialism: Any action, choice or policy must be judged exclusively in terms of the resulting, or consequent, effects. The outcome, not process, matters. Welfarism: Excludes all non-utility aspects of the situation.

Given the above tenants, the neoclassical approach was criticized across numerous experts in the healthcare sector and consequently led to the development of the health economics. The mainstream narrative related to the need to replace the concept of utility with an alternative one. It was believed that preferences are too limited to be adopted in the allocative decision-making aspect in healthcare. Instead, it was believed that the focus should be broader. That is how the extra welfarism approach was developed. The word extra is an indication of change from the sole concept of utility towards the concept of health. Extra-welfarist economists substituted the maximization of utility with the maximization of health.9 Starting from that position, the role of the healthcare system should be to increase the overall health of society. The underlying rationale that defines health as the essential good determines the rules of healthcare organizations. It has been recognized as unethical not to provide medical aid to patients in need irrespective of their financial or sociodemographic status. Since access to healthcare cannot be denied in the moment of need, the size of health benefits should not be linked to the economic resources of an individual, either. Consequently, any demand assessment with the willingness-to-pay approach commonly adopted for consumer goods was deemed inappropriate for healthcare. Access to healthcare in life-threatening conditions was to be granted based on the principle of human dignity. As Arrow claimed, clinicians should be driven by ethical choices, not a monetary imperative. In the consequences of denial of the neoclassic approach with utility maximization and health as the optimalization’s objective, the extra welfarism approach moved away from patients’ preferences towards system based on the value assessment of other stakeholders. Here, we must be reminded of the Arrow notion of information asymmetry. It eventually led to the development of the paternalistic approach to the healthcare system. In essence, the rejection of the neoclassic approach not only introduced the concept of health as the driver of the healthcare system, but also led to the emergence of the decision-maker role as an agent for health maximization. The organization of the healthcare system was set up from the perspective of a third-party agent (payer). It is the public or private governing body that takes thr responsibility of providing access to healthcare and defining the allocative pathway for healthcare resources. It was adopted as a safeguard to ensure that the efficiency objective was defined as the health maximization principle under the circumstances of market failure. According to Cunningham, a free market that does not suffer from market failure is the best choice for coordinating allocative decisions on scarce goods.10 But that is not the case for healthcare. Market failure is understood as the circumstances under which services cannot

The birth of the healthcare system 5

be offered and demanded upon the payment.11 It is the situation when free pricing does not allocate resources efficiently in the economy. In the sense of neoclassical economics, defining the situation as a market failure means that the allocation of goods and services by free market would not be considered efficient under the Pareto approach, either. The sources of failure in the healthcare market are obviously related to all three key arguments Arrow mentioned earlier. Let us remind ourselves that he foresaw the unpredictable demand for healthcare and uncertainty arising from the information asymmetry that leads to the advantage of clinicians over patients with respect to knowledge about the disease and potential treatment outcomes. Not only is the demand side “distorted” but also, as Arrow pointed out, the supply side is not defined by the free market ruling but determined, in fact, by the proxy of health needs to be articulated by healthcare personnel who bear ethical responsibility for the health of individuals. In essence, the disconnect between the demand for health and the demand for medical help, as well as the supply distortion, become the key recognition of market failure in the healthcare sector.12 It translates into the inability for companies to either produce effectively due to unpredictable demand or price freely due to ethical considerations. Such a perception of healthcare specificity puts some responsibility on the third party in the organization of the healthcare sector. Its role is mainly to correct failures by regulating the activities of actors. Thus, an innumerable set of entry requirements for healthcare services manufacturers were implemented to ensure the maximization of overall health. Health policy logic was meant to meet the challenge of the scarcity of healthcare resources and the formalized processes of pricing and reimbursement of health technologies. The decision-makers introduced the cost-effectiveness analysis as the allocative tool for limited healthcare resources.13 It centered the healthcare system towards the objective of the maximization of quality-adjusted life years (QALYs).14 A QALY is defined as the combination of years and quality of life achieved with a given healthcare intervention.15 The introduction of QALYs helps the third-party agent with their decision-making process across multiple therapeutic areas and multiple budget issues given the ability to define the objective of each healthcare intervention in a similar fashion. With that brief introduction to the current understanding of the organization of the healthcare system, we can conclude on two major points that define the current structure of the healthcare system. First, treating health as an intrinsic good caused governmental interference in our lives by defining which health problems are to be treated and how to treat them. On the grounds of ethical consideration, we empowered clinicians to become our advocates as we believed it was the only way to eliminate information asymmetry that disfavour patients. It eventually moved us from individual sovereignty towards the paternalistic healthcare system that I named as the first axiom of health economics. Second, we introduced the system of a third party that collects funds from us and distributes them according to healthcare needs. It eventually led to the

6  The birth of the healthcare system

empowerment of the government as decision-maker of allocative processes. In consequence, we arrived with the second axiom of today’s framework of the healthcare system, which addresses the decision-maker perspective driven by the notion of health maximization and defined in the matrix of generic QALY to facilitate the process of pricing and reimbursement of pharmaceuticals and, to some extent, medical devices. Such an approach enables the comparability of health technologies, still paradoxically leaving other healthcare technologies, at most times, outside the transparent allocative decision-making process. In my opinion, it is the paternalism and decision-maker perspective with the outcome defined as QALY that determines the construct of today’s healthcare system organization or, more precisely, as Kuhn names it, defines health economics as a normal science. I name both as axioms that define the current paradigm of health economics. Since Arrow’s publication, there have been obviously many developments, but their magnitude can be explained clearly as the evolution of normal science. Kuhn defines such process as an “early fact-gathering” exercise. Normal science consists of “puzzle solving” within a given framework.16 The key consideration is that it is “usually restricted to the wealth of data that lie ready to hand.” Indeed, experiencing challenges with limited financial resources, we tried to find solutions within the scope of the current framework of both axioms. It was always about bringing more structure and more governmental oversight in terms of health technology assessment or regulatory entry criteria as it was about “actualization achieved by extending the knowledge of those facts that the paradigm displays as particularly revealing, by increasing the extent of the match between those facts and the paradigm’s predictions, and by further articulation of the paradigm itself.” The question is, however, whether in the post - pandemic time, are we still within that paradigm or has anything happened that can indicate a revolution? If so, what are the sources of the crisis? Are Arrow’s assumptions still valid today? Coincidently, they originate the same year as Kuhn’s The Structure of Scientific Revolutions – 1962. This seems to be the year that defines the beginning of the paradigm of health economics, which ends 60 years later …? Let us continue and see. On top of that list, one may add the moral hazard that leads to overconsumption of healthcare services due to either patient or physician misbehaviour and, finally, externalities such as the impact of vaccination. In this past paragraph, I outlined how the nature of health economics emerged from the rejection of neoclassical welfare economics. The distinctive features of health led experts to the acknowledgement of the market failure in the healthcare sector. As a result, both clinicians and third-party agents (budget holders) were empowered to take care of individuals at the time of illness and protect them from disease burden, respectively. I believe that the paternalism and decision-maker perspectives constitute key foundations of today’s healthcare system organization, but also the source of the crisis understood by Kuhn as the “prelude to the paradigm shift.” In the

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next chapter, I will define each axiom in more detail and describe its role in the healthcare system organization during recent years. Second, I will elaborate on that notion of crisis and discuss the drivers of revolutionary change and the potential sources of the novel health economics theories. As Kuhn said, the “emergence of a new theory breaks with one tradition of scientific practice and introduces a new one conducted under different rules and within a different universe.” Therefore, looking into the digital era, I will address the question of why a digital revolution may accelerate the replacement of axioms and consequently drive the healthcare system from treatment to prevention.

NOTES 1 Arrow, “Uncertainty and the Welfare Economics of Medical Care,” The American Economic Review, vol. 53, no. 5, Dec. 1963, pp. 941–73, https://web.stanford.edu/~ jay/health_class/Readings/Lecture01/arrow.pdf 2 Kuhn, The Structure of Scientific Revolutions. 4th ed., The University of Chicago Press, 2012. 3 Reisman, “Adam Smith on Market and State,” Journal of Institutional and Theoretical Economics ( JITE), vol. 154, no. 2, Jun. 1998, pp. 357–83, https://www.jstor.org/ stable/40752070 4 Driver, “The History of Utilitarianism,” Stanford Encyclopedia of Philosophy, 22 Sept. 2014, https://plato.stanford.edu/entries/utilitarianism-history/ 5 Tilman, “Thorstein Veblen and the Disinterest of Neoclassical Economists in Wasteful Consumption,” International Journal of Politics, Culture and Society, vol. 13, no. 2, Winter, 1999, pp. 207–23, https://www.jstor.org/stable/20020018 6 Pareto, Manual of Political Economy, A Critical and Variorum Edition. Oxford University Press, 2014. 7 Samuelson, “Master of Modern Economics,” Palgrave Macmillan, 08 Jan. 2020, https:// link.springer.com/chapter/10.1057/978-1-137-56812-0_12 8 Revill, Suhrcke, Moreno-Serra, Sculpher, Global Health Economics: Shaping Health Policy in Low- and Middle-income Countries. World Scientific Publishing, 2020. 9 Brouwer, Culyer, van Exel, Rutten, “Welfarism vs. Extra-Welfarism,” Journal of Health Economics, vol. 27, no. 2, 27 Mar. 2008, pp.325–38. 10 Cunnigham, “Understanding Market Failures in an Economic Development Context,” Mesopartner, July 2011. 11 Zerbe Jr, McCurdy, “The Failure of Market Failure,” Journal of Policy Analysis and Management, vol. 18, no. 4, 1999, pp. 558–78. 12 Arrow, “Uncertainty and the Welfare Economics of Medical Care,” The American Economic Review, vol. 53, no. 5, Dec. 1963, pp. 941–73, https://web.stanford.edu/~ jay/health_class/Readings/Lecture01/arrow.pdf 13 Thomas, Chalkidou, “Cost-Effectiveness Analysis.” In Health System Efficiency: How to Make Measurement Matter for Policy and Management [Internet], edited by J Cylus, I Papanicolas, PC Smith, European Observatory on Health Systems and Policies, 2016. (Health Policy Series, No. 46.) 6, https://www.ncbi.nlm. nih.gov/books/ NBK436886/. 14 Ibidem 15 https://www.ispor.org/docs/default-source/resources/outcomes-research-guidelinesindex/paper2revised.pdf?sfvrsn=519c9ad4_0 16 Kuhn, The Structure of Scientific Revolutions. A Synopsis from the original by Professor Frank Pajares from the Philosopher’s Web Magazine, https://www.uky.edu/~eushe2/ Pajares/kuhnsyn.html

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BIBLIOGRAPHY Arrow, K. J. “Uncertainty and the Welfare Economics of Medical Care.” The American Economic Review, vol. 53, no. 5, Dec. 1963, pp. 941–73, https://web.stanford.edu/~ jay/health_class/Readings/Lecture01/arrow.pdf Brouwer, Culyer, van Exel, Rutten. “Welfarism vs. Extra-Welfarism.” Journal of Health Economics, vol. 27, no. 2, Mar. 2008, pp. 325–28. Cunnigham, S. Understanding Market Failures in an Economic Development Context. Mesopartner, 2011. Driver, J. “The History of Utilitarianism,” Stanford Encyclopedia of Philosophy, 22 Sept. 2014, https://plato.stanford.edu/entries/utilitarianism-history/ Kuhn, T. S. “The Structure of Scientific Revolutions,” A Synopsis from the original by Professor Frank Pajares from the Philosopher’s Web Magazine, https://www.uky. edu/~eushe2/Pajares/kuhnsyn.html Kuhn, T. S. The Structure of Scientific Revolutions. 4th ed., The University of Chicago Press, 2012. Pareto, V. Manual of Political Economy: A Critical and Variorum Edition. Oxford University Press, 2014. Reisman, D. A. “Adam Smith on Market and State.” Journal of Institutional and Theoretical Economics ( JITE), vol. 154, no. 2, Jun. 1998, pp. 357–83, https://www.jstor.org/stable/ 40752070 Revill, Suhrcke, Moreno-serra, Sculpher. Global Health Economics: Shaping Health Policy in Low- and Middle-Income Countries. World Scientific Publishing, 2020. Samuelson. Master of Modern Economics. Palgrave Macmillan, 08 Jan. 2020, https://link. springer.com/chapter/10.1057/978-1-137-56812-0_12 Thomas, Chalkidou. “Cost–Effectiveness Analysis.” In Health System Efficiency: How to Make Measurement Matter for Policy and Management [Internet], edited by J Cylus, I Papanicolas, PC Smith, European Observatory on Health Systems and Policies, 2016. (Health Policy Series, No. 46.) 6, https://www.ncbi.nlm.nih.gov/books/ NBK436886/ Tilman, R. “Thorstein Veblen and the Disinterest of Neoclassical Economists in Wasteful Consumption.” International Journal of Politics, Culture and Society, vol. 13, no. 2, Winter, 1999, pp. 207–23, https://www.jstor.org/stable/20020018 Zerbe, McCurdy Jr. “The Failure of Market Failure.” Journal of Policy Analysis and Management, vol. 18, no. 4, 1999, pp. 558–78.

2 CHANGES IN THE HEALTHCARE ORGANIZATION TO PREPARE FOR THE DIGITAL ERA

2.1  How paternalism replaced individual sovereignty Paternalism starts when individual freedom finishes. Do you agree? Please let me bring Hutt’s definition to your attention. He said the following about consumer sovereignty: “principle of consumer sovereignty … treats consumer preferences as given and not subject to the theoretician’s scrutiny and judgement.”1 The individual satisfaction is a traditionally accepted way to measure individual welfare. It is regarded as a central departure point for the aggregation of the overall societal welfare. In other words, when assessing what is good for society, the starting point is really to define what makes an individual better off. As far as health is concerned, it was believed, however, that we should not leave any individual to make his/her own decisions due to their insufficient knowledge. This is called the asymmetry of information, as previously described. It leaves people unable to make informed decisions about their health. In other words, information asymmetry means people cannot maximize their satisfaction. Consequently, the demand for healthcare does not reflect the level of demand needed for utility maximization. If demand levels are incorrect, then supply cannot be correct, either. If supply and demand are unbalanced, then prices are skewed, too. Information asymmetry is detrimental to allocative efficiency. It destroys the way we can observe the demand and supply connection in a free market when the customers’ satisfaction drives towards equilibrium. Hence, it was believed that healthcare must be organized differently … The medical professionals possess both scientific and practical knowledge over their patients. Therefore, the healthcare system positioned them as trustful agents with sole accountability for the destiny of the patient pathway to the hope of recovery. The healthcare system has established many regulations on DOI: 10.4324/b23291-2

10  Changes in the healthcare organization to prepare for the digital era

how to ensure clinicians’ empowerment and … much less so when it comes to the regulations on how to ensure patients’ empowerment. In the result, patients gave their full control to medical professionals during their healthcare journey as passengers do to the pilot while boarding a plane. For some, it surely became an effortless experience without any requirement of a better understanding of the circumstances and relief from the stress of decision-making in the light of uncertainty. Usually, it is not cognitive trust but a blind trust driven by the lack of information. Patients are allowed not to possess knowledge about their health problems, and perhaps clinicians use it for their own empowerment. I do not mean financial benefit, but the benefit of being the trusted agent, expert or sometimes the saviour. The luxury position that the healthcare professional has gained over years of “market failures” only deepened the problem of information asymmetry. The divide between experts and nonexperts grew. The patients are not motivated to pursue the information about their health problems. Many times, clinicians accuse patients on wrongdoing with a Google search for knowledge. But what is wrong with patients’ interests in deeper understanding of their health problems and solutions? It’s against the rules of paternalism. We need to understand the fact that the denial of individual sovereignty due to information asymmetry along with the acknowledgement of market failure led to paternalism. What is paternalism about? It is understood as any form of the third-party interference in a person’s decision-making for the purpose to improve individual welfare. In other words, paternalism involves acts of coercion (restricting freedom of choice) aimed at maximizing the overall welfare of society. It is the notion of the superiority of medical professionals empowered by specific regulations established within the healthcare system as well. Dworkin defined paternalism as “interference with a person’s liberty of action justified by reason referring exclusively to the welfare … of the person being coerced.”2 Paternalistic rules interfere with the principle of individuals’ sovereignty when it comes to redefining their own interests. In other words, there is authoritarian power that substitutes one’s judgement for his or her own destiny. However, this does not mean there is a negative intention. Paternalism is driven by the presumption that people should be limited with the right to choose their own course of action as it may harm them or others. The paternalist efforts can be targeted towards the prevention of spread of infectious diseases or modifying a person’s choices for their own good in chronic disease management. According to some scientists, paternalism may be justified within the school of utilitarianism when each action is evaluated from the standpoint of consequentialism, i.e., from the desired positive outcome. In other words, any action that leads to the best output is justifiable – “the ends are used to justify the means.” Some experts promote the notion of “weak paternalism.” The logic of such ideology stems from the position that interventions to prevent people from harming themselves are justified when there is a misperception in individuals’ assessment of the situation that may lead them to self-harming

Changes in the healthcare organization to prepare for the digital era 11

activity. Surely, some experts believe in paternalistic actions still being rational, not related to prevention of harm but rather being introduced based on voluntary and informed consent for treatment from patients in an effort to improve health with a new innovative treatment. According to some experts, the externalities make paternalism justifiable. The most prominent example is vaccinations. Given the assumption that some choices are suboptimal  for individuals as they may not realize the consequences, for example – disease onset due to non-vaccination. In its strict form, it can even use legal obligations like some countries introducing mandates for COVID vaccines. The rationale behind this is to not only protect the individual but also others in case of contagiousness. In its soft form, it can be limited to some form of taxes, for instance, the “sin taxes” aiming to reduce the consumption of unhealthy products such as alcohol, cigarettes or sugar. For example, in the first ten months of taxation on drinks with added sweeteners, there was a 21% reduction in their consumption as noted in Berkeley in 2016.3 More recently, there is growing recognition of the behavioural economics that points towards greater reliance on public interventions making them justifiable even without the acknowledgement of market failures. This new approach stems from a critical view on the preference-satisfaction theory of welfare. The behavioural form of paternalism is also called libertarian paternalism. Let us remind ourselves that standard neoclassical welfare economics is based on the concept of “revealed preference,” i.e., the notion that preferences are revealed by making choices and that those preferences are our guide to evaluating welfare or well-being. Behavioural scientists question the relationship between individual choice and welfare. Instead, they believe that some actions are not in the individuals’s best interests. These deviations from normal behaviour or simply “behavioural anomalies” are not random, but are biased in certain directions. The neoclassical assumption of rational behaviour is questioned. In that sense, behavioural science arguments for a third-party intervention, which is meant to “increase their level of subjective welfare, something they cannot do themselves due to cognitive or behavioural limitations.”4 While welfare economics allows for interventions related to the improvement of welfare or redistributive considerations due to “market failures,” the behavioural science makes the impact on the individual’s freedom of choice acceptable under other circumstances. Specifically, behavioural sceince argues that interference is legitimate to prevent one’s actions that may be a source of later regret and lead to potential mistakes. In short, the main justification of behavioural paternalism is that incoherent preferences may violate the standards of rational choices. Libertarian paternalism introduces a key function – the choice architect. It is a superior power responsible for framing the set of choices. The underlying assumption is that individuals often do not make decisions in their best interest, instead they tend to use shortcuts (heuristics), which are prone to cognitive biases. With such a presumption, it is believed to be justifiable that people

12  Changes in the healthcare organization to prepare for the digital era

should be nudged towards better decisions by those who know more. The nudge is a persuasive tool that is used to slightly push someone into a certain direction. Libertarian paternalism introduces the choice architect who has a right to alter people’s behaviour in a predictable way without forbidding any options but making it easier to choose what is believed best for individual. It is based on evidence indicating people might choose poorly. The discourse of Prof. Richard Thaler, one of the key proponents of behavioural science, leads us to a role of governmental policy with “nudge” as a kind of incentive that alters individual behaviour in a predictable way, without forbidding any of available options but nudging individuals towards decisions that are considered in their best interest.5 Supported by the 2017 Nobel Prize for Economics, nudge theory and choice architecture have made a significant impact on behavioural economics research as well as policy-making. They attempt to identify the sources of potential biases in the decision-making process and use them as entry points to shape interventions to optimize the future behaviours.6 It can be based on simple tricks such as reminders, incentives or motivators. The nudge theory has many successful stories of tangible impacts on patient awareness, such as the increase in organ donations. According to different reviews, countries with presumed consent have 60% more of the population registered compared to others with explicit consent.7 Nudge theory can bring tangible cost savings for healthcare systems, too. One example is when the University of Pennsylvania Health System introduced a generic drug as the default option, which led to the increase of prescribing rates of less-expensive medications from 75% (ten-month pre-intervention period) to 98% (seven-month post-intervention period).8 Libertarian paternalism based on nudge tactics led to the establishment of the Behavioural Insights Team in the UK. It was one of the first formal governmentled Nudge Units. According to its website, there were more than 700 projects by the end of 2021, which speaks to the plethora of opportunities nudge theory has brought to policymaking.9 Among many other projects related to tax payment mechanisms and retirement programs, it established a set of simple nudges for NHS procurement to improve decision-making with the reduction of choice overload, risk aversion and status quo bias.10 In summary, information asymmetry led to the strong establishment of the notion of paternalism and the empowerment of clinicians. The acknowledgement of market failure has been the rationale for healthcare system organization and financing from the perspective of public or private payer. With the dawn of a new libertarian paternalism that redefines the role of the agent as a support function, the individuals are led through life with the system of nudges that helps them to make the right choices free from cognitive or behavioural limitations. Is that a right move forward towards individuals’ happiness in the digital era? What’s the next phase of clinicians’ and perhaps patients’ empowerment?

Changes in the healthcare organization to prepare for the digital era 13

2.2 Why a greater reliance on individual sovereignty brings a revolutionary change from treatment to prevention Surely everyone will admit that our lives were divided into pre- and post-pandemic times. Among other changes, are we can acknowledge that the COVID virus showed us that it is the time to replace the empowerment of healthcare professionals with the empowerment of patients or the axiom of paternalism with the axiom of individual sovereignty? Despite growing interests into libertarian paternalism, we still encounter failure with missed patient focus in the healthcare sector. Does it mean that the pandemic made us realize that it is not about market failure but government failure? Does it mean it’s time for consumer driven healthcare sector? Let us discuss these questions. It seems that the government became ineffective in meeting societal expectations which eventually led to vaccine hesitancy. The International Monetary Fund (IMF) organized individual-level surveys across 17 countries between November 2020 and April 202111 to focus on the drivers and implications of COVID-19 vaccine demand and what aspects should be prioritized when designing policies to tackle hesitancy. In total, 114,000 individual observations were analysed. It turned out that 40% of respondents are either unsure or unwilling to take the vaccine. The COVID-19 pandemic revealed the ugly truth that paternalism in healthcare can’t rely on the government alone to strive for allocative efficiency. What’s the purpose of innovation if it is not used? We need to make patients like the idea and follow the idea. Why do I suggest this? The IMF study revealed two major reasons for vaccine hesitancy. The first one related to potential side effects of the vaccine, which speaks to the lack of trustful sources of information. The second determinant was related to the trust of government and whether it will be able to provide an effective vaccine. In other words, the IMF study revealed that lack of trust and lack of information are the sources of vaccine hesitancy. How did this happen? Surely it is not only the COVID effect; it is mistrust towards the government born earlier but also exacerbated by the pandemic. The study indicated as well that there is a potential to engage peers to ask them to share information and consequently work to decrease the level of vaccine hesitancy. The IMF study is not the only one that clearly showed that no intervention works without the patients’ engagement. The study of COVID-19 vaccine acceptance across 15 survey samples covering 10 ten low- and middleincome countries (LMICs) in Asia, Africa and South America as well as Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals, led to similar conclusions. The fear of side effects was the major driver of vaccine hesitancy.12 The authors indicated again the need for patients to receive more information from trusted sources. In this survey, they pointed towards healthcare professionals. In the Netherlands, a similar pattern has been observed. Although almost half of the participants (49%) were positive

14  Changes in the healthcare organization to prepare for the digital era

about the Dutch government’s approach towards the fight against COVID-19 in the beginning of the pandemic, this figure dropped to 31% in the middle of 2021.13 Another study in Italy showed similar findings. In addition, it revealed that communication was a key success factor of trust.14 The importance of trust in the government was explicitly mentioned across 1476 adults in the UK surveyed in December 2020 as well. Those who said they would take the vaccine were more likely to have stated that they trusted the government’s handling of the pandemic.15 The importance of education was shown in Canada, where the hesitancy has fallen by more than 10% to 16.5% among women when the results of national survey were compared between February and June 2001 across 5000 citizens.16 It was believed that getting the right information about the benefits of treatment and more information about vaccine availability “near you,” such as https://vaccinehunters.ca/, to non-vaccinated was successful. Such lessons indicate clearly that there is a need to move out from the acceptance of the fact that patients can be only passive recipients of medical instructions. There is a need to equip patients with correct information and evidence to ensure he/she becomes an active participant in the healthcare system. Vaccine hesitancy during the COVID-19 pandemic showed that the government failed with its paternalist efforts. The lessons learnt from the examples may indicate that the trust erosion and lack of information were one of the valid sources of vaccine hesitancy. It is yet another important reminder that we live in an ecosystem that requires collaboration with understanding and alignment regarding the common objective. Since individual action may hurt others, the success of paternalism can’t happen without societal engagement. In other words, vaccine hesitancy is a very real example of a healthcare system that needs to be built on the machine of mutual compatible elements in both micro and macro scale. The fuel of that machine is trustworthy information. Typically, we trust ourselves and those who are close to us. That’s why we need to equip everyone with the right information at the right time and hope it is going to act towards the overall benefit. Any form of paternalism without information sharing should be forbidden. With the growing access to information, the role of government should therefore change. The right information delivered at the right time for the right purpose is therefore needed to engage patients. Otherwise, the healthcare system will not be able to achieve its objective as patients will eventually search for the information in other sources that might be polluted with bias or simply misinformation. The new form of paternalism should be therefore developed with the government as the centre of credible and objective information and decision-making built on the rules of patient empowerment. The structure of the healthcare system should be built on communication channels that allow fast transition of credible, objective and trustworthy information. The lack of trust in the healthcare system unfortunately has a monetary cost. In my opinion it should be called the cost of governmental failure or failure of paternalism. The US Centers for Disease Control and Prevention (CDC)

Changes in the healthcare organization to prepare for the digital era 15

published the results of the analysis of data on 600,000 COVID-19 cases from 13 states collected from April through mid-July 2021 (before the Omicron variant) and found that the unvaccinated were 4.5 times more likely than the fully vaccinated to get infected, over 10 times more likely to be hospitalized and 11 times more likely to die.17 Vaccine hesitancy and the slow pace of vaccination clearly indicate the size of government failure. The lack of trust was obviously not born in the pandemic. In 2019, a study of 16,000 respondents worldwide found that, on average, only 34% of respondents were fairly or very satisfied with the healthcare system.18 Recent developments prompt us to reflect upon information asymmetry but from a different angle: the understanding of the high risk of mistrust and dissatisfaction in the paternalist healthcare system due to limited information. Does it lead us to the question of whether information asymmetry is sufficient justification for government intervention? One must acknowledge that the government to be effective in its paternalistic efforts needs to collaborate with society, as the case of the pandemic clearly showed. The vast majority of welfare economics, however, has focused on problems of market failure due to information asymmetry missing the consequences of abandoning the principle of individual sovereignty. The growing mistrust towards the government led to the crisis, which was mainly exemplified with COVID vaccine hesitancy. The question remains whether the crisis observed during pandemic would lead to some radical change in healthcare system organization. Can the healthcare system fix that challenge with the current mode of operation? Do we observe the dynamic defined by Kuhn as an anomaly that is understood as something more than just another puzzle to be solved in the framework of normal science? Has the transition to crisis and to extraordinary science begun? Has COVID-19 brought the end of paternalism in healthcare? Do patients want to be engaged in the decisionmaking processes? Let’s look beyond the COVID pandemic to search for the answers. Information asymmetry assumed that doctors made decisions as “doctor knows best.” So, paternalism introduced a blind trust in the doctor-patient relationship. It moved the healthcare system into a mode that is dependent on human resources. It might have been acceptable in the early days of the healthcare system, but now it is simply unsustainable. Let us remind ourselves that the WHO estimates that roughly 18 million health workers will be missed to sustain the healthcare system by 2030.19 With such scarce human resources, the “doctor knows best” approach looks like a clear pathway towards bankruptcy. According to 2010 and 2016 Organisation for Economic Co-operation and Development (OECD) data, more than 50% of patients waited more than one month for specialist visits in Canada and Sweden.20 Waiting time means postponing diagnosis and initiation of treatment and pushing patients into a more sick stage, which is simply more costly. In 2018, the median waiting time for cataract surgery was over 250 days in Poland, while waiting times for hip replacement was 282 in Estonia, and knee replacement was

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839 days in Chile. We need to also consider the fact that 16% of doctors’ time is spent on administration, according to the interviews conducted in 2014 across 4720 US physicians who worked 20 or more hours per week in direct patient care.21 With such waiting times and limited healthcare professionals, one can only draw one conclusion. Doctors have truly limited opportunities to look holistically at the patient. How much financial effort have authorities really used to steer patient’s behaviour and collect patient-level data to prevent health problems? Well, would it be better if the healthcare system focused on how to avoid costly but preventable incidences? Why does a greater reliance on individual sovereignty bring revolutionary change? With the search for an answer, we turn towards the importance of education. There were already some pre-pandemic examples that justify the importance of patient engagement in the journey towards better efficiency. According to the CDC obesity costs the US healthcare system an average of US$1429 compared to non-obese patients with heart disease, stroke, type 2 diabetes and some cancers being the major drivers.22 A cost effectiveness analysis of a Patient Empowerment Programme (PEP) for type 2 diabetes mellitus (DM) in primary care showed a very high (66% likelihood) that the PEP is cost-effective compared with the non-PEP when willingness-to-pay (WTP) for a QALY is US$46,153 with an incremental cost per subject of US$197 and the incremental QALYs gained were 0.06 per subject. Perhaps a more engaged patient or more empowered patient is also a cheaper patient for the healthcare system. Another study tested the impact of information shared with patients via DVD and booklet formats via US mail in support of their decision-making process prior to surgery in the US setting.23 In total, 332 (41%) eligible patients with hip osteoarthritis and 978 (28%) eligible patients with knee osteoarthritis received decision aids. These groups of patients reported 26% fewer hip replacement surgeries, 38% fewer knee replacements and 12–21% lower costs over six months. Similarly, in 2010 and 2012, a total of 10,957 patients of Fairview Health Services in Minnesota filled out the Patient Activation Measure to gauge patient’s “engagement,” activation or self-management capabilities. The questionnaire defined four levels with level 1 suggesting that a person may not yet understand that the patient’s role is important while level 4 indicates that a person is proactive about health and engages in many recommended health behaviours.24 In multivariate regression analysis, higher baseline activation levels were predictive of better health outcomes for the majority of health indicators. The lowest average per capita cost (US$6411) was predicted for patients who were at level 4 while those who remained in levels 1 or 2 had costs that were 31% higher. Another example is about Integration of patient reported outcomes (PROs) into the routine care of patients with metastatic cancer. It was conducted at Memorial Sloan Kettering Cancer Center in New York between September

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2007 and January 2011. Out of 766 patients were randomly assigned either to the usual care group or to the PRO group, in which patients provided a selfreport of 12 common symptoms from the National Cancer Institute’s Common Terminology Criteria for Adverse Events at and between visits via a web-based PRO questionnaire platform. When the PRO group participants reported a severe or worsening symptom, an email alert was sent to a clinical nurse responsible for the care of that patient. A report profiling each participant’s symptom burden history was generated at clinic visits for the treating oncologist. The usual care group received the standard procedure for monitoring symptoms in oncology practice: symptoms were discussed during clinical encounters and patients could contact the office by telephone between visits for concerning symptoms.25 It was found that digital symptom monitoring during chemotherapy helped patients live longer (5.2 months longer median overall survival), improved the quality of life (31% of patients), and reduced hospitalization (4%) and ER visits (7%). While the pandemic showed us that the paternalistic mode of healthcare operation could be costly, pre-pandemic examples indicated how patient’s engagement in the decision-making process may produce a positive impact. It can lead to savings by ensuring greater accountability and responsibility of individuals in their search for better health outcomes. Will that bring us to the conclusion that a paradigm shift with a greater reliance on individual sovereignty is coming?

2.3 Why individual sovereignty can be accelerated in the healthcare system of the digital era The analysis of trends in healthcare dynamics needs take into account the impact related to the digital transformation. According to recent studies, patient empowerment is actually the main benefit of the digital revolution.26 This opinion was echoed from 31% of the respondents among 166 responses received from the readers of Pharmaceutical Technology, a Verdict network site, between February 02, 2020 and April 19, 2021. In 2017, there were nearly 325,000 health, medical and fitness-related mobile apps27 and the market of wearable devices was projected to amount to $81.5 billion in 2021 in terms of spending.28 In 2018, there were 122.6 million wearable devices distributed worldwide, and that number is about to increase to 190.4 million units by 2022.29 The global digital health market size was accounted at US$181.8 billion in 2020 and is expected to increase to US$551.1 billion by 2027.30 The digital era not only will accelerate the further development of patient empowerment improvement but will also change its meaning. In the fall of 2020, Deloitte Center for Health Solutions asked 30 finance executives (CFOs, finance VPs and revenue cycle VPs) of large health systems (revenue greater than US$500 million) about their projections for the future of the digital era. “Developing or refining a consumer engagement strategy and

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executing on it” – was mentioned as a key component of future strategy. It is believed that healthcare organizations should “engage consumers through data-sharing, interoperability, and price transparency and whether they will buy, build, or partner for certain needed capabilities.”31 The consultations with 3000 patient groups organized by PatientView between 2020 and 2021 established the following nine indicators for pharma ceutical companies to adopt in the post-pandemic era in support of patient empowerment: patient information, patient safety, patient-centred products, patient integrity, patient-related groups, patient engagement in R&D, access to medicine, support and services of “Being Patient Centric.”32 It is not surprising that the number of Google hits for “digital health patient empowerment” is about 104 million results (September 5, 2021). Patient empowerment is greater than the adherence we hoped for. The digital era introduces patient sovereignty. Why? First, digitalization helps patients to educate themselves thanks to new digital tools, allowing individuals be more informed and more aware. Second, it helps patients to become the owner and decision-maker of their own health journey. Let me illustrate both points with relevant examples. First, most importantly, equipping patients with tools that allow them to control their health means that the role of healthcare professionals truly will change from the decision maker to consultant. Thanks to access to information, patients become more informed and consequently more empowered but also more demanding. According to one study conducted across 709 patients in India, the discussion of information found on the Internet during doctor–patient interactions was beneficial for the patient’s perception of the interaction with the doctor. At the same time, however, discouragement by the clinician with respect to online health information gathered by patients has been found to be a barrier for healthy communication.33 That indicates new dynamic between healthcare professionals and individuals, which will hopefully eventually lead to greater partnership. Clinical personnel will be needed even more to support patient self-education by leading them to the right information, but most importantly, equipping patients with right digital tools. Here is one example from a study: 471 placed on a low-calorie diet, physical activity plan and group counselling sessions for 24 months.34 At six months, telephone counselling sessions, text message prompts, and access to study materials on a website with the intervention group were added. The enhanced group was additionally equipped with wearables along with a web-based interface to monitor physical activity and diet (“energy expenditure and physical activity of the participant is provided by multisensory device worn on the upper arm through a small display or through web-based software developed by the manufacturer”). After 24 months, the weight loss difference between both groups was 2.4 kg in favour of the patients equipped with a wearable device. Second, patient empowerment means patients have access to information but also data that they collect themselves. It leads to a major change for healthcare

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professionals to become educators as well as to the system from being reactive by collecting data post factum to a proactive mode with data collection to react before an event. In other words, from treatment to prevention. A systematic literature review published in 2019 with respect to Wearable Sensor Health Technology (WSHT) identified 97 papers.35 It revealed a number of ways WSHT can support patients with the “real-time” management of diseases with disease monitoring (61 papers), by continuous monitoring of a patient’s vital parameters or early diagnosis (45 papers) and helping patients to connect symptoms to a given health problem through an emergency alert mechanism (20 papers) that sends an alarm when parameters exceed or fall below a predefined threshold. There were also studies related to rehabilitation (14 papers) and training (13 papers) in support of fast recovery. I believe that those two simple examples show that digital transformation creates a new version of empowered patient which is, in fact, patient sovereignty. Consequently, it changes the role of other stakeholders in the system as well as the system itself. The patient of the digital era does not ask healthcare professionals to make decisions. It is the patient who decides with support of multiple sources of information, with clinicians being just one. The patient of the digital era feels more informed but also more satisfied with faster access to information. Recently, I was asked by my mother to drive her to the emergency department, as she felt acute back pain after a fall at home a week earlier. My attempts to discourage her from going to the emergency department was unsuccessful. She claimed it is faster to get all the needed diagnoses that way than waiting a couple of days for X-ray exams and an orthopaedic visit. As a result, she spent six hours waiting at the ER. Yes she got both X-ray and CT scans as well as consultation with both an orthopaedist and neurosurgeon. Is it an effective way to spend our healthcare budget? According to some estimates, two-thirds of ER visits in the US are “avoidable” and “not an actual emergency.” 36 The average cost of such visits for common conditions that could have been remedied through primary care tops US$2000, which is 12 times higher than visiting a physician’s office. It is a very sobering calculation of governmental failure. In the digital era, there is growing evidence that digital tools can prevent disease consequences thanks to patient-provided data. For instance, the impact of the use of smartphone-based electrocardiography (ECG) monitoring devices (EC) across 45 patients who underwent ablation of atrial fibrillation (AF) was compared to 45 patients treated with ambulatory ECG monitoring (external cardiac event monitor, mobile cardiac monitor, or Holter monitor) (SC). In total, only 13% of EC patients were seen in the clinic or emergency room vs. 33% in the SC group over a 100-day study period (P value < 0.001). 37 The potential of digital health in driving patient empowerment and consequently lowering the cost of healthcare becomes even more tangible if one recalls the lack of access to medical support to pre-digital healthcare.

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If we list all potential healthcare problems that can be avoided, the costly failure of the healthcare system will have a greater price tag than the one mentioned in statistics about ER visits. Therefore, if we define the objective of the healthcare system as not limiting treatment, but that of prevention and treatment, would the price tag change? There are some examples of preventive measurements already in healthcare systems, such as the ones listed earlier in the fight against obesity. Is it, however, enough? It is fascinating that so many new health technologies in support of prevention and patient engagement are entering the market every year. In 2020, the top two categories of EU patents granted were medical technology and digital technology (14,000 roughly each), while pharmaceuticals only had 8500 patents, as mentioned in the introduction to this book.38 Healthcare authorities tend to regard any kind of innovation as a headache as they observe it through the lens of additional cost. The paternalistic approach empowered the governments to decide what is best for patients in the pursuit of the ultimate objective of optimal allocation of scarce healthcare resources. Therefore, the methodology rooted in that approach tends to limit the focus on the question of how much the incremental cost of the incremental health outcome is and whether it is worth granting access to new technology. What if we approach it with a different mindset – instead of paternalistic could it be patient empowerment or, more precisely, individual sovereignty? Could patient empowerment potentially save the healthcare system from bankruptcy? If we adopt the patient perspective, the primary objective of the healthcare system would be the avoidance of health failures. Why? Who would like to suffer from any disease? At the end of the day, it is the patient who is more interested in limiting the risk of disease consequences than a paternalistic body who governs the healthcare system. The objective would change from “treat” to “prevent and treat.” What if we abandon the idea that the paternalist healthcare system knows what is best for us and simply educate patients to take control of their health and support patients in preventing health problems instead. It would mean that the incremental cost per additional outcome should be replaced with the question of how likely a given technology would help the patient to avoid a given health problem. For the healthcare system, it would translate to the question of how much a given health technology would allow us to save after it has been introduced into the clinical practice. Let me put my idea into real-life context with an example of lung cancer, which is the second leading cause of death.39 Overall, according to the WHO estimates, there were 20 million new cancer cases and almost 10 million deaths from cancer in 2020.40 With an estimated 2.2 million new cancer cases and 1.8 million deaths, lung cancer is the second most diagnosed cancer, representing approximately one in ten (11.4%) cancers diagnosed and one in five (18.0%) deaths. The survival of patients with lung cancer at five years after diagnosis was only 10% to 20% in most countries among those diagnosed in 2010 through 2014. The very recent review of cost effectiveness of innovative immunotherapies revealed that we must pay at least USD$200,000 per incremental QALY

Changes in the healthcare organization to prepare for the digital era 21

gain (every life year saved adjusted for quality of life).41 With about two-thirds of lung cancer deaths worldwide attributable to smoking, the disease can be largely prevented through effective tobacco-control policies and regulations. The economic miracle would have happened if we utilized digital health solutions to empower patients and help them to minimize the risk of disease in the first place. How? Let us just review two very tangible examples. The first one shows how patient empowerment can happen with the support of mobile applications. It was a randomized study with a total of 556 participants (treatment: n = 277; control: n = 279).  The treatment group received cognitive behavioural therapy for smoking cessation via a smartphone application including one-to-one coaching, craving tools and tracking capabilities. The control group received very brief advice along the ask, advise, act model. During four weeks after the post-quit date, the percentage of participants in the treatment group who had not smoked in the preceding seven days was higher than in the control group (44.5% compared with 28.7%, risk ratio 1.55, 95% CI 1.23–1.96; P < 0.001; intentionto-treat, n = 530). Another example was a randomized controlled trial with 160 smokers Tweet2Quit – peer-to-peer support and accountability for maintaining commitment to quit smoking. The participants received 56 days of nicotine patches as well as emails with the links to the smokefree.gov online portal along with instructions on how to define a quit date within seven days.42 The intervention group was additionally enrolled in 20-person, 100-day Twitter groups and received daily discussion topics via Twitter and daily engagement feedback via text (Tweet2Quit). Tobacco abstinence was reported at 60 days follow-up. Tweet2Quit participants reported significantly greater sustained tobacco abstinence compared with the control: 40% vs. 20%; (P = 0.012). The engagement was high, with participants averaging 57 tweets over an average of 47 days. The more a participant tweeted, the more likely they were to successfully quit smoking (P = 0.003).

2.4  QALY journey in the healthcare sector So, what really is the objective of a healthcare system? Let us again remind ourselves that the specific characteristics of health made health economists believe that unless regulated by a third-party agent, we will observe the failure of healthcare sector. As a consequence, a very strict set of market entry regulations for specific products and services were introduced under the full control of institutions established by the third-party agent. In practice, there are two major entry steps in place for the majority of pharmaceuticals and some medical devices. First, such health technologies must be evaluated with respect to safety and efficacy from the clinical practice standpoint. Second, the assessment of cost effectiveness with QALY as the outcome and potentially budget impact, has to follow from the perspective of the decision-maker’s standpoint. It is important to note that the overarching objective of the latter is to ensure the optimal allocation of limited healthcare resources. In essence, it means that

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the value proposition of a given health technology must addresses clearly the unmet medical needs of patients articulated either in the form of improvement in quality of life and/or life expectancy. It is the way the extra-welfarism ideology with the underlying principle of health maximization was adopted. It is too difficult to challenge the importance of health as a key objective for a healthcare system. It is fundamental to human well-being, so access to healthcare is an essential good. With QALY being the golden standard, however, the understanding of health has been limited to only two dimensions: life expectancy and life quality defined from the perspective of healthcare system. Why? Following the extra-welfarism perspective, the third-party agent has been assigned the decision-maker’s responsibility for budget allocation. Hence, health outcomes must be quantifiable within the zone of healthcare system not outside. Another important point is that the market entry pathway has been focused solely on pharmaceuticals and some medical devices, as they fall under the umbrella of responsibility of the third-party agent, leaving other technologies outside of consideration. It is very much linked with the pre-assumption of information asymmetry that led clinicians to decide what is good for our health. Naturally, we adopted the QALY concept to assess treatment effects generated by these types of technologies administrated to us within the healthcare system. As a result, both the regulatory and pricing and reimbursement processes are set towards the quest for incremental treatment benefits omitting, for example, the benefits of preventive methods adopted outside healthcare or efficiency gains for the healthcare system adopted outside clinician responsibility. In all, we ended up with a very limited understanding of health. However, health is just a component of our lives that we cannot separately from other spheres of life. According to the WHO, the health system “includes all actions whose primary purpose is to promote, restore, or maintain health.”43 WHO defines health pretty broadly as a state of “complete physical, mental and social well-being and not merely the absence of disease or infirmity.” Thanks to its generic nature, QALYs allow the ranking of incremental health gains of various health technologies irrespective of therapeutic area. It is the biggest undeniable advantage of QALY to become a tool for allocative decisions of limited healthcare resources. Still, when we limit the source of quality of life or life expectancy to just a given health technology because it is distributed by clinical experts, I believe we miss a lot, that’s what I will focus on later in the chapter. In summary, the fear of market failure led to the establishment of third-party budget holders who defined the market entry criteria with the obligation to pursue an optimal allocation of limited healthcare resources towards health maximization. QALY became the generic measure to estimate the value of health technology defined from the perspective of the definition of the healthcare system framed by clinicians and third-party agents.

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2.5 From a decision-maker perspective to a holistic view on health The sole focus on health gains from a given treatment misses the importance of other determinants of health as well as other consequences of disease. For example, what about aspects unrelated to a given technology influencing health during the treatment, or underestimated factors leading to health deterioration that remain untreated? Who does not feel happy while being on vacation? Can’t a nice dinner with friends brighten our mood? Does not falling in love give us a different perception of health problems? Can’t all these life boosters surely give us energy to prevent and even cure our diseases? I am giving such extreme examples on purpose to indicate that it sounds intuitively correct to believe that there are other sources of quality of life improvement. It is the holistic view on health that I am advocating here for. So why we don’t capture all dimensions of health from a 360-degree standpoint? Health economists call it societal perspective, which simply implies that the disease consequences are measured both inside and outside of the healthcare system. Depression is an example. It is the disease that clearly impacts both quality of life and life expectancy. We knew pre-pandemic that depression is difficult to measure with current quality of life (QoL) gold standard instruments44 on top of other negative aspects of the disease45 that negatively affect an individual’s productivity, which the economy of a given country. Some estimates suggest that it might affect up to 30% of annual income!46 The consequences of mental disorders can cost the healthcare system a lot as well. In a review of electronic medical records of a children’s hospital emergency department (patients aged 5–24 years) between January 1, 2018, and January 1, 2021, with a mental health diagnosis, the proportion of ED visits for MH conditions, showed a significant increase (from 4.0% [338.6 of 8559.9] to 5.7% [260.8 of 4582.3]).47 Would we be able to tailor more effective treatment options if we accounted for all health consequences of depression, including professional life and elevated risk of other diseases, across the assessment of value of any health technologies? The example of depression is extremely important in the discussion about the future of healthcare due to the COVID-19 pandemic. It reminded us about the importance of the holistic view on that health problems and left us with another pandemic – a mental health pandemic. The comparison of data of the National Health Interview Survey, 2019, with  the Household Pulse Survey from 2020 in the US revealed almost four times growth in those reporting anxiety and/or depressive disorders, from 11% to 41%. According to another study, which was an online survey conducted across 551 respondents in Europe September–December 2020, 51% of respondents reported their mental health problems had gotten worse since the COVID-19 outbreak. Furthermore, 57% of respondents mentioned they experienced a decline in their mood due to the pandemic.48 Finally, the study that aimed to establish the

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global prevalence of mental health issues among the general population amid the COVID-19 pandemic pooled data from 32 different countries and 398,771 participants. The pooled prevalence of mental health issues amid the COVID-19 pandemic varied widely across countries and regions and was higher than in previous reports before the COVID-19 outbreak began. The global prevalence estimate was 28.0% for depression; 26.9% for anxiety; 24.1% for post-traumatic stress symptoms; 36.5% for stress; 50.0% for psychological distress; and 27.6% for sleep problems.49 To illustrate the significance of these data, prior to the pandemic, global estimates were about 5.0% among adults and 5.7% among adults older than 60 years, according to WHO data.50 It is in line with the review of studies across 27 European countries who were included in the second wave of the European Health Interview Survey, collected between 2013 and 2015. The overall prevalence of current depressive disorder was 6.38% (95% CI 6.24–6.52).51 The data from 2020–2021 indicated that roughly one in every five individuals across the OECD region suffers from depressive disorders, which is four times higher than pre-pandemic.52 To reinforce the importance of such findings is the Lancet publication with a systematic review of global data reporting the prevalence of major depressive disorder and anxiety disorders during COVID-19. It was estimated additional 53.2 million (44.8–62.9) cases of major depressive disorder globally (an increase of 27.6% [25.1–30.3]) due to the COVID-19 pandemic and additional 76.2 million (64.3–90.6) cases of anxiety disorders globally (an increase of 25.6% [23.2–28.0]) globally in 2020.53 Not only does the growing mental health problem remind us of the importance of a holistic view from the perspective of health consequences outside of healthcare system, but it also brings a new perspective with respect to the sources of health problems too. According to different estimates, the healthcare system influences between 15% and 43% of a patient’s health status, the remaining being mainly genetic composition and socio-economic factors. According to other estimates, the latter reaches up to 50% of total impact on health.54 So, what is the possible source of the increase in depression during the pandemic? According to available surveys, loneliness, social isolation and uncertainty as well as Zoom fatigue were mentioned as key determinants of mental deterioration.55 The mental health problem shows us, therefore, that adopting the holistic approach will actually give us the opportunity to understand the sources of health problems and find preventive measures more effectively. Everything is truly interconnected. It was in the 1990s when healthcare providers in Sweden reimbursed patients suffering from depression for vacations to sunny destinations. Does the adoption of a holistic approach lead us back to such solutions? Why not organize comparative analysis and send one group to Gran Canaria, Spain and provide the other with antidepressant treatment? Let’s make sure both groups are homogenous and apply a propensity score and assess the quality of life improvements to compare the response rate to treatment? Does anyone want to bet which is the cost effective option?

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Reviewing all these terrifying statistics mentioned above, shall we hope that the pandemic will lead us to the realization that health is something greater than problems solved within the healthcare system? Are we ready for an axiom of holistic health? Health is about how we feel and interact with others. If we do not feel socially connected, it impacts all other aspects of our lives, including somatic health. The growing problem of mental disorders indicates it very clearly. Let’s hope it leads us to the understanding that an axiom of the decisionmaker’s perspective that was a grounding rule of the healthcare system organization is in the “crisis mode,” i.e., a stage defined by Kuhn as preledium to a paradigm shift.

2.6 Why a holistic perspective on health will become the revolutionary change from treatment to prevention Do we need to replace the axiom of the decision-maker with the axiom of holistic approach just to be able to treat depression more effectively? We need to acknowledge that there are many other health problems that have behavioural origins. The Danish Twin Study1 established that only about 20% of how long the average person lives is dictated by our genes, whereas the other 80% is dictated by our lifestyle. In ancient times, the average length of life was about 35 years, while it was 72.3 in 2020 on average globally.56 This is not a surprising finding if one takes into consideration the pace of innovation happening in recent centuries. However, what is surprising is that according to some studies in ancient Greece, some individuals managed to experience life expectancy similar to our times. According to one study, there were a total of 83 persons identified, whose mean (±SD) and median length of life were 71.3 ± 13.4 and 70 years, respectively. Among contributing factors were a good level of sanitary and hygienic conditions of housing, adequate nutrition and many slaves engaged for the hard work. Additionally, it was believed that an intense intellectual and animated social life in which the aged actively participated was a major contributor to the successful live expectancy. Another more recent example is the study of 145,000 Washingtonians who died at age 75 or older between 2011 and 2015.57 The analysis revealed that neighbourhood walkability, higher socio-economic status and a high percentage of the working age population (a measure of age diversity) were positively correlated with reaching centenarian status. My ultimate aim is to bring understanding to the importance of health determinants. In that respect, I am not the only one. Today, more and more experts highlight the importance of socio-economic factors of health. It is important to discuss a project established by the National Geographic expedition, led by Dan Buettner, to uncover the secrets of longevity, which led into the discovery of the five places around the world where people consistently live over 100 years, the Blue Zones. For example, in Sardinia, one of these five zones, there are nearly ten times more centenarians per capita than in the United

26  Changes in the healthcare organization to prepare for the digital era

States. They labelled it the first Blue Zone. Based on his research, Dan Buettner’s study listed nine factors related to diet, exercise and social sphere. None is usually directly linked to the core set of objectives of the healthcare system. That does not mean that the role of health technology delivered by healthcare system is nonsense, but it does mean that healthcare for its own interests should ensure patients stay healthy. These longevity examples tell us more about well-being than health. There is a growing body of literature that suggests how social life improves cognitive function in the elderly. So why does the healthcare system not evaluate the value of reimbursement for the cost of any kind of socializing activities for the elderly? In pre-pandemic times, there was mounting evidence that indicated that health policy programs are more successful when conducted with a holistic perspective as well. There have been some examples of a holistic approach with active multiple stakeholders set up focused on patient education and change of behaviour. Take the example of obesity. It is one of the most important problems, and according to the WHO, the worldwide prevalence of obesity nearly tripled between 1975 and 2016.58 According to the WHO, being overweight or obese is the fifth leading risk factor for causes of death.59 Two out of five Americans are obese.60 The systematic literature review from January 2021 of 96 studies focused on obesity reduction revealed that the stakeholders’ multicomponent interventions have the greatest likelihood of delivering positive outcomes.61 Specifically, the education efforts directed at patients and engagement of peers can contribute significantly to the success of health policy. Another study of 3988 participants “recruited through PatientsLikeMe, an online research platform where patients share their personal and medical history data” indicated that satisfaction with healthcare access was most strongly correlated with empowerment scores and positive patient-provider interaction. Interestingly, some room for improvement was identified along with the many cited difficulties with matching their treatment goals with those of their physician (34%) and spending sufficient time with the physician (36%).62 In both studies, not only were provider relationships important in empowerment but also in enabling other patients and learning from others. It must be admitted that public health is born on the principle of paternalism, which puts us in the blind spots of understanding health with the holistic mindset. In healthcare decision-making driven by the QALYs, there were limited efforts spent on understanding the value of avoidance of health problems. The search for incremental health gains against incremental costs is, by default, not suitable to define cost savings of prevention, meaning the success of avoidance. There are several examples that prove that. For instance, the intervention dedicated to the prevention of hip fracture using a cost effectiveness framework indicated the case of dominance (meaning a cheaper and more effective option).63,64 Applying such analytical framework does miss the incremental

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benefit of avoidance. In other words, such examples ask the question of whether we could simply replace QALY with a simple calculation of the cost of preventable events if we both know the cost of avoided event and epidemiological data. To do that, one could simply apply a cost-benefit analysis as a standard approach; it is a monetary value assigned to each gain and a very tangible analysis. That’s also the reason why the cost benefit analysis has been used so far for public health policies in some jurisdictions. However, that brings the problem of how to quantify forgone consequences, i.e., to weigh the value of avoided events. The efforts taken with the adoption of a cost-benefit analysis are limited, however, to the questions about how to quantify health gains. The question is, therefore, not only how to measure benefits but, even more importantly, how to value them. Looking at the prevention from a holistic point of view does highlight the importance of subjective preferences. The different approaches adopted to the calculation of lockdown measures proved that point. It turned out that a slight change of assumption of the measurement of the value of statistical life (VSL) changed the perspective from useful to useless on the point of lockdown.65 VSL is often misinterpreted as the value the government places on saving an individual’s life rather than the value that individuals themselves place on small changes in their own risks. Dependent on the adjustment by age, we could make different decisions. In other words, the value of life by age determines the results. Although public health is meant for patients, it is organized with a limited engagement of patients. Any public health intervention, from vaccination programmes through additional taxes to modify bad habits and finally towards screening programs for early disease detection, is performed from the perspective of a third-party agent (budget holder). They do not search for the answer to whether another public intervention brings any meaning to the society or more directly to the end users. The lack of patient satisfaction is missed in the value assessment of public interventions. If we are not satisfied with the service at the restaurant, we simply choose another one next time. If we bought a new dress and it falls apart after our first wearing, we do not trust the producer or shop and choose another one next time. The lack of satisfaction or mistrust always have different tangible consequences in our future choices. The ultimate objective of the healthcare system should be to keep us healthy. What it really means is to either prevent the incidence of diseases or, once they occur, prevent the diseases’ consequences. Does QALY capture what we could prevent? If we forget about QALY for a moment, it would be pretty easy to understand the size of governmental failure in the healthcare sector. According to OECD data from 2019, over one-quarter of all deaths (almost 3 million) could have been avoided through better prevention and healthcare interventions. Across avoidable deaths, about 1.9 million were considered preventable through effective primary prevention and other public health measures and over 1 million were considered treatable through more effective and timely healthcare interventions.66

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In summary, there might be some compelling evidence provided that defining health from a broader perspective than just QALY can be profitable for a healthcare system as we observe other contributors that help systems to cure more effectively. Patient satisfaction might be a different dimension than the patient quality of life derived from purely medical treatment. Life is multi-dimensional, but still at the core is our somatic health. Hence, we need to acknowledge a 360-degree perspective if we want to treat the patient effectively. That’s the purpose of the axiom of a holistic approach.

2.7 Why adoption of a holistic axiom can be accelerated in the digital transformation In the pandemic, I was interviewed by one of the radio stations in Poland to help with the review of the well-being of elderly people through a mobile application designed especially for them. There are more and more digital solutions developed in support of such interventions.67 Even a simple telemedicine can work wonders. A randomized controlled trial was conducted in 16 communitybased urban and rural oncology practices involved in the Indiana Cancer Pain and Depression (INCPAD) trial.68 Recruitment occurred from March 2006 through August 2008 and follow-up concluded in August 2009. The participating patients had depression (Patient Health Questionnaire PHQ-9 ≥ 10), cancerrelated pain (Brief Pain Inventory [BPI] worst pain score ≥ 6), or both. That’s in line with the overall prevalence of current depressive disorder of 6.38% (95% CI 6.24–6.52). The intervention group received centralized telecare managed by a nurse-physician specialist team, which coupled with automated home-based symptom monitoring by interactive voice recording or the Internet. The study noted significant between-group differences at 1, 3, 6, and 12 months. The 202 patients randomly assigned to receive the intervention and 203 to receive usual care were sorted by symptom type. In the era of the digital revolution, however, there are new ways that allow us to adjust the system to the patient perspective more easily. There are some attempts, including one in which digitized patient reported outcomes of total knee replacement (TKR)/total hip replacement (THR) that tracked patients from the time of hospital admission until 12 months post-discharge.69 It subsequently brings some arguments to the discussion of how to value assessment performed by clinical experts vs. patient assessment, which concerns some experts regarding the risk of the bias brought by patient-reported outcomes. As such, some experts indicate that they “should be used in conjunction with objective outcomes.” 70 I am a bit surprised to see the suggestion of bias in conjunction with PRO. What’s wrong with being subjective? Shall we avoid anyone’s value for being an individual? Or perhaps different responses to the same set of questions in the PRO questionnaire mean that internal and external validity has not been achieved? The focus is on clinically meaningful change defined from a purely clinical standpoint.

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In the digital era, the adoption of the holistic approach will help us identify new solutions to old problems, too. Bringing a holistic perspective on health looking into sociodemographic determinants of health will naturally provide us a perfect scene to set up for prevention again! I was truly excited when I found recently that games can help to fight obesity in children.71 For example, the Nintendo Switch fitness game can track calories burned  (in 30 minutes). Another game, Ring Fit Adventure, claims to burn 245 kcals.72 Perhaps a change in mindset to a holistic outcome will help us find cheap solutions for our health problems. “Research has shown that early treatment of developmental issues can lead to higher IQs and better social skills. The Autism & Beyond app uses the frontfacing HD camera in iPhone, along with innovative facial recognition algorithms, to analyse emotional reactions to videos in children as young as 18 months. And children can be screened without having to see a specialist in person, allowing for earlier diagnosis and treatment. The app successfully enrolled more people in the first month than a previous nine-month onsite study did.” 73 Bringing a holistic approach is no less important in the effort to avoid costly failures. A study, published in Science, concluded that the algorithm that supports hospitals in their decision-making process was less likely to refer black people than white people who were equally sick to programmes that aim to improve care for patients with complex medical needs. Hospitals and insurers use the algorithm and others like it to help manage care for about 200 million people in the United States each year. So it is significant.74 This is one of many examples that indicate that we need to treat the data holistically to be able to observe what reality looks like. Only knowing all interconnections can we limit the risk of bias in the process of decision-making. In summary of deliberations outlined in this chapter, we can arrive at the conclusion that the axiom of paternalism was indeed the potential source of crisis we encountered during the recent COVID-19 pandemic. The symptoms were observed in the form of mistrust of authorities that led to vaccine hesitancy among major parts of societies worldwide. It reinforced the need for patient empowerment that, in the era of digitalization, will be not only more feasible but also more profitable. The future is bright as soon as we start to treat our health holistically. Our health is the final outcome of many factors; not only is it about treatment of somatic problems, our health can be affected from multiple sides. Hence, satisfaction of life and happiness are greater definitions than just quality of life. Perhaps we can have a good quality of life but still be unhappy and, consequently, still suffer from missing something or someone, which eventually may lead us to mental problems and to increased risks of cardiovascular problems or obesity.75 Is it insignificant? I do not think so. So, we arrive finally to my foremost important conclusion that the axiom of paternalism was the source of crisis. The symptoms of the crisis were observed in the COVID pandemic and mistrust of the authorities led to vaccine hesitancy among major parts of societies worldwide. It reinforced the need for patient

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empowerment that, in the era of digitalization, will be not only more feasible but also more profitable. I strongly believe that the digital era is an accelerator of change. It ends information asymmetry and brings healthcare system back to the axiom of individual sovereignty.

NOTES 1 Desmarais-Tremblay. “W. H. Hutt and the Conceptualization of Consumers’ Sovereignty,” Political Theory: History of Political Thought eJournal, 17 Feb. 2020, https://www. semanticscholar.org/paper/W.-H.-Hutt-and-the-Conceptualization-of-Consumers %E2%80%99-Desmarais-Tremblay/16ff3b5f2f482e19160328e4e41bcaf6ca4b2c38 2 Dworkin. “Paternalism,” The Monist, vol. 56, no. 1, 1 Jan. 1972, pp. 64–84. 3 Raguso. “Council Approves $1.5M to Fight Soda Consumption,” Berkeleyside, 20 Jan. 2016. 4 Kapeliushnikov. “Behavioral Economics and the ‘New’ Paternalism,” Russian Journal of Economics, vol. 1, no. 1, Mar. 2015, pp. 81–107, https://www.sciencedirect.com/ science/article/pii/S2405473915000057 5 Thaler, Sunstein. Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press Ltd, Apr. 2008. 6 Soofi, et al. “Using Insights from Behavioral Economics to Mitigate the Spread of COVID-19,” Applied Health Economics and Health Policy, vol. 18, May 2020, pp. 345–350, https://link.springer.com/article/10.1007/s40258-020-00595-4 7 Robitaille. “A Little Nudge Goes a Long Way in Increasing Organ Donor Registrations,” 2 May 2019, https://theconversation.com/a-little-nudge-goes-a-long-way-inincreasing-organ-donor-registrations-115051 8 Patel, et al. “Generic Medication Prescription Rates after Health System Wide Redesign of Default Options within the Electronic Health Record,” JAMA Internal Medicine, June 2016, https://jamanetwork.com/journals/jamainternalmedicine/ fullarticle/2520677 9 The Behavioural Insights Team, https://www.bi.team/ 10 Londakova, et al. “Buying Better: Improving NHS Procurement with Behavioural Insights,” The Bahavioural Insights Team, June 2021, https://www.bi.team/wpcontent/uploads/2021/06/FINAL-BIT-Procurement-in-the-NHS-Report.pdf 11 Khan, et al. “Who Doesn’t Want to Be Vaccinated? Determinants of Vaccine Hesitancy during COVID-19,” International Monetary Fund, Working Paper No. 2021/130, 6 May 2021, https://www.imf.org/en/Publications/WP/Issues/2021/05/06/WhoDoesnt-Want-to-be-Vaccinated-Determinants-of-Vaccine-Hesitancy-DuringCOVID-19-50244 12 Solís Arce, et al. “COVID-19 Vaccine Acceptance and Hesitancy in Low- and Middle-Income Countries,” Nature Medicine, vol. 27, 16 July 2021, pp. 1385–1394. https://www.nature.com/articles/s41591-021-01454-y 13 “Applying Behavioural Science to COVID-19,” National Institute for Public Health and the Environment, https://www.rivm.nl/en/coronavirus-covid-19/research/ behaviour 14 Bucchi. “To Boost Vaccination Rates, Invest in Trust,” Nature Italy, 12 Jan. 2021, https://www.nature.com/articles/d43978-021-00003-y 15 Jennings, et al. “Lack of Trust, Conspiracy Beliefs, and Social Media Use Predict COVID-19 Vaccine Hesitancy,” MDPI Pharmacoepidemiology in Vaccine: Generating the Real-World Data to Promote Vaccine Safety and Uptake, 3 June 2021, https://www.mdpi. com/2076-393X/9/6/593/htm 16 Parkin, et al. “Vaccine Hesitancy Is Decreasing in Canada, But It’s too Soon to Celebrate,” The Conversation, 28 July 2021, https://theconversation.com/vaccinehesitancy-is-decreasing-in-canada-but-its-too-soon-to-celebrate-165133

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17 Scobie, et al. “Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status – 13 U.S. Jurisdictions, April 4–July 17, 2021,” Morbidity and Mortality Weekly Report, 2021, pp. 1284–1290. 18 “Level of Satisfaction with National Health Systems Worldwide as of 2019, by Country,” Statista Research Department, 20 June 2020, https://www.statista.com/ statistics/1109036/satisfaction-health-system-worldwide-by-country/ 19 “Health Workforce,” World Health Organization, https://www.who.int/health-topics/ health-workforce#tab=tab_1 20 “Waiting Times for Health Services: Next in Line,” OECD Health Policy Studies, 28 May 2020, https://www.oecd-ilibrary.org/social-issues-migration-health/waitingtimes-for-health-services_242e3c8c-en 21 Woolhandler, Himmelstein. “Administrative Work Consumes One-Sixth of U.S. Physicians’ Working Hours and Lowers Their Career Satisfaction,” International Journal of Health Services, 2014, pp. 635–642. 22 “Adult Obesity Facts,” CDC Centers for Disease Control and Prevention, May 2022, https://www.cdc.gov/obesity/data/adult.html 23 Arterburn, et al. “Introducing Decision Aids at Group Health Was Linked to Sharply Lower Hip and Knee Surgery Rates and Costs,” Health Affairs (Millwood), 31 Sept. 2012, pp. 2094–2104, https://pubmed.ncbi.nlm.nih.gov/22949460/ 24 Greene, et al. “When Patient Activation Levels Change, Health Outcomes and Costs Change, Too,” Health Affairs, vol. 34, no. 3, 2015, pp. 431–437. https://www. healthaffairs.org/doi/pdf/10.1377/hlthaff.2014.0452 25 Basch, et al. “Overall Survival Results of a Trial Assessing Patient-Reported Outcomes for Symptom Monitoring During Routine Cancer Treatment,” JAMA, 11 July 2017, pp. 197–198, https://kaikuhealth.com/for-cancer-clinics/?gclid=EAIaI QobChMIhaSCz_mp9AIVgo9oCR3edgtXEAMYAyAAEgIAiPD_BwE 26 “Increased Patient Empowerment the Main Benefit of Digital Health Tools: Poll,” Pharmaceutical Technology, 21 April 2021, https://www.pharmaceutical-technology. com/news/increased-patient-empowerment-the-main-benefit-of-digital-healthtools-poll/ 27 “Digital Health Market Size, Growth, Trends, Report 2022–2030,” Precedence Research, 2022, https://www.precedenceresearch.com/digital-health-market 28 “Gartner Forecasts Global Spending on Wearable Devices to Total $81.5 Billion in 2021,” Stamford, 12 Jan. 2021, https://www.gartner.com/en/newsroom/press-releases/ 2021-01-11-gartner-forecasts-global-spending-on-wearable-devices-to-total-81-5billion-in-2021 29 “Health and Wellness Wearable Electronic Devices Final Report,” Global Electronics Council, May 2021, https://globalelectronicscouncil.org/wp-content/uploads/FINAL_ HWWEDSOSR_Combined_05May2021.pdf 30 “Digital Health Market Size to Garner Around US$551.1 Bn by 2027,” Precedence Research, 30 June 2021, https://www.globenewswire.com/news-release/2021/06/30/ 2255806/0/en/Digital-Health-Market-Size-to-Garner-Around-US-551-1-Bnby-2027.html 31 Phelps, et al. “Greater Transparency and Interoperability in Health Care,” Deloitte Insights, 25 Jan. 2021, https://www2.deloitte.com/us/en/insights/industry/health-care/ health-care-pricing-transparency-operability.html 32 “Being Patient-Centric as a Consequence of the Covid-19 Pandemic Updating Patient View’s Model of 2017,” Patients View, 2nd ed., Aug. 2021, https://app.box. com/s/k2m2om9e0yx1o2cqxt5mab4pr1jb4ryp 33 Singh, Banerjee. “Internet and Doctor–Patient Relationship: Cross-Sectional Study of Patients’ Perceptions and Practices,” Indian Journal of Public Health, 2019, https:// www.ijph.in/text.asp?2019/63/3/215/267223 34 Jakicic, et al. “Effect of Wearable Technology Combined with a Lifestyle Intervention on Long-Term Weight Loss,” JAMA Network, 20 Sept. 2016, http://jama.jama network.com/article.aspx?articleid=2553448

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35 ScolarSpace. https://scholarspace.manoa.hawaii.edu/bitstream/10125/59821/0381.pdf 36 Williams. “‘Avoidable’ ER Visits Fuel Health Care Costs,” U.S. News and World Report, 22 July 2019, https://www.usnews.com/news/health-news/articles/201907-22/avoidable-er-visits-fuel-us-health-care-costs 37 Aljuaid, et al. “Smartphone ECG Monitoring System Helps Lower Emergency Room and Clinic Visits in Post–Atrial Fibrillation Ablation Patients,” Clinical Medicine Insights: Cardiology, Jan. 2020, https://www.researchgate.net/publication/338715667_ Smartphone_ECG_Monitoring_System_Helps_Lower_Emergency_Room_and_ Clinic_Visits_in_Post-Atrial_Fibrillation_Ablation_Patients 38 “Trends in Patenting 2020,” European Patent Office, https://documents.epo.org/ projects/babylon/eponet.nsf/0/837DBDFC91C99042C12586950032FDBD/$FILE/ epo_patent_index_2020_infographic_en.pdf 39 “Cancer,” World Health Organization, 3 Feb. 2022, https://www.who.int/news-room/ fact-sheets/detail/cancer 40 Sung, et al. “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians, vol. 71, no. 3, 04 Feb. 2021, pp. 209–249. https://acsjournals.onlinelibrary. wiley.com/doi/full/10.3322/caac.21660 41 Cherlaa, et al. “Cost-Effectiveness of Cancer Drugs: Comparative Analysis of the United States and England,” eClinicalMedicine Research Paper, vol. 29, 01 Dec. 2020, https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(20)30369-2/ fulltext 42 Pechmann, et al. “Randomised Controlled Trial Evaluation of Tweet2Quit: A Social Network Quit-Smoking Intervention,” JJ Tob Control, Mar. 2017, pp. 188–194. 43 “Health Systems,” IFMSA, 2018, https://ifmsa.org/wp-content/uploads/2018/09/ VPA_Program_Health-Systems.pdf 44 Short, et al. “The Performance of the EQ-5D-3L in Screening for Anxiety and Depressive Symptoms in Hospital and Community Settings,” Health and Quality of Life Outcomes, vol. 19, 19 Mar. 2021, https://rdcu.be/cQeJK 45 Smith. “The U.S. Shouldn’t Use the ‘QALY’ in Drug Cost-Effectiveness Reviews,” STAT, 22 Feb. 2019, https://www.statnews.com/2019/02/22/qaly-drug-effectivenessreviews/ 46 Jong-Min Woo, et al. “Impact of Depression on Work Productivity and Its Improvement after Outpatient Treatment with Antidepressants,” Value in Health, vol. 14, 2011, pp. 475–482, https://www.valueinhealthjournal.com/article/S1098-3015 (10)00041-0/pdf 47 Krass, et al. “US Pediatric Emergency Department Visits for Mental Health Conditions during the COVID-19 Pandemic,” JAMA Network, 30 Apr. 2021, https:// jamanetwork.com/journals/jamanetworkopen/fullarticle/2779380 48 Stewart. “Impact of COVID-19 Pandemic on Mental Health and Mood in Europe in 2020,” Statista, 2022, https://www.statista.com/statistics/1233576/covid-19-impacton-mental-health-in-europe/ 49 Nochaiwong, et al. “Global Prevalence of Mental Health Issues among the General Population during the Corona Virus Disease‑2019 Pandemic: A Systematic Review and Meta‑Analysis,” Nature Scientific Reports, 2021 https://rdcu.be/cQeNJ 50 “Depression,” World Health Organization, 13 Sept. 2021, https://www.who.int/newsroom/fact-sheets/detail/depression 51 Arias-de la Torre, et al. “Prevalence and Variability of Current Depressive Disorder in 27 European Countries: A Population-Based Study,” The Lancet Public Health, 2021, pp. 729–738. https://www.thelancet.com/journals/lanpub/article/ PIIS2468-2667(21)00047-5/fulltext 52 “Health at a Glance 2021: OECD Indicators,” OECD Publishing, 2021, https://www. oecd-ilibrar y.org/docser ver/ae3016b9-en.pdf ?expires=1644218192&id=id& accname=guest&checksum=5194EAFF4C256E7C4B28D6211EDFB239

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53 “Global Prevalence and Burden of Depressive and Anxiety Disorders in 204 Countries and Territories in 2020 due to the COVID-19 Pandemic,” The Lancet, vol. 398, 6 Nov. 2021, https://www.thelancet.com/action/showPdf?pii=S0140-6736% 2821%2902143-7 54 “Broader Determinants of Health: Future Trends,” The Kings Fund, 2021, www. kingsfund.org.uk/time-to-think-differently/trends/broader-determinants-health 55 Leigh-Hunt, et al. “An Overview of Systematic Reviews on the Public Health Consequences of Social Isolation and Loneliness,” Public Health, vol. 152, Nov. 2017, pp. 157–171. https://www.sciencedirect.com/science/article/abs/pii/S0033350617 302731 56 “World Life Expectancy 1950–2022,” United Nations World Population Prospects 2019, https://www.macrotrends.net/countries/WLD/world/life-expectancy 57 Van Dongen. “Centenarian Study Suggests Living Environment May Be Key to Longevity,” Washington State University, 17 June 2020, https://www.sciencedaily.com/ releases/2020/06/200617145256.htm 58 “Obesity and Overweight,” World Health Organization, 9 June 2021, https://www. who.int/news-room/fact-sheets/detail/obesity-and-overweight 59 “Global Health Risks Mortality and Burden of Disease Attributable to Selected Major Risks,” ProQuest & World Health Organization, 2009. 60 “America’s Vaccination Woes Cannot Be Blamed Only on Politics,” The Economist, 27 July 2021, https://www.economist.com/united-states/2021/07/27/americasvaccination-woes-cannot-be-blamed-only-on-politics?frsc=dg%7Ce 61 Daniellia, et al. “Systematic Review into City Interventions to Address Obesity,” eCinicalMedicine Research Paper, vol. 32, 01 Feb. 2021, https://www.thelancet.com/ journals/eclinm/article/PIIS2589-5370(20)30454–5/fulltext 62 Chiauzzi, et al.“Factors in Patient Empowerment: A Survey of an Online Patient Research Network,” The Patient, vol. 9, 7 May 2016, pp. 511–523, https://rdcu.be/cQe6B 63 Pueyoa, et al. “Cost-utility and Budget Impact Analysis of Primary Prevention with Alendronate of Osteoporotic Hip Fractures in Catalonia,” Reumatologia Clinica, vol. 8, no. 3, May–June 2021, pp. 105–162, https://www.reumatologiaclinica.org/ en-cost-utility-budget-impact-analysis-primary-articulo-S2173574312000615 64 Hiligsmann, et al. “Cost-Effectiveness of Vitamin D and Calcium Supplementation in the Treatment of Elderly Women and Men with Osteoporosis,” European Journal of Public Health, vol. 25, no. 1, Feb. 2015, pp. 20–25, https://doi.org/10.1093/eurpub/cku119 65 Robinson, et al. “Do the Benefits of COVID-19 Policies Exceed the Costs? Exploring Uncertainties in the Age-VSL Relationship,” Risk Analysis: An Official Publication of the Society for Risk Analysis. vol. 41, May 2021, pp. 761–770, https://doi.org/10.1111/ risa.13561 66 “Health at a Glance 2021: OECD Indicators,” OECD, 9 Nov. 2021, https://doi. org/10.1787/ae3016b9-en 67 Brown, et al. “Mobile Health Applications for People with Dementia: A Systematic Review and Synthesis of Qualitative Studies,” Informatics For Health & Social Care, vol. 45, Apr. 2020, pp. 343–359, http://doi.org/10.1080/17538157.2020.1728536 68 Kroenke, et al. “Effect of Telecare Management on Pain and Depression in Patients with Cancer: A Randomized Trial,” JAMA Network, 14 July 2010, pp. 163–171, https://doi.org/10.1001/jama.2010.944 69 Kuklinski, Oschmann, Pross, et al. “The Use of Digitally Collected PatientReported Outcome Measures for Newly Operated Patients with Total Knee and Hip Replacements to Improve Post-Treatment Recovery: Study Protocol for a Randomized Controlled Trial,” Trials vol. 21, 04 Feb. 2020, https://doi.org/10.1186/ s13063-020-04252-y 70 Kluzek, et al. “Patient-Reported Outcome Measures (PROMS) as Proof of Treatment Efficacy,” BMJ Journals, 4 June 2021, https://ebm.bmj.com/content/ebmed/27/3/153. full.pdf

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71 Kollat. “Get Fit with Nintendo Switch: Playing Ring Fit Adventure Can Help You Lose Weight at Home,” T3, 2 Feb. 2021, https://www.t3.com/features/nintendoswitch-fitness-games-ring-fit-adventure-fitness-boxing-mario-and-sonic-at-theolympics 72 Garret, U. “These Nintendo Switch Fitness Games Will Make You Actually Enjoy Working Out,” CNN Underscored, 8 Sept. 2021, https://edition.cnn.com/ cnn-underscored/electronics/best-nintendo-switch-fitness-games 73 “Empowering Medical Researchers, Doctors, and You,” Apple, https://www.apple. com/lae/researchkit/ 74 Ledford. “Millions of Black People Affected by Racial Bias in Health-care Algorithms,” Nature, vol. 574, 24 Oct. 2019, https://doi.org/10.1038/d41586-019-03228-6 75 Penninx, et al. “Understanding the Somatic Consequences of Depression: Biological Mechanisms and the Role of Depression Symptom Profile,” BMC Medicine, vol. 11, 11 Apr. 2013, https://doi.org/10.1186/1741-7015-11-129

BIBLIOGRAPHY “Adult Obesity Facts.” CDC Centers for Disease Control and Prevention, May 2022, https://www.cdc.gov/obesity/data/adult.html Aljuaid, et al. “Smartphone ECG Monitoring System Helps Lower Emergency Room and Clinic Visits in Post–Atrial Fibrillation Ablation Patients.” Clinical Medicine Insights: Cardiology, Jan. 2020, https://www.researchgate.net/publication/338715667_ Smartphone_ECG_Monitoring_System_Helps_Lower_Emergency_Room_and_ Clinic_Visits_in_Post-Atrial_Fibrillation_Ablation_Patients “America’s Vaccination Woes Cannot Be Blamed Only on Politics.” The Economist, 27 July 2021, https://www.economist.com/united-states/2021/07/27/americasvaccination-woes-cannot-be-blamed-only-on-politics?frsc=dg%7Ce “Applying Behavioural Science to COVID-19.” National Institute for Public Health and the Environment, https://www.rivm.nl/en/coronavirus-covid-19/research/behaviour Arce, et al. “COVID-19 Vaccine Acceptance and Hesitancy in Low- and Middle-Income Countries.” Nature Medicine, vol. 27, 16 July 2021, pp. 1385–1394, https://www. nature.com/articles/s41591-021-01454-y Arias-de la Torre, et al. “Prevalence and Variability of Current Depressive Disorder in 27 European Countries: A Population-Based Study.” The Lancet Public Health, 2021, pp. 729–738, https://www.thelancet.com/journals/lanpub/article/PIIS24682667(21)00047-5/fulltext Arterburn, et al. “Introducing Decision Aids at Group Health Was Linked to Sharply Lower Hip and Knee Surgery Rates and Costs.” Health Affairs (Millwood), 31 Sept. 2012, pp. 2094–2104, https://pubmed.ncbi.nlm.nih.gov/22949460/ Basch, et al. Overall Survival Results of a Trial Assessing Patient-Reported Outcomes for Symptom Monitoring During Routine Cancer Treatment. JAMA, vol. 318, no. 2, 11 July 2017, pp. 197–198, https://pubmed.ncbi.nlm.nih.gov/28586821/ “Being Patient-Centric as a Consequence of the Covid-19 Pandemic Updating PatientView’s Model of 2017.” PatientsView, 2nd ed., Aug. 2021, https://app.box. com/s/k2m2om9e0yx1o2cqxt5mab4pr1jb4ryp “Broader Determinants of Health: Future Trends.” The Kings Fund, 2021, www. kingsfund.org.uk/time-to-think-differently/trends/broader-determinants-health Brown, Andrew, O’Connor. “Mobile Health Applications for People with Dementia: A Systematic Review and Synthesis of Qualitative Studies.” Informatics for Health & Social Care, vol. 45, Apr. 2020, pp. 343–359, http://doi.org/10.1080/17538157.2020.1728536

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Bucchi. “To Boost Vaccination Rates, Invest in Trust.” Nature Italy, 12 Jan. 2021, https:// www.nature.com/articles/d43978-021-00003-y “Cancer.” World Health Organization, 3 Feb. 2022, https://www.who.int/news-room/ fact-sheets/detail/cancer Cherlaa, et al. “Cost-Effectiveness of Cancer Drugs: Comparative Analysis of the United States and England.” eClinicalMedicine Research Paper, vol. 29, 01 Dec. 2020, https:// www.thelancet.com/journals/eclinm/article/PIIS2589-5370(20)30369-2/fulltext Chiauzzi, et al. “Factors in Patient Empowerment: A Survey of an Online Patient Research Network.” The Patient – Patient-Centered Outcomes Research, vol. 9, 7 May 2016, pp. 511–523, https://rdcu.be/cQe6B Daniellia, et al. “Systematic Review into City Interventions to Address Obesity.” eClinicalMedicine Research Paper, vol. 32, 01 Feb. 2021, https://www.thelancet.com/journals/ eclinm/article/PIIS2589-5370(20)30454-5/fulltext “Depression.” World Health Organization, 13 Sept. 2021, https://www.who.int/newsroom/fact-sheets/detail/depression Desmarais-Tremblay. “W. H. Hutt and the Conceptualization of Consumers’ Sovereignty.” Political Theory: History of Political Thought eJournal, 17 Feb. 2020, https://www. semanticscholar.org/paper/W.-H.-Hutt-and-the-Conceptualization-of-Consumers %E2%80%99-Desmarais-Tremblay/16ff3b5f2f482e19160328e4e41bcaf6ca4b2c38 “Digital Health Market Size, Growth, Trends, Report 2022–2030.” Precedence Research, 2022, https://www.precedenceresearch.com/digital-health-market “Digital Health Market Size to Garner around US$551.1 Bn by 2027.” Precedence Research, 30 June 2021, https://www.globenewswire.com/news-release/2021/06/ 30/2255806/0/en/Digital-Health-Market-Size-to-Garner-Around-US-551-1-Bnby-2027.html Dworkin. “Paternalism.” The Monist, vol. 56, no. 1, 1 Jan. 1972, pp. 64–84. “Empowering Medical Researchers, Doctors, and You.” Apple, https://www.apple.com/ lae/researchkit/ Garret, U. “These Nintendo Switch Fitness Games Will Make You Actually Enjoy Working Out.” CNN Underscored, 8 Sept. 2021, https://edition.cnn.com/cnn-underscored/ electronics/best-nintendo-switch-fitness-games “Gartner Forecasts Global Spending on Wearable Devices to Total $81.5 Billion in 2021.” Stamford, 12 Jan. 2021, https://www.gartner.com/en/newsroom/pressreleases/2021-01-11-gartner-forecasts-global-spending-on-wearable-devices-tototal-81-5-billion-in-2021 “Global Prevalence and Burden of Depressive and Anxiety Disorders in 204 Countries and Territories in 2020 due to the COVID-19 Pandemic.” The Lancet, vol. 398, 6 Nov. 2021, https://www.thelancet.com/action/showPdf?pii=S0140-6736%2821%2902143-7 Greene, et al. “When Patient Activation Levels Change, Health Outcomes and Costs Change, Too.” Health Affairs, vol. 34, no. 3, 2015, pp. 431–437, https://www.healthaffairs. org/doi/pdf/10.1377/hlthaff.2014.0452 “Health and Wellness Wearable Electronic Devices Final Report.” Global Electronics Council, May 2021, https://globalelectronicscouncil.org/wp-content/uploads/FINAL_ HWWEDSOSR_Combined_05May2021.pdf “Health at a Glance 2021: OECD Indicators.” OECD, 9 Nov. 2021, https://doi.org/ 10.1787/ae3016b9-en “Health at a Glance 2021: OECD Indicators” OECD Publishing, 2021, https://www. oecd.org/health/health-at-a-glance/ “Health Systems.” IFMSA, 2018, https://ifmsa.org/wp-content/uploads/2018/09/VPA_ Program_Health-Systems.pdf

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“Health Workforce.” World Health Organization, https://www.who.int/health-topics/ health-workforce#tab=tab_1 Hiligsmann, et al. “Cost-Effectiveness of Vitamin D and Calcium Supplementation in the Treatment of Elderly Women and Men with Osteoporosis.” European Journal of Public Health, vol. 25, no. 1, Feb. 2015, pp. 20–25, https://doi.org/10.1093/eurpub/ cku119 “Increased Patient Empowerment the Main Benefit of Digital Health Tools: Poll.” Pharmaceutical Technology, 21 Apr. 2021, https://www.pharmaceutical-technology. com/news/increased-patient-empowerment-the-main-benefit-of-digital-healthtools-poll/ Jakicic, et al. “Effect of Wearable Technology Combined with a Lifestyle Intervention on Long-Term Weight Loss.” Jama Network, 20 Sept. 2016, http://jama.jamanetwork. com/article.aspx?articleid=2553448 Jennings, et al. “Lack of Trust, Conspiracy Beliefs, and Social Media Use Predict COVID-19 Vaccine Hesitancy.” MDPI Pharmacoepidemiology in Vaccine: Generating the Real-World Data to Promote Vaccine Safety and Uptake, 3 June 2021, https:// www.mdpi.com/2076-393X/9/6/593/htm Kapeliushnikov. “Behavioral Economics and the ‘New’ Paternalism.” Russian Journal of Economics, vol. 1, no. 1, Mar. 2015, pp. 81–107, https://www.sciencedirect.com/ science/article/pii/S2405473915000057 Khan, et al. “Who Doesn’t Want to Be Vaccinated? Determinants of Vaccine Hesitancy During COVID-19.” International Monetary Fund, Working Paper No. 2021/130, 6 May 2021, https://www.imf.org/en/Publications/WP/Issues/2021/05/06/WhoDoesnt-Want-to-be-Vaccinated-Determinants-of-Vaccine-Hesitancy-DuringCOVID-19-50244 Kluzek, et al. “Patient-Reported Outcome Measures (PROMs) as Proof of Treatment Efficacy.” BMJ Journals, 4 June 2021, https://ebm.bmj.com/content/ebmed/27/3/153. full.pdf Kollat. “Get Fit with Nintendo Switch: Playing Ring Fit Adventure CAN Help You Lose Weight at Home.” T3, 2 Feb. 2021, https://www.t3.com/features/nintendoswitch-fitness-games-ring-fit-adventure-fitness-boxing-mario-and-sonic-at-theolympics Krass, et al. “US Pediatric Emergency Department Visits for Mental Health Conditions During the COVID-19 Pandemic.” JAMA Network, 30 Apr. 2021, https://jamanetwork. com/journals/jamanetworkopen/fullarticle/2779380 Kroenke, et al. “Effect of Telecare Management on Pain and Depression in Patients With Cancer: A Randomized Trial.” JAMA Network, 14 July 2010, pp. 163–171, https://doi. org/10.1001/jama.2010.944 Kuklinski, et al. “The Use of Digitally Collected Patient-Reported Outcome Measures for Newly Operated Patients with Total Knee and Hip Replacements to Improve Post-Treatment Recovery: Study Protocol for a Randomized Controlled Trial.” Trials, vol. 21, 04 Feb. 2020, https://doi.org/10.1186/s13063-020-04252-y Ledford. “Millions of Black People Affected by Racial Bias in Health-Care Algorithms.” Nature, vol. 574, 24 Oct. 2019, https://doi.org/10.1038/d41586-019-03228-6 Leigh-Hunt, et al. “An Overview of Systematic Reviews on the Public Health Consequences of Social Isolation and Loneliness.” Public Health, vol. 152, Nov. 2017, pp. 157–171, https://www.sciencedirect.com/science/article/abs/pii/S0033350617302731 “Level of Satisfaction with National Health Systems Worldwide as of 2019, by Country.” Statista Research Department, 20 June 2020, https://www.statista.com/ statistics/1109036/satisfaction-health-system-worldwide-by-country/

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Londakova, et al. “Buying Better: Improving NHS Procurement with Behavioural Insights.” The Bahavioural Insights Team, June 2021, https://www.bi.team/wpcontent/uploads/2021/06/FINAL-BIT-Procurement-in-the-NHS-Report.pdf Nochaiwong, et al. “Global Prevalence of Mental Health Issues among the General Population during the Corona Virus Disease‑2019 Pandemic: A Systematic Review and Meta‑Analysis.” Nature Scientific Reports, 2021, https://rdcu.be/cQeNJ “Obesity and Overweight.” World Health Organization, 9 June 2021, https://www.who. int/news-room/fact-sheets/detail/obesity-and-overweight Parkin, et al. “Vaccine Hesitancy Is Decreasing in Canada, but It’s Too Soon to Celebrate.” The Conversation, 28 July 2021, https://theconversation.com/vaccine-hesitancy-isdecreasing-in-canada-but-its-too-soon-to-celebrate-165133 Patel, et al. “Generic Medication Prescription Rates after Health System – Wide Redesign of Default Options within the Electronic Health Record.” Jama Internal Medicine, June 2016, https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2520677 Pechmann, et al. “Randomised Controlled Trial Evaluation of Tweet2Quit: A Social Network Quit-Smoking Intervention.” JJ Tob Control, Mar. 2017, pp. 188–194. Penninx, et al. “Understanding the Somatic Consequences of Depression: Biological Mechanisms and the Role of Depression Symptom Profile.” BMC Medicine, vol. 11, 11 Apr. 2013, https://doi.org/10.1186/1741-7015-11-129 Phelps, et al. “Greater Transparency and Interoperability in Health Care.” Deloitte Insights, 25 Jan. 2021, https://www2.deloitte.com/us/en/insights/industry/health-care/healthcare-pricing-transparency-operability.html Pueyoa, et al. “Cost-Utility and Budget Impact Analysis of Primary Prevention with Alendronate of Osteoporotic Hip Fractures in Catalonia.” Reumatologia Clinica, vol. 8, no. 3, May–June 2021, pp. 105–162, https://www.reumatologiaclinica.org/ en-cost-utility-budget-impact-analysis-primary-articulo-S2173574312000615 Raguso. “Council Approves $1.5M to Fight Soda Consumption.” Berkeleyside, 20 Jan. 2016. Robinson, et al. “Do the Benefits of COVID-19 Policies Exceed the Costs? Exploring Uncertainties in the Age-VSL Relationship.” Risk Analysis: An Official Publication of the Society for Risk Analysis, vol. 41, May 2021, pp. 761–770, https://doi.org/10.1111/ risa.13561 Robitaille. “A Little Nudge Goes a Long Way in Increasing Organ Donor Registrations,” 2 May 2019, https://theconversation.com/a-little-nudge-goes-a-long-way-in-increasing-organ-donor-registrations-115051 Scobie, et al. “Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status – 13 U.S. Jurisdictions, April 4–July 17, 2021.” Morbidity and Mortality Weekly Report, 2021, pp. 1284–1290. “ScolarSpace.” https://scholarspace.manoa.hawaii.edu/bitstream/10125/59821/0381.pdf Short, et al. “The Performance of the EQ-5D-3L in Screening for Anxiety and Depressive Symptoms in Hospital and Community Settings.” Health and Quality of Life Outcomes, vol. 19, 19 Mar. 2021, https://rdcu.be/cQeJK Singh, Banerjee. “Internet and Doctor–Patient Relationship: Cross-Sectional Study of patients’ Perceptions and Practices.” Indian Journal of Public Health, 2019, https://www. ijph.in/text.asp?2019/63/3/215/267223 Smith. “The US shouldn’t Use the ‘QALY’ in Drug Cost-Effectiveness Reviews.” STAT, 22 Feb. 2019, https://www.statnews.com/2019/02/22/qaly-drug-effectiveness-reviews/ Soofi, et al. “Using Insights from Behavioral Economics to Mitigate the Spread of COVID-19.” Applied Health Economics and Health Policy, vol. 18, May 2020, pp. 345–350, https://link.springer.com/article/10.1007/s40258-020-00595-4

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Stewart “Impact of COVID-19 Pandemic on Mental Health and Mood in Europe in 2020.” Statista, 2022, https://www.statista.com/statistics/1233576/covid-19-impacton-mental-health-in-europe/ Sung, et al. “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.” CA: A Cancer Journal for Clinicians, vol. 71, no. 3, 04 Feb. 2021, pp. 209–249, https://acsjournals.onlinelibrary. wiley.com/doi/full/10.3322/caac.21660 Thaler, Sunstein. Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press Ltd, New Haven, 2008. The Behavioural Insights Team, https://www.bi.team/ “Trends in Patenting 2020.” European Patent Office, https://documents.epo.org/projects/ babylon/eponet.nsf/0/837DBDFC91C99042C12586950032FDBD/$FILE/epo_ patent_index_2020_infographic_en.pdf Van Dongen. “Centenarian Study Suggests Living Environment May Be Key to Longevity.” Washington State University, 17 June 2020, https://www.sciencedaily.com/releases/ 2020/06/200617145256.htm “Waiting Times for Health Services: Next in Line.” OECD Health Policy Studies, 28 May 2020, https://www.oecd-ilibrary.org/social-issues-migration-health/waitingtimes-for-health-services_242e3c8c-en Woo, et al. “Impact of Depression on Work Productivity and Its Improvement after Outpatient Treatment with Antidepressants.” Value in Health, vol. 14, 2011, pp. 475–482, https://www.valueinhealthjournal.com/article/S1098-3015(10)000410/pdf Woolhandler, Himmelstein. “Administrative Work Consumes One-Sixth of U.S. Physicians’ Working Hours and Lowers Their Career Satisfaction.” International Journal of Health Services, vol. 44, no. 4, 2014, pp. 635–642. World Health Organization. Global Health Risks: Mortality and burden of disease attributable to selected major risks. [Report]: WHO; 2009. “World Life Expectancy 1950–2022.” United Nations World Population Prospects 2019, https://www.macrotrends.net/countries/WLD/world/life-expectancy Williams. “‘Avoidable’ ER Visits Fuel Health Care Costs.” U.S.News and World Report, 22 July 2019, https://www.usnews.com/news/health-news/articles/2019-07-22/ avoidable-er-visits-fuel-us-health-care-costs

3 HOW TO CHANGE THE MINDSET TO EMBRACE OPPORTUNITIES OF THE DIGITAL REVOLUTION IN HEALTHCARE?

What is wrong with today’s healthcare system? Is it time to abandon the paternalism and third-party agent perspective? According to Kuhn, the drive for change has to be of such significance that it will evoke a crisis, making it more than just an anomaly. This must lead to difficulty in the adoption of the paradigm shift. As Kuhn says, “Failure of existing rules is the prelude to a search for new ones.”1 Has the pandemic let us observe “the persistent failure of the puzzles of normal science.”

3.1 How to switch the mindset to individual sovereignty and holistic health Limited healthcare resources are the key concerns of today’s healthcare sector. For example, the life expectancy at birth has increased by at least 12 years compared to 19602 and there will be more than 20% of world’s population over age 60 in 2050.3 The healthcare system is always a hot political issue. The increase in health taxes or healthcare insurance premiums have their limits. It is therefore a dilemma of conflicting objectives and demands for unlimited access to healthcare against limited healthcare budgets. But there is a way. A better option is to switch the focus from prevention to cure. To do that, we need to teach patients how to develop their own individual healthcare system of preventive measures and instruments to mitigate the risk of exposure to health decrements. I advocate that we have to introduce at least two major changes to achieve that vision. In the previous chapter, I provided some arguments. First, it is to introduce firmly and fully the principle of individual sovereignty. Second, it is to introduce a holistic approach to the definition of health. It will not be easy, as today’s healthcare system is built on the principle of paternalism and has a limited focus on health gains from the perspective of QALY, which are defined DOI: 10.4324/b23291-3

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by decision-makers as explained earlier. Therefore, I advocate for the paradigm shift to introduce such a radical change. I introduced two new axioms of health economics in order for both changes to be rooted in science. Health economics was built to define the organization of healthcare systems irrespective of cultural, social and geographical context. I approach my vision of a paradigm shift with the same assumption of being relevant for any jurisdiction, irrespective of healthcare system organization. The paradigm shift with new axioms will lay new foundations for any healthcare system that will eventually build its own new patient-centric ecosystems. It is the mindset shift that develops new scientific grounds for health economics. Kuhn indicates clearly that paradigm shift does not happen suddenly. It starts with the awareness of the anomaly. But it an anomaly that calls into question explicit and fundamental generalizations of the paradigm. As Kuhn says, it is “proliferation of divergent articulations … the rules of normal science become increasingly blurred.” In other words, there must be a failure of adoption of existing rules that acts as a prelude to a search for new rules or more precisely with the emergence of new phenomena. The disruption of trust during the pandemic as well as mental health outbreaks revealed what was surely the driver of crisis, which Kuhn identifies as a necessary prelude to a paradigm shift. Still, how can we ensure the change truly happens? Patient’s empowerment is obviously in opposition to paternalism. Still, maybe a patient-centric approach instead of governmental control can do more good than harm in monetary value in addition to being more informed. How shall we replace the concept of paternalism with individual sovereignty and develop a more holistic approach to health outcomes? How can we ensure that patient empowerment will turn costly governmental failure into a cost saving mode? How can we ensure that the healthcare system supports patients to take control of their own health, actively participate in decision-making and, more importantly, join in the quest for self-defined health improvement, ensuring they emerge from the darkness of non-knowledge. What shall we offer to those who want to keep the status quo with a paternalistic payer system? As Kuhn stated, “After the discovery had been assimilated, scientists were able to account for a wider range of natural phenomena or to account with greater precision for some of those previously known. But that gain was achieved only by discarding some previously standard beliefs or procedures and, simultaneously, by replacing those components of the previous paradigm with others.” Therefore, I believe it requires a mindset shift to ensure that patient empowerment is a revolutionary paradigm shift from treatment to prevention. The mindset shift would redefine the system of healthcare and build it anew on the communication channels across all stakeholders that allow fast exchange of credible, objective and trustworthy information. In order to do that, we need to redefine the principles and foundation of the new system. It requires not only legal changes, but also a mindset shift and the development of cognitive trust. The major driver of that process should have an understanding of the new

How to change? 41

role of the patient who needs to become not just a receiver but an aware and well-educated partner in the clinical process. The examples above show that the healthcare system without patient empowerment may be less efficient. There is another question to be raised: Is it ethical to leave a patient outside of the decision-making process, using the excuse of information asymmetry? Yes, you read it correctly “excuse.” Until now we have notoriously declined individual sovereignty, blaming asymmetric information in favour of physicians. We assumed that physicians possessed more information than patients. Is it still the case? I do not think so. Since digital health solutions are used by patients themselves, if equipped with right skills and qualifications to read and interpret the data, patients, not physicians, will eventually possess more information. The era of information asymmetry is over. The era of individual sovereignty of patients has just arrived. As Kuhn rightly noted, “epistemological counterinstances are to constitute more than a minor irritant, that will be because they help to permit the emergence of a new and different analysis of science within which they are no longer a source of trouble.” Upon the arrival of the digital revolution, it is the healthcare system that misses information about patients’ medical needs; therefore, it is not successful anymore and in order to save us from market failure we introduced paternalistc structure which unintendedly moved us towards government failure. Still, it is up to the government to equip the patient with appropriate qualifications and skills to help them read the data and act upon the information collected. A digital transformation allows the healthcare system to be focused on proactive actions and more precisely on preventive measures.

3.2 Mindset shift towards individual sovereignty Discussing the importance of the mindset shift towards patient empowerment, we need to determine the exact definition. The WHO defines it as “the process of building the capacity of patients, families, carers, as well as health care providers, to facilitate and support the active involvement of patients in their own care, in order to enhance safety, quality and people-centredness of health care service delivery.”4 Other researchers refer to the “the process of an enabling the shift in the balance of power and the outcome of this process.”5 “The dimensions reflect outcome indicators, such as participation in decision-making and control, and process indicators, such as knowledge acquisition and coping skills.” Indeed there are many definitions of patient engagement, but all share an underlying theme: the facilitation and strengthening of the role of those using services as coproducers of health and healthcare policy and practice. Although there are a number of definitions of patient engagement with similar traits, still it does not mean that healthcare systems operate in the mode of putting patients at the centre of decision-making. Researchers tried to reviewed whether there is any evidence of a benefit to putting the patient more in control of their health decisions.6 In total, only 30 studies with 19 different instruments of patient empowerment were identified.7

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Such instruments may support the assessment of educational programs for patients. One of these 19 questionnaires, the health education impact questionnaire (HeiQ), was used for the analysis of the impact of an educational programme in Denmark for 83 patients with type 2 diabetes. The program encompassed disease-specific patient education, dietary counselling, advice about physical activity and smoking cessation support organized in group sessions one to two times per week over 2–10 weeks. Of 35 items, the questionnaire measures seven domains: health-directed activity; positive and active engagement in life; emotional well-being; self-monitoring and insight; constructive attitudes and approaches skill and technique acquisition; and social integration and support. The largest improvement was observed in new skill and technique acquisition at both two weeks and 12 months. The greatest improvement was observed with respect to new skill and technique acquisition at two weeks and 12 months. The study indicates that educational activities helped the patients increase their independence, learn how to accept having a chronic illness and reduce negative attidues toward their disease.8 The implementation of the standard of patient engagement requires not only instruments to measure it but also the tools that equip patients with correct knowledge. Interestingly enough, the  International Patient Decision Aid Standards (IPDAS) were established in 2003 and identified 500 patient decision aids.9 In addition to that, the Cochrane review of decision aids revealed that up to April 2015, there were 105 studies involving 31,043 people testing such aid tools in the context of surgery, screening (e.g., prostate cancer, colon cancer, prenatal), genetic testing and medication treatments (e.g., diabetes, atrial fibrillation). The analysis of these studies revealed that such aids improve not only patient knowledge of the alternative options (high-quality evidence) but also make patients feel better (high-quality evidence). I am happy to note so many examples of patient empowerment, but even greater implementations of such treatment aids will not produce a mindset change towards individual sovereignty. The paradigm shift requires patient self-reliance and accountability for his or her health. In other words, it does require a shift of decision-making from the doctor’s office to a patient’s mobile phone, which is the digital centre of healthcare data. How do we achieve a mindset shift that will drive us to a new axiom of individual sovereignty? We must educate both patients and clinicians, as the latter need to change their role from decision-makers to consultants who support patients on their health journey. How do we make it happen? Ironically, we should look to the past to Kant. It may seem odd to use Kant’s guidance to lead us out the dark era of paternalism to a new era of patient enlightenment. Our educative efforts can be filled with Kant’s approach. He called for everyone to be an autonomous agent, acting according to the idea of free will for a human to be self-governed. According to Kant, autonomous action is action that is deliberately and self-consciously motivated by moral reasons. The person is autonomous if she/he has freedom of will unaffected by external factors and driven only by his/ her own rationality – the “quintessential expression of human rationality.”10 In

How to change? 43

the paternalistic healthcare system, Kant’s idea works fine as long as individual choices are deemed rational within the pre-established context of political, legal and cultural mechanisms. What if some decisions are irrational for others but rational for the patient? Kant describes the notion of individual rationality as the underlying concept of moral autonomy. Some experts go one step further and point towards mental capacity instead of rationality. Kant’s “autonomy of the will” does not only determine the power to judge autonomously, which he calls the “reason,” but also requires individuals to assume responsibility. “Crucial to autonomy, therefore, is the exercise of reason, and crucial to rational reasoning is a good ground of knowledge and understanding.”11 So shall we allow an individual to execute his own decisions no matter others’ opinions, but ensure he has a basic understanding of what this decision entails? The answer is brought by Kant himself again. Kant called for enlightenment, which is man’s emergence from his self-imposed nonage. Nonage, according to Kant, is the “inability of making use of one’s own understanding without the guidance of another.”12  He talked about “self-appointed guardians” who empowered themselves and took the authority to make decision on behalf of others. Being very direct, he noted, “Laziness and cowardice are the reasons why such a large part of mankind gladly remain minors all their lives, long after nature has freed them from external guidance.” According to Kant, guardians empowered themselves by showing their listeners “the danger that would threaten them if they should try to walk by themselves.” Reading Kant’s essay, it’s almost hard to believe that it was created more than a century ago. It can be used as a pure description of the current paternalistic nature of healthcare system. Among others, he used doctors as an example of guardians. The self-imposed guardians include today not only medical professionals but also governmental bodies that we even explicitly manage to name as decision-makers. As he noted himself, it is however “difficult for the individual to work himself out of the nonage which has become almost second nature to him. He has even grown to like it, and is at first really incapable of using his own understanding because he has never been permitted to try it.” Kant speaks therefore about cultivating our own minds in the spirit of autonomous self-governance. Still, the mental capacity to evaluate the circumstances and arrive at the decision does not automatically guarantee the right to act upon it. We need to allow individuals to act upon their own will hence legal form is necessary as long as our actions does not harm others. That brings us to the notion of Mill’s idea of liberty, which says that the only limits of freedom are related to the harm of others. That the only purpose for which power can be rightfully exercised over any member of a civilized community, against his will, is to prevent harm to others. His own good, either physical or moral, is not a sufficient warrant. … Over himself, over his own body and mind, the individual is sovereign (https://www.mtsu.edu/first-amendment/article/1258/john-stuart-mill).

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The choice of Kant and Mill’s philosophies has a much greater impact than both mental and legal capacity. To conclude, the return to individual sovereignty is therefore a process that needs to be driven by individual awareness and many educational efforts of today’s healthcare guardians. Can we forget about paternalism completely? Not really. It is worth recalling Sunstein (2014), who calls for a “means paternalism” that aims to help individuals achieve their own ends, as judged by themselves.13 Some propose epistemic paternalism that focuses on beliefs or knowledge instead of behaviours. This approach aims to increase the level of justification of already-existing beliefs or make the beliefs turn into knowledge. Sometimes it is narrowed to the actions related to withholding evidence for the sake of promoting true beliefs.14 The Pigouvian solution, particularly through the implementation of sin taxes on products with negative impact on our health, should be considered as well.

3.3  Mindset shift towards holistic health I believe that a natural consequence of the mind shift towards individual sovereignty will be one of the holistic views on health. It will go beyond the QALY approach defined through the third-party agent lens. The holistic approach is one that is based on subjective preferences. Given that choices reveal preferences, then preference being a normative criterion depends on the freedom of choice. Hence, the adoption of paternalism actually does not allow the development of free choices. Behavioural welfare scientists speak of a “planner” or a “choice architect” that should make the decision the individuals would make if they had perfect foresight, no weakness of will and if they had chosen according to well-informed preferences. How shall we really define health from an individual sovereignty perspective? The discussion I brought in the previous chapter indicates that life satisfaction is perhaps much broader than quality of life analysed in the healthcare sector but still remains impactful on health. According to the welfare approach, the right act is the act that maximizes well-being. It perhaps is too broad to be adaptable. If we still want to look for the holistic perspective, perhaps we could go a bit further back in the history of science. Let’s take perhaps the Hedonistic Utilitarianism approach that says: the right act is the act that maximizes happiness or pleasure. On the hedonistic pathway, shall we follow Aristippus’s hedonism and focus on patient engagement in activities that bring them positive experiences while limiting those of a negative nature, or should we rather follow Aristotle’s “eudaimonic” form of happiness? Aristotle wrote 2300 years ago: “Happiness is the meaning and the purpose of life, the whole aim and end of human existence.”15 His notion of eudaimonia indicated the “pursuit of virtue, excellence, and the best within us.”16 Adopting such broad view on health will naturally bring us to the question of how we redefine the treatment outcome for any technologies that will be used to improve our newly defined health and how we shall change the evaluative space

How to change? 45

of the healthcare system as well. This approach raises another set of questions, such as the construct of “outcome” defined from preferences (whose?) that who is to decide about the value of healthcare services and products? It is important to bring up the notion of opportunity cost, that is forgone opportunities, provided we spend a given budget on a given healthcare solution. Are healthcare costs to be evaluated with respect to resources used outside the healthcare sector? What kind of determinants decide what (and whom) to include and what to exclude? Perhaps the answers can be brought by another welfarist approach called consequentialism. As some health economists like to frame it, “it is the ethical doctrine that one should judge actions by the outcomes that can reasonably be expected to follow, and not by the actors’ intention or by fidelity to an abstract moral principle.”17 My suggestion would be that we should simply define the value from the standpoint of those who will be affected by the given health solutions. This raises yet another important ethical question: how far shall we actually allow patients to be aware of health issues to allow them to make informed choices in a holistic health approach? This is the “hard case” for the defence of the public’s right to information. To explore the best arguments for withholding information, we will therefore assume a form of methodological consequentialism. In other words, we will assume that holistic health means each individual should be left alone to define it. However, taking the health holistically from an individual perspective will allow us to have many subjective definitions of health. The perception of health problems will be left to the individual. The issue remains: what kind of information on a given health condition should be available for the public and how to determine whether to make it available? That warrants a specific discussion with the general public. Despite these challenges to be solved on a local level, I believe that the adoption of a holistic approach to health will not only allow us to understand health problems from patient perspective better “beyond the third-party agent perspective,” but also make the healthcare system more agile to detect health problems. As the view on health broadens, several new things will appear visible. Not only will we be able to detect more health problems but also find more solutions early. A very interesting example is the Danish OPUS study that compared a specialized assertive early intervention programme (OPUS treatment is defined by individually tailored treatment schedule with social skills training all delivered by a design team consisting of social workers, psychologists, psychiatric nurses, occupational therapists and a psychiatrist) to treatment as usual (TAU, Community Mental Health Centers) for young people experiencing their first episode of non-affective psychosis (FEP). The OPUS group of 347 patients experienced fewer psychiatric hospital visit days during the first three years of the 10-year follow-up period compared to 200 patients in the TAU group.18 When we acknowledge the need for a holistic approach, the next question that arises is how can we quantify the benefits of preventive measures including non-health technologies that are currently not considered as added value? It has been shown earlier how a limited focus is spent on prevention in the healthcare

46  How to change?

system today. So far, prevention had been under the premise of public health interventions. I believe that when we redefine the objective of the healthcare system and adopt a holistic perspective, it will be easier to shift the focus from treatment to prevention. It will grow our understanding of how non-healthcare technologies can improve our health and also how sociodemographic health determinants can help us understand sources of health problems so we can address them more effectively in a different new way. Finally, we need to ask ourselves how the adoption of a holistic approach can help us to identify cost-saving options. Do we need an analytical framework for that? I believe that the shift from QALY to a holistic perspective will require adoption of new methodological approaches. It is not only ensure our understanding how sociodemographic characteristcs may confound treatment effect. There is growing body of literature to study the impact of determinannts of quality of life.19 If it is, for example, about the assessment of the impact on the health of nonhealth technologies, how would we do that? It is truly about redefining health outcomes into multi-dimensional improvements built more on the synergetic connection of different components rather than just being generated from a single intervention. Can a standard such as randomised controlled trial (RCT), widely adopted for regulatory approval, truly track that? RCT set up “artificial settings” to allow the assessment of safety and efficacy of a given intervention. However, what we do observe in real-life is effectiveness when a given intervention does not influence the patient’s health in isolation from other determinants of health. Let us first take a step back and develop new assessment rules about innovative health interventions to cover the health impact in a fully holistic manner. The key question is whether we still want the incremental change we are looking for. Surely, it is about safety and the positive impact on individual health. While the former is dependent on intervention, the latter must be subjective and influenced by multiple factors that occur at the moment a given intervention is provided. In other words, holistic does not only imply to look at patient from his well-being standpoint, but also ensures that the synergic effect of intervention and the ecosystem are captured as well. Holistic means adoption of a 360-degree view on everything we do to achieve the ultimate objective. Shall we abandon the quest for incremental difference? It would be truly revolutionary. For example, there is an approach that simply suggests “justificatory conditions” for the assessment of given interventions from a holistic perspective. Childress et al. proposed the following: (1) effectiveness, (2) proportionality, (3) necessity, (4) least infringement and (5) public justification. Such criteria could be potentially of supplementary nature. What I am trying to argue here is that with the launch of a holistic approach comes something beyond incremental value. It is not only about the health outcome but also about how we design a new treatment pathway. It was already exemplified earlier by the OPUS study. It is worth mentioning that the OPUS

How to change? 47

study was continued beyond the study period. Patients who received OPUS treatment after implementation (N = 3328) had a tendency towards lower mortality (hazard ratio = 0.60, 95% CI = 0.33, 1.09), fewer and shorter psychiatric admissions, and possibly fewer filled prescriptions of antipsychotics and other psycholeptics after four or five years. Not only did the OPUS treatment maintain its efficacy after it was implemented as a standard treatment, it paralleled or surpassed many of the effects observed when the OPUS intervention was delivered in a randomized trial.20 This is an example of how the adoption of a holistic approach may lead to positive outcomes defined in a pragmatic way as a simple reduction in healthcare resource utilization apart from health outcomes. More importantly, we must reject the need to elicit incremental added value of a given treatment and instead focus holistically on the treatment intervention of multi-components. The mindset shift towards a holistic outcome might require abandoning the condition of incremental value generated from a single intervention. Instead the incremental value may arise from the integration of multiple interventions whereas each one seperately does not constitute enough of added value. I may go one step further with my deliberation; that is, instead of incremental value, we might move in the opposite direction and pair interventions to achieve an optimal treatment pathway. How do we do it? There are some methodological approaches available to implement that vision. One option to define optimal treatment ingredients is that we might adopt a component study in which a given intervention is compared with the intervention with at least one component removed (a dismantling study). We could also compare a given intervention where a component is added to an existing intervention to test whether it improves outcomes (an additive study). Alternatively, a factorial experiment can be chosen to allow one to explore the main effects of factors and interactions among factors. One of the big advantages of factorial designs is that they allow researchers to look for interactions between independent variables. An interaction is a result in which the effects of one experimental manipulation depend upon the experimental manipulation of another independent variable. An interaction effect exists when the differences on one factor depend on the level of another factor. It’s important to recognize that an interaction is between factors, not levels. A fractional factorial design is a variation on the factorial design that employs a systematic approach to reduce the number of experimental conditions to allow a more manageable study at the cost of allowing only the main effects and a pre-specified set of interactions to be tested. Fractional factorial designs require the assumption that higher-order interactions are negligible in size, because they are confounded, or aliased, with lower-order effects. The practical approach to introduce factorial design in a real-life setting is the Multiphase Optimization Strategy (MOST) approach. The principles underlying MOST are drawn from engineering and emphasize efficiency. It provides an evaluation framework that determines the most efficient and scalable combination of

48  How to change?

interventions that are empirically proven to be responsible for changes in specific outcomes. It helps to differentiate “active ingredients” from “inactive.” MOST consists of three phases, each of which addresses a different set of questions about the intervention by means of randomized experimentation. 1. Preparation stage: relevant factors and components to be investigated are identified. These components are then pilot tested for acceptability, feasibility, evidence of effectiveness and ease of implementation and refined as needed. MOST also involves the identification of the optimization criterion, which is the operational definition of the target change sought that is used to judge the optimal intervention, subject to resource or other constraints. Constraints such as limits on cost, time and participant logistical or cognitive burden can be incorporated into the optimization criterion, but they must be explicitly identified and operationalized. 2. Optimization stage: a factorial experiment is used to evaluate the main effects and interactions of each factors and select the components and component levels that make up the intervention that meets the optimization criterion. At the end of the optimization phase, the investigator has selected the intervention components and component levels that make up the optimized intervention. The first consideration when selecting the subset of the experimental conditions is statistical, with a need to maintain a balanced design in which every factor occurs an equal number of times at each level, and in which all factors are orthogonal to each other. This requires the potential configurations of subsets available. These designs can be developed using factorial design tables using some statistical packages. The second key consideration is to select the subset of experimental conditions that maximizes the ability to estimate the main effects and interactions that are of highest priority for the research question. 3. Evaluation stage: an optimized intervention based on the results of the previous trial is tested in an RCT. In summary, the mindset shift from the third-party agent perspective to a holistic view on health requires replacement of the incremental value of a single health technology in favour of an aggregated value, which means that intervention should be holistically defined with multiple components. The holistic approach is a quest for the synergy across different elements constituting the wholeness of a given health intervention. The holistic view on health allows the definition of the outcome of the intervention from the standpoint of its consequences as well. Hence, moving out of the box of quality and life expectancy defined from the third-party agent perspective brings us many new dimensions. It is about new challenges in how to define health and what kind of information we should use, both available and unavailable, for a given individual. It is about new opportunities to react to health problems more promptly, adopting the shift from treatment to prevention as well!

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3.4 Why the digital revolution will accelerate a mindset shift in healthcare In 2018, medical technology was the leading field of patent applications at the European Patent Office (EPO). In 2019, digital communication became number one, with computer technology being the second fastest growing.21 In 2020, digital communication again received second place, with medical technology being number one. Obviously, the medical technology industry has an edge on the innovation pathway compared to pharmaceuticals and biotechnology given their shorter life cycle as well as a less budget intense research and development process. Still, the fact that both medical and digital technologies are the source of innovation is a very real sign of the fourth industrial revolution. It is data that fuels that process. The volume of data produced in the world is growing rapidly, from 33 zettabytes in 2018 to an expected 175 zettabytes in 2025.22 Once combined with other technologies, such as Big Data, 5G or artificial intelligence, they enable the automation of entire business processes, including repetitive intellectual tasks previously performed by humans. Digital communication is defined broadly as any technology from telecommunications and computers that builds connectivity with the exponential growth mainly due to 5G wireless networks. It is estimated that the cumulative additional GDP contribution of new digital technologies could amount to EUR 2.2 trillion in the EU alone by 2030, which is actually equivalent to the combined GDP of Spain and the Netherlands from 2019.23 In 2021, Internet of Things (IoT) analytics expects the global number of connected IoT devices to grow 9%, to 12.3 billion active endpoints from 11.3 billion in 2020.24 The main revenue driver for 54% of enterprise IoT projects is cost savings.25 Digital technologies are the vehicle that will drive us towards the era of individual sovereignty and a holistic view on healthcare objectives if only we do it correctly … There are at least two arguments to prove that. First, the introduction of individual sovereignty is simply the prerequisite to embracing the wealth of the digital revolution. Patients need to be interested to use digital technologies and engage themselves with data collection. This is what I try to advocate for throughout this book. The paradigm shifts to a new health economics axiom towards individual sovereignty. From patient empowerment to holistic health is just a “short distance.” In the patient-centric healthcare system, it will be patient who will decide his or her health dimensions. So those two are indeed interconnected. Reaching back to Kant’s theory of how to introduce individual sovereignty, we need to focus on awareness and educational efforts. That’s the mindset shift I focus on in this chapter. The fuel of the digital revolution is data, data comes from individuals and individuals need to engage themselves in the process of data sharing. Hence, I believe that the mindset shift towards individual sovereignty and a holistic perspective on health will be accelerated. Digital technologies are hungry for data and interconnectivity across different data outlets. Individual sovereignty and the holistic approach are actually

50  How to change?

pre-requisites for the acceleration of the digital revolution. It is estimated that E-health solutions in Germany and France alone can bring savings of €55 billion, and if extrapolated for European Union, it would arrive at €120 billion.26 The key sources are five groups: online interactions and monitoring, paperless data, workflow automation, decision support and appropriate patient self-care. As I showed earlier, sooner or later we will understand that the digital era is the era of efficiency gains, as we can focus more on prevention thanks to patients taking control of their health. When payers actually realize that digital solutions are a less expensive and faster way compared to the standard mode, I predict most will choose the most economical route. A similar situation we experience during the introduction of generic compounds. The patient, by default, is prescribed the cheapest version of a drug. If he/she wants to have the original compound, he/ she has to pay the price difference out of pocket. The same will eventually happen with digital health. Second, due to limited human and financial resources, governments will eventually turn towards digital solutions after seeing how they can produce efficiency and savings. Patients will turn towards them, acknowledging it is eventually faster option to get medical aid. Empowerment of patients through data is in the interest of decision-makers. Empowered, knowledgeable patients are easier and less expensive to treat according to their wishes understand the consequences of diseases and want them to be prevented. The average cost per inperson visit is US$125. The average cost for a telehealth visit is around US$45.27 One hospital reports it saves US$86.64 every time a telehealth application is used over an in-person visit in the ER or urgent care.28 The Teladoc company, which is one of the leaders in telemedicine services in the US, estimated that the average online appointment costs insurance companies about US$45, while it is US$120 to US$125 for a typical office visit, or even US$175 for an urgent care visit.29 Overall, Teladoc-funded studies show each telemedicine service introduces an average of US$472 in saving. Another study showed that the cost savings with access to telehealth visits were between US$19 and US$121, driven mainly by the forgone ER visits.30 Surely, telemedicine will eventually become attractive to a greater number of patients. Consider that the average in-office visit takes 121 minutes, including 101 minutes of commute and waiting room time – and only 20 minutes with the doctor. In the meantime, a medical office in Las Vegas clocked video visits at 5 minutes of wait time and 8–10 minutes seeing the doctor.31 Summing up, we can assume digital transformation has the potential to revolutionize healthcare and accelerate the mindset shift towards individual sovereignty and a holistic mindset. Not only patients but also payers and clinicians need to understand that moving patients into the digital world will require the greater reliance on a patient’s independence. Until now, the patient was actively searching for contact with clinical experts and giving the clinician a description of his symptoms. In the digital era, a mobile app will answer his question; it will give the diagnosis. It will only happen with access to data. If a patient will give

How to change? 51

access to his daily live symptom monitoring to all algorithms connected to his wearable, sensors and phone, a diagnosis will be automatically generated by the algorithm. For healthcare, it means a clear shift with asymmetric information switched from clinician to patient. The role of clinicians as key decision-makers will change into more of consultants. Moreover, patient outcomes would directly improve due to health professionals’ access to complete and accurate health information. This could be the future after the mindset shift with the new axioms of individual sovereignty and holistic health. Digital technologies require data, i.e., patient-level data, to develop data-driven products. As such, patients will become more and more important, not only as recipients of health technology, but also as data providers for digital solutions development.

3.5  How the digital revolution will accelerate the mindset shift Despite numerous promising developments in the pandemic era with the digital healthcare transformation, today we are still not yet fully able to embrace technology and the opportunities that await us. The available data on how much we rely on digital technologies indicate that there is room to grow. Only a fraction of enterprises use advanced digital technologies (14% Big Data, 25% artificial intelligence [AI] and 26% cloud) across the EU according to the 2021  Digital Economy and Society Index  (DESI). 32 Among those who were seeking information on Internet, the group that makes an appointment with a practitioner via a website about health (an injury, disease, nutrition, improving health, etc.) doubled between 2003 and 2021. Among those using the Internet in the last three months, 60% searched for such information in 2021 across the EU setting. 33 Still, it must be emphasized that only one in five EU citizens used the Internet to make an appointment with a practitioner in 2021. 34 About 80% of data processing and analysis that takes place in the cloud occurs in data centres and centralized computing facilities, and 20% in smart connected objects, such as cars, home appliances or manufacturing robots, and in computing facilities close to the user (“edge computing”). By 2025, these proportions are set to change markedly. 35 Among Europe’s 2030 goals is the objective to extend access to Electronic Health Records (EHR) to all EU citizens. 36 Obviously, to make it happen, interoperability has to be introduced in much greater way. The investment into the interoperability of unified electronic health record (EHR)/exchange (and e-prescribing) and funding the necessary adaptation of the physical/digital infrastructure would lead to savings. According to McKinsey, thanks to data sharing, up to 120 billion EUR can be saved within the EU’s health sector annually. 37 My mom, at the time of writing this book, was 76 years old … she uses WhatsApp to receive messages and phone calls, but sending photos continues to be an issue no matter how many times I have tried to explain it. She loves her independence and is extremely healthy, but she does realize she needs me … for WhatsApp communication.

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The greatest challenge ahead is the fact that only a bit more than half of EU citizens (56%) have basic digital skills.38 The European Commission’s vision of Europe’s Digital Decade set a target for 80% of the population to acquire basic digital skills by 2030. It is along another ambitious objective of 20 million Information and Communication Technologies (ICT) specialists by 2030. So this is actually how the digital revolution will accelerate the mindset shift – through improved digital literacy and digital skills. But what do we mean by digital literacy? A very inspirational initiative is the European Digital Competence Framework (DigComp), developed by the Human Capital and Employment Unit (Joint Research Centre) on behalf of the Directorate General for Employment, Social Affairs and Inclusion of the European Commission. The DigComp framework covers multiple areas, including the ability to browse, search and evaluate digital content. It also discusses how to communicate with and engage citizens as well as collaborate and manage digital content creation. So far, there have been at least 30 different case studies and 20 tools adopting the DigComp framework, but these are limited to the field of Education and Training as well as Life-long Learning and Inclusion and Employment. So how shall we measure digital literacy in healthcare? The problem is that we do not even know how to measure digital literacy in the healthcare area. The instruments are scarce. The very first was an eight-item measure of eHealth literacy developed to measure consumers’ combined knowledge, comfort and perceived skills at finding, evaluating and applying electronic health information to health problems.39 It managed to be adopted to eight different languages across 26 countries, but it still does not capture the recent development in the digital revolution era.40 In a recent systematic literature review focusing on measuring the digital literacy of older adults, out of 27 selected articles, more than half (16/27, 59%) used eHEALS (8-item measure of eHealth literacy). Across main eight instruments identified, only one or two of the DigComp framework’s elements were actually measured; only the Mobile Device Proficiency Questionnaire (MDPQ) covered all five elements, including “digital content creation” and “safety.”

3.6 How can the digital revolution change the mindset to individual sovereignty? Even though we know how to measure digital literacy it is still important to learn how to improve it. This is an important moment to focus more on individual sovereignty. The journey towards individual sovereignty in the digital era is not only about teaching the older generation how to use digital technologies. The story is a bit more complex. Why? Some researchers believe that the association between age and digital capacity is a too far-fetched simplification and not sufficient to explain the digital divide.41,42 In the Netherlands, for example, “digital natives” are perhaps even

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nonexistent and other factors like life stages and socialization are considered to be more impactful on the development of digital literacy than age itself. Also, in a German study, perceptions of threat due to technologization were perceived as the main predictors of digital capacity, rather than age itself. I believe the mindset revolution will simply require motivation. With motivation, the empowerment by cognitive trust can be achieved and the technical skills subsequently generated. Clinicians received our blind trust as they were equipped with their medical knowledge and supported by the well-established system. It does not matter whether we like the system or not, but it has been there since we were born for the majority of us. A paradigm shift to a new healthcare system will require the development of trust. It is the only fertilizer in the soil of the new healthcare system that will enable us to grow any fruit. The digital revolution can make that soil more fruitful, but trust is needed. Cognitive trust is needed for the mindset shift. I elaborated earlier on Kant’s theory of how to introduce individual sovereignty with awareness and educational efforts. Both are linked with cognitive trust. The development of which can not only be successfully achieved in the digital era but even accelerated. Let’s go back for a moment to the mode of crisis. Vaccine hesitancy has shown us very clearly that lack of trust towards safety can truly lead to resistance in use. The same unfortunately applies to digital solutions. Thematic analysis of newspaper articles referring to remote GP consultations from two time periods: 2 March–31 May 2020 (period 1–19 articles) and 30 July–12 August 2020 (period 2–17 articles).43 Period 1 depicted it positively, equating digital change with progress and linking novel technological solutions with improved efficiency and safety (e.g., infection control) in a service that was overdue for modernization. Articles in period 2 questioned the persistence of a remote-first service now that the pandemic was waning, emphasizing, for example, missed diagnoses, challenges to the therapeutic relationship, and digital inequalities. The fears related to digital solutions are pretty much linked with fears related to data sharing, too. One example is a new law in the UK General Practice Data for Planning and Research (GPDPR) that was planned to be introduced in July 2021 as replacement for GP Extraction Service (GPES) but became delayed until an unknown date. NHS Digital was to collect data on treatments, referrals, and appointments over the past ten years, alongside other data from medical records data for a patient’s entire history. In contrast to earlier framework, GPDPR was supposed to ensure a national database with some additional data collected about sexual orientation and other data points. Access to data for research purposes was supposed to be granted. However, the lack of reassurance that the data would be securely protected and would only be made available for the purposes of healthcare planning and research caused the project to be postponed after the public debate could be first commenced. The British Medical Association and the Royal College of General Practitioners argued that not enough people knew about the data transfer or that they could opt out of pseudonymized data.

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The NHS joined forces with Palantir, along with Microsoft and Amazon, last year to develop a data platform to help inform the COVID-19 response. The move triggered concerns from privacy activists (https://www.bloomberg.com/ news/articles/2022-11-29/nhs-deal-with-palantir-draws-legal-threat-frompatient-groups?utm_source=website&utm_medium=share&utm_campaign= copy accessed 26 June 2023). The lawsuit claimed that NHS England failed to consider the impact on patients and the public. “What is of fundamental importance is the transparency of this process to ensure that trust is not eroded as we move to a far more dynamic relationship between health and care systems and citizens,” said Dr Charles Alessi, chief clinical officer at Healthcare Information and Management Systems Society (HIMSS).44 We should not be surprised by such a resistance. Let’s remind ourselves that by assuming the notion of market failure, we further assume that citizens are not able to make their own decisions due to information asymmetry. Hence, the role of the patient was limited to the blind trust in clinicians and showing their full compliance with clinicians’ recommendations. With new developments leading to a growing reliance on patient data and greater patient engagement in decision-making, such resistance is an expected reaction. First, it was Kuhn who mentioned that crisis is the normal phase, i.e., the prelude to the revolution. I am still hopeful that the crisis that is the mistrust of governments and technologies will bring eventually the rebirth of individual sovereignty and a paradigm shift towards a new healthcare system. Surely, the greater engagement of patients in clinical decision-making processes may facilitate that process. In recent decades, some changes have been made to strengthen the role of the general public in the allocation of limited healthcare resources. We are still at the very beginning of our journey. On the European Medicine Agency (EMA) website, we can learn that it collaborates with 35 patient advocacy groups (Nov. 2021) but details are not available.45 Again why not more than 35? Let me kindly remind ourselves that there are 72,616 different international classification of diseases (ICDs) and not less than 3800 procedure codes. Looking for more inspiring examples, there are numerous initiatives driven by the US Food and Drug Administration (FDA). For example, in 2020, the FDA kicked off the project “Patient Voice” to collect patient-reported outcomes from oncological clinical trials in a systematic manner. The goal was to gain more insight into the patient’s experiences with treatment with a specific focus on side effects. The objective is to identify specific information that is important to the patients, ensure they are to be collected routinely by companies in future clinical trials and establish methods on how to display them in a systematic manner.46 There are some examples of patients’ power being the driver of the decision-making process, mainly the unmet medical needs that are actually the key factor for Alzheimer treatment.47 Such examples indicate that the voice of patients is not incorporated early enough in a structured way, but in the situation of lack of public funding contrasting unmet medical needs, it transpires and affects stability and trust towards the system. A review of 60 submissions for orphan drugs in both the

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US and Europe revealed that in less than 30% were cases patient-reported outcomes mentioned in regulatory indication for uses such as Summaries of Product Characteristics (SmPCs).48 It is a bit better when it comes to the oncological drugs. Oncology drugs and indications approved between 2012 and 2016 by both the FDA and the EMA were reviewed.49 Patient Reported Outcomes (PRO) data were utilized in the decision-making processes in 70.3% and 46.7% cases, respectively. Why not 100%? If we assess these numbers from the perspective of patients being the customers, we should expect not less than 100%. So why not yet?

3.6.1  How can the digital revolution introduce more cognitive trust? Digital transformation has finally influenced some legal developments, particularly in relation to information disclosure and best interests of the allocation of limited healthcare resources. The WHO explicitly states that “civil society can play a crucial role in holding governments and companies accountable for the deployment and operation of digital proximity tracking technologies.”50 “Empowerment through Digital Health” is one of two flagships in the European Programme of Work 2020–2025 presented by the WHO Regional Office for Europe. It is noted there the importance of “providing technical and policy guidance and expertise on the safety and efficacy of digital health solutions.”51 The other flagship initiative is the Mental Health Coalition, whose importance I explained earlier. The explicit consent introduced by the GDPR in the EU has led to a new chapter of debate about patient independence and the importance in decision-making.52 “The data subject shall have the right to receive the personal data concerning him or her, which he or she has provided to a controller, in a structured, commonly used and machine-readable format and have the right to transmit those data to another controller without hindrance from the controller to which the personal data have been provided.”53 This brings us to another development of the mindset shift fostering the cognitive trust; apart from the one expressed in the principle of informed consent (GDPR again), there is also an increasing tendency for patients to take charge by asking for second opinions, forming interest associations and actively searching for knowledge concerning their condition. In 2020, I had the great pleasure to visit Thallin e-Estonia centre when I was presented with the Estonian model of shared decision-making in healthcare. For those who are unfamiliar with Estonia, let me bring to your attention that according to an online survey across 322 eHealth professionals in Europe in 2021, 23% of respondents believe Estonia is the leading country for eHealth innovation, followed by Denmark, which received 14% of responses.54 In essence, thanks to the digital accessibility of the healthcare system, a patient can not only see his lab tests and order a prescription, but also ask for a second opinion with an option of hiding the primary opinion given by first doctor. Shared decision-making is getting more and more attention for its process of

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“patient choice with professional control in the selection of treatment options.”55 Historically, the doctor-patients relationship was based on non-cognitive trust. Growing access to information with limited access to healthcare professionals introduces opportunities for cognitive trust, which emphasizes the significance of knowledge and control. The understanding of different patients’ ability to acquire and translate the information for their own benefits is important. Emma Cave deliberated about it in the context of NHS treatment options in England and Wales. She distinguished across three groups of patients with capacity, who lack capacity but whose past or present wishes are ascertainable, and patients who lack capacity and whose wishes cannot be determined. We need to acknowledge that we currently await The European health data space  regulation to be announced in early 2022. Among other objectives, it highlights the importance of citizen control over their health data. Codes of Conduct for secondary health data use, governance, infrastructure, data quality and data solidarity and empowering citizens with regards to secondary health data use in the EU56 promote a safe exchange of patient data (including when they travel abroad) and citizens’ control over their health data: • • • • •

Support research on treatments, medicines, medical devices and outcomes Encourage the access to and use of health data for research, policymaking Regulation, with a trusted governance framework and upholding dataprotection rules Support digital health services Clarify the safety and liability of artificial intelligence in health

Generally speaking, greater recognition of patients’ ownership of data will hopefully lead to more engagement in the decision-making process. Higher engagement will subsequently translate into the greater trust. There is some initiative underway that follows that logic. For example, the MyData Global initiative has more than 100 organization members and close to 400 individual members from over 40 countries on 6 continents. Its goal is “to empower individuals with their personal data, thus helping them and their communities develop knowledge, make informed decisions, and interact more consciously and efficiently with each other as well as with organisations.”57 This brings us to another important recent development that contributes to the paradigm shift: the patients’ active participation in the development of health solutions based on the medically relevant data collected through new mobile technologies and smart appliances. Further, citizens play a crucial role in making their data available for research. For this reason, we need a trustworthy framework to enable people to aggregate, integrate, and analyse data from different sources. Health data cooperatives such as MIDATA provide such a framework.58 This type of group empowers patients to aggregate data about themselves and to control access to them. ETH Zurich and the Bern University of Applied Sciences developed MIDATA, which has been operating since 2016, serving as a platform

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for different research projects of such kind. It is important to bring to the attention the White Paper “On Artificial Intelligence – A European Approach to Excellence and Trust,” published by the European Commission in February 2020. It laid the ground work for a regulatory and investment-oriented approach to promoting the uptake of AI while addressing the risks associated with certain uses of this new technology.59 It is another important voice in the discussion about the growing role of patients in creating a new healthcare system of digital era.

3.7 How can the digital revolution change the mindset to holistic health? There are still a few points to make with respect to the holistic perspective of the healthcare system. In an earlier chapter, I mentioned consequentialism and multifactorial design of value assessment to ensure a holistic perspective. How can digital transformation accelerate that? So far, there are limited efforts to incorporate digital solutions into the current organization of healthcare systems. One of the factors that differentiates digital solutions from other health technologies is less stringent entry criteria. It does facilitate faster market access and also direct marketing to patients, which is forbidden in many jurisdictions such as the EU for medicines for patients. It is another component of why the digital transformation brings patient empowerment. It also carries enormous risks that should be mitigated when it comes to safety. A review of 47 papers that focused on the accuracy of the data collected by consumer wearable health devices (CWHDs) listed what kind of aspects should be considered in that assessment60: (i) tracker and sensor type, (ii) the algorithm used in the CWHD and (iii) the limitation in the design, energy consumption and the processing capability of the device. Interestingly, nothing of these factors has ever been considered for regulatory purposes or pricing and reimbursement for health technologies. That brings us to another element I consider important in the mindset shift towards greater individual sovereignty and holistic health. That is, the new healthcare system in the digital era needs to address non-health-related patients’ needs. This is a healthcare system not limited to a doctor’s office, hospital or pharmacy. The digital transformation changes the definition of the healthcare system from an office focus to a patient focus in the home or online settings. It is the healthcare system that is part of everything we do, as our behaviour determines prevention and data will tell us whether we are doing a good job. Hence, we need to introduce both health and nonhealth-related criteria into the system of the assessment of digital technologies. It is not only about unmet medical needs anymore. It is also about functionality and usability as well as acceptance to ensure the adoption of health solutions in the situation of non-urgent health needs. There is already some research available that can be helpful in identifying additional non-health-related value attributes in the assessment of digital solutions. Two examples are the Technology Acceptance Model or Unified Theory of Acceptance and Use of Technology. The adoption of these models sheds a new

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light on key success factors for the implementation of wearables in clinical practice. A questionnaire study across 288 medical and general public representatives highlighted the impact of a good user experience and technology on the utilization of wearable devices.61 In other words, the healthcare system, success does not depend anymore on molecule efficacy and safety but on patients’ acceptance from a usability standpoint. Patients are not just sick individuals anymore, they are the end user of a given technology. We must therefore understand customer needs. It can happen when we apply a holistic approach. This idea is in line with the Dutch Rathenau Instituut, which advocates for “an integrated approach to innovation … that gives shape and direction to the digital transition and as a result to our society, from the viewpoint of public values.”62 In a similar tone, one can read the Montreal Declaration for Responsible Development of Artificial Intelligence launched by University de Montreal and the  Fonds de Recherché du Quebec in December 2018. Based on the input of  100 ethics and technology specialists, the declaration highlights potential non-health-related needs of patients that should be taken into consideration: (1) an organization for independent citizen scrutiny and consultation, (2) artifical intelligence system (SIA) audit and certification policy, (3) empowerment and automation, (4) education and ethics, (5) inclusive development of AI, (6) protection of democracy, (7) international development of AI and (8) environmental footprint. With respect to the autonomy principle, it makes strong reference to the empowerment of citizens and the fostering of literacy and critical thinking. With the solidarity principle, it states that “the development of artificialintelligence systems must be compatible with maintaining the bonds of solidarity among people and generations.” There is more and more positive news with the adoption of digital solutions. For example, a survey of more than 800 participants conducted by The North West London (NWL) Collaboration of Clinical Commissioning Groups (CCGs) and the Healthy London Partnership (HLP) in 2017 revealed positive findings.63 People are not motivated to use digital health services until they are ill themselves; however, people that are already using digital health services overwhelmingly value these services for any future use. It makes it clear that digital solutions can actually become the virtual connectors of different diseases to ensure a holistic approach to individual health and well-being. Additionally, it was found that some people may be uncomfortable about losing the face-to-face relationship with their clinicians due to simply non-health-related but cultural reasons. In Switzerland, there is an initiative called “Swiss Personalized Health Network” (SPHN) of the Swiss Federal Government, namely the State Secretariat for Education, Research and Innovation (SERI) and the Federal Office of Public Health (FOPH). A total of CHF 68 million was allocated to the initiative for the period 2017–2020, and 66.9 million for the period 2021–2024.64 It is forecast to return a robust return on investment. It is a national initiative with 33 different Swiss organizations from hospitals to academic institutions and patients’ organizations taking part under the leadership of the Swiss Academy of Medical

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Sciences (SAMS), in collaboration with the Swiss Institute of Bioinformatics (SIB). Its scope discusses a holistic perspective on using health data. The overarching objective is to contribute to the development, implementation and validation of coordinated data infrastructures in order to make health-relevant data interoperable and shareable for research in Switzerland. There are already multiple objectives pursued, which gives a better understanding of how healthcare system organization can be changed in a data-driven ecosystem far from the perspective of health maximization alone. Research ideas indicate new objectives including digital biomarkers identifications, digital solutions developments, quality and efficiency improvements (real-world data use to identify compliance of clinical practice with clinical guidelines) or data-based hypothesis generation for new treatments. An example of the project using available data is highdimensional single-cell cytometry combined with computer-aided analysis that provides the most innovative technology platform to identify reliable biomarkers and novel therapeutic targets.65 Interviews with 48 expert stakeholders from Switzerland that have been working principally with health databases indicated that the preferences of the society in which they are active, their concerns and their priorities should be taken into consideration while developing the health data ecosystem.66 These examples of holistic inclusion in decision-making can be complemented by recent experiences with the launch of mobile applications to the fight against COVID-19.67 The recent example from Netherlands justifies how national action was taken to develop a COVID-tracking mobile app. The Supervisory Committee Digital Support was established to advise the Ministry of Health, Welfare and Sport. In a holistic manner, the Committee consisted of about 15 experts from the fields of, i.e., epidemiology, virology, technology, privacy and security. Their advice is partly based on proposals from the Taskforce Digital Support against COVID-19 and the Taskforce Behavioural Sciences.68 An expert panel chaired by Twente University conducted an ethical analysis of this app based on the following values: (1) voluntariness, (2) effectiveness, (3) privacy, (4) fairness, (5) inclusion, (6) procedural fairness, (7) responsibility, (8) preventing improper use, (9) safeguarding civil liberties and (10) necessity and proportionality. After several of rounds of revisions specifically focused on anti-abuse provisions, the Dutch Parliament approved the CoronaMelder app in early September 2020. Such criteria are very much in line with the Mobile Device Proficiency Questionnaire (MDPQ), which is one of other similar instruments potentially adopted for the value assessment of digital solutions. MDPQ attempts to measure older adults’ digital content creation capacity (developing, integrating and re-elaborating digital content; copyright; licenses; programming),69 which can give valuable information regarding an individual’s ability to add value to new media for self-expression and knowledge creation.70 In summary, the focus of this chapter is to illustrate how the digital transformation positions patients at the centre of healthcare. The digital era introduced many new data-driven products such as IoT for health monitoring, new types

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of data available for decision-making such as behavioural data, new data-driven technologies such as virtual reality allowing for distance treatment. Eventually each individual will have his or her own new health ecosystem scaled not by geographical settings but by health needs. The paradigm shift towards a patientcentric digital healthcare system can be defined by two major components. First, it is about healthcare services offered directly to patients and health technologies that are developed based on patient data and constantly improved by patient data. As a consequence, the patient becomes not only the sole customer but also data provider. That mindset shift will happen thanks to the digital transformation that will not only help digital technologies to grow but also patients to be equipped with tools to prevent and treat in an empowering fashion. Second, it is about the healthcare system’s changed definition from officebased to “everywhere” based. The digital technologies used by patients give an opportunity to redefine the healthcare system to a new virtual ecosystem. Non-health determinants will become important drivers of the new healthcare system, bringing the value of patient preferences in the way he or she defines her/his health journey. As the essential piece to ensure that this mindset shift happens smoothly in the digital transformation, we need to give patients the power of decision-making from legal standpoint. This is the missing part that has to be introduced to fully capture the advent of digital transformation. I have given a couple of examples of regulatory changes and initiatives that provide a good direction in that respect. At the end of the day, the patient empowerment of the digital era gives us the opportunity to move us away from a reactive mode of treatment into proactive mode of prevention as a new direction of the healthcare system. This would have potential to immediately translate into cost savings if the healthcare system organization is modified with support of such technologies, such as wearables and mobile applications. The mindset shift should be the objective for us all.

NOTES 1 Kuhn. “The Structure of Scientific Revolutions,” The University of Chicago Press, Chicago and London, 4th ed., 2012. 2 “OECD Family Database: CO1.2: Life Expectancy at Birth,” OECD, https://www. oecd.org/els/family/CO_1_2_Life_expectancy_at_birth.pdf 3 “Ageing and Health,” World Health Organization, 4 Oct. 2021, https://www.who. int/news-room/fact-sheets/detail/ageing-and-health 4 “Patient Engagement: Technical Series on Safer Primary Care,” World Health Organization, 2016, https://apps.who.int/iris/bitstream/handle/10665/252269/978 9241511629-eng.pdf 5 Cerezo, et al. “Concepts and Measures of Patient Empowerment: A Comprehensive Review,” Revista da Escola de Enfermagem da U S P, vol. 50, no. 4, 2016, pp. 667–674. https://doi.org/10.1590/S0080-623420160000500018 6 “The key aims of our review were as follows: to identify measures of patient empowerment that have been developed and psychometrically tested; to assess the quality of existing patient empowerment measures; and finally to describe the conceptual domains captured by existing measures of patient empowerment.”

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7 Barr, et al. “Assessment of Patient Empowerment – A Systematic Review of Measures,” PloS One, vol. 10, no. 5, 13 May 2015, p. e0126553. https://doi.org/10.1371/journal. pone.0126553; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4430483/ 8 Laursen, et al. “Assessment of Short and Long-Term Outcomes of Diabetes Patient Education Using the Health Education Impact Questionnaire (HeiQ),” BMC Research Notes, 2017, pp. 2–9. https://doi.10.1186/s13104-017-25366 9 International Patient Decision Aid Standards (IPDAS) Collaboration, http://ipdas. ohri.ca/index.html 10 Campbell. “Kant, Autonomy and Bioethics,” Ethics, Medicine and Public Health, vol. 3, no. 3, July–Sept. 2017, pp. 381–392. https://www.researchgate.net/publication/3190 24244_Kant_autonomy_and_bioethics 11 Coggon, Miola. “Autonomy, Liberty, and Medical Decision-Making,” The Cambridge Law Journal, vol. 70, 2011, pp. 523–547. https://doi.org/10.1017/S0008197311000845 12 Kant. What Is Enlightenment? Columbia University in the City of New York, http:// www.columbia.edu/acis/ets/CCREAD/etscc/kant.html 13 Sunstein. Why Nudge? The Politics of Libertarian Paternalism. Yale University Press, 2014. 14 Jackson. What’s Epistemic about Epistemic Paternalism? Routledge, 2021, https:// philpapers.org/archive/JACWEA.pdf 15 “Aristotle & Happiness,” https://www.pursuit-of-happiness.org/history-of-happiness/ aristotle/ 16 Gatchpazian. Eudaimonia: Definition, Meaning, & Examples. Berkeley Well-Being Institute, https://www.berkeleywellbeing.com/eudaimonia.html 17 Marseille, Kahn. “Utilitarianism and the Ethical Foundations of Cost-Effectiveness Analysis in Resource Allocation for Global Health,” Philosophy, Ethics, and Humanities in Medicine, vol. 14, 2019, https://doi.org/10.1186/s13010-019-0074-7 18 Secher, et al. “Ten-Year Follow-up of the OPUS Specialized Early Intervention Trial for Patients with a First Episode of Psychosis,” Schizophrenia Bulletin, vol. 41, May 2015, pp. 617–626, https://doi.org/10.1093/schbul/sbu155 19 Ursenbach, et al. “Evidence for Measurement Bias of the Short Form Health Survey Based on Sex and Metropolitan Influence Zone in a Secondary Care Population,” Health and Quality Life Outcomes, vol. 18, 3 Apr. 2020, https://doi.org/10.1186/ s12955-020-01318-y 20 Posselt, et al. “The Danish OPUS Early Intervention Services for First-Episode Psychosis: A Phase 4 Prospective Cohort Study with Comparison of Randomized Trial and Real-World Data,” The American Journal of Psychiatry, 28 July 2021, https://doi. org/10.1176/appi.ajp.2021.20111596 21 “Digitalisation Triggers Patenting Growth,” European Patent Office, 2020, https:// www.epo.org/about-us/annual-reports-statistics/statistics/2019/digitalisationtriggers-patenting-growth.html 22 “2018: A Year of Growth,” Annual Report 2018, European Patent Office, https:// www.epo.org/about-us/annual-reports-statistics/annual-report/2018.html 23 “Shaping the Digital Transformation in Europe,” European Commission DG, Communications Networks, Content & Technology, Sept. 2020, https://digital-strategy. ec.europa.eu/en/news/commission-publishes-analysis-macro-economic-potentialdigital-transformation-independent 24 Hasan. “State of IoT 2022: Number of Connected IoT Devices Growing 18% to 14.4 Billion Globally,” IOT Analytics, 18 May 2022, https://iot-analytics.com/numberconnected-iot-devices/ 25 Petrov. “49 Stunning Internet of Things Statistics 2022 [The Rise of IoT],” TechJury, 2 June 2022, https://techjury.net/blog/internet-of-things-statistics/#gref 26 “Shaping the Digital Transformation in Europe,” European Commission, 22 Sept. 2020. 27 “Just the Facts: 30 Telehealth Statistics for Doctors to Know,” OrthoLive, 7 June 2018, https://www.ortholive.com/blog/just-the-facts-30-telehealth-statisticsfor-doctors-to-know/

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28 Abassi. “Virtual Doctor's Visits: The Promises of Telemedicine,” American Council of Science and Health, 18 Jan. 2016, https://www.acsh.org/news/2016/01/18/virtualdoctors-visits-the-promises-of-telemedicine 29 Bell. “Can Telemedicine Be Both Cost Efficient and High Quality?” US News & World Report, 27 Feb. 2018, https://www.usnews.com/news/healthcare-of-tomorrow/ articles/2018-02-27/can-telemedicine-be-both-cost-efficient-and-high-quality 30 Charleson. “Telehealth Statistics and Telemedicine Trends 2022,” The Checkup by Single Care, 20 Jan. 2022, https://www.singlecare.com/blog/news/telehealthstatistics/ 31 Ashford. “1 In 5 People Would Switch Doctors for Video Visits,” Forbes, 30 Jan. 2017, https://www.forbes.com/sites/kateashford/2017/01/30/videodoctor/?sh=2b5a062d10ce 32 “Digital Economy and Society Index 2021: Overall Progress in Digital Transition but Need for New EU-wide Efforts,” European Commission, 12 Nov. 2021, https:// ec.europa.eu/commission/presscorner/detail/en/ip_21_5481 33 “The Evolution of an Indicator and Compare Countries,” Data Visualisation Tool – Data & Indicators, https://digital-agenda-data.eu/charts/see-the-evolution-ofan-indicator-and-compare-countries#chart={%22indicator-group%22:%22ehealth%22, % 2 2 i nd ic ator % 2 2:% 2 2 i _ i h i f % 2 2 ,% 2 2 bre a kdow n% 2 2:% 2 2 i nd _ tot a l% 2 2 , %22u n it-mea su re%22:%22pc _ i nd _ iu 3%22 ,%22ref-a rea%22:[%22 EU %22 , %22DK%22]} 34 “The Evolution of an Indicator and Compare Countries,” Data Visualisation Tool – Data & Indicators, https://digital-agenda-data.eu/charts/analyse-one-indicator-andcompare-breakdowns#chart={%22indicator-group%22:%22ehealth%22,%22indicator %22:%22i _ iu m app%22 ,%22brea kdow n-g roup%22:%22tot a l%22 ,%22u n itmeasure%22:%22pc_ind%22,%22time-period%22:%222012%22,%22ref-area%22: [%2 2 AT %2 2 ,%2 2 BE%2 2 ,%2 2 BG%2 2 ,%2 2 H R%2 2 ,%2 2C Y %2 2 , %22CZ%22,%22DK%22,%22EE%22,%22EU%22,%22FI%22,%22FR%22,%22DE% 22,%22EL%22,%22HU%22,%22IS%22,%22IE%22,%22IT%22,%22LV%22,%22LT %22,%22LU%22,%22MT%22,%22NL%22,%22NO%22,%22PL%22,%22PT%22,%2 2RO%22,%22SK%22,%22SI%22,%22ES%22,%22SE%22,%22UK%22]} 35 “A Roadmap for the Next-Generation IoT in Europe,” European Commission, 16 Mar. 2022. 36 “A Digital Health Decade: From Ambition to Action: 4 Pillars for a Trusted and Collaborative Health Data Space,” Digital Europe, 23 Nov. 2021, https://www. digitaleurope.org/resources/a-digital-health-decade-from-ambition-to-action/ 37 “Shaping the Digital Transformation In Europe,” European Commission DG, Communications Networks, Content & Technology, Sept. 2020, https://www.ospi.es/export/ sites/ospi/documents/documentos/Sstudy_Shaping_the_digital_transformation_ in_Europe_Final_report_202009.pdf 38 “Europe’s Digital Decade: Digital Targets for 2030,” European Commission, Apr. 2021, https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-age/ europes-digital-decade-digital-targets-2030_en 39 Norman, et al. “eHEALS: The eHealth Literacy Scale,” Journal of Medical Internet Research, vol. 8, 4 Nov. 2006, https://doi.org/10.2196/jmir.8.4.e27 40 https://www.jmir.org/2021/11/e30644 41 Schneider, et al. “Bridging the ‘Digital Divide’: A Comparison of Use and Effectiveness of an Online Intervention for Depression between Baby Boomers and Millennials,” Journal of Medical Internet Research, Nov. 2006 pp. 236, 243–251, https://doi. org/10.2196/jmir.8.4.e27 42 Loos. “Senior Citizens: Digital Immigrants in Their Own Country?” Observatorio, 2012, pp. 1–23, https://www.researchgate.net/publication/236631337_Senior_citizens_ Digital_immigrants_in_their_own_country 43 Mroz, et al. “Changing Media Depictions of Remote Consulting in COVID-19: Analysis of UK Newspapers,” British Journal of General Practice, vol. 71, no. 702, Jan. 2021, https://doi.org/10.3399/BJGP.2020.0967

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44 Mageit. “Software Giant Palantir Made £22M Profit after NHS Data Deal,” HealthCare IT News, 25 Aug. 2021, https://www.healthcareitnews.com/news/emea/software-giantpalantir-made-22m-profit-after-nhs-data-deal 45 “Eligible Patients and Consumers Organisations,” European Medicines Agency, https:// www.ema.europa.eu/en/partners-networks/patients-consumers/eligible-patientsconsumers-organisations 46 “Project Patient Voice,” FDA, 15 Feb. 2022, https://www.fda.gov/about-fda/oncologycenter-excellence/project-patient-voice 47 McGinley. “FDA Releases Fresh Details on Internal Debate Over Controversial Alzheimer’s Drug,” The Washington Post, 22 June 2021, https://www.washingtonpost. com/health/2021/06/22/aducanumab-aduhelm-alzheimers-drug-controversy-/ 48 Jarosławski et al. “Patient-Reported Outcome Claims in European and United States Orphan Drug Approvals,” Journal of Market Access & Health Policy, vol. 6, 7 Nov. 2018, https://doi.org/10.1080/20016689.2018.1542920 49. Gnanasakthy et al. “A Review of Patient-Reported Outcomes Labeling for Oncology Drugs Approved by the FDA and the EMA (2012-2016),” Value Health, vol. 22, no. 2, Feb. 2019, pp. 203-209, https://doi.org/10.1016/j.jval.2018.09.2842 50 “Ethical Considerations to Guide the Use of Digital Proximity Tracking Technologies for COVID-19 Contact Tracing: Interim Guidance,” World Health Organization, 28 May 2020, https://apps.who.int/iris/ handle/10665/332200 51 The European Programme of Work, 2020–2025: United Action for Better Health. Copenhagen: WHO Regional Office for Europe, 2021. Licence: CC BY-NC-SA 3.0 IGO. 52 “General Data Protection Regulation: Art. 20 GDPR Right to Data Portability,” Intersoft Consulting, https://gdpr-info.eu/art-20-gdpr/ 53 Kolasa, et al. “Future of Data Analytics in the Era of the General Data Protection Regulation in Europe,” PharmacoEconomics, vol. 38, 8 June 2020, pp. 1021–1029, https://doi.org/10.1007/s40273-020-00927-1 54 “Which Country Do You Consider Being a Role Model for eHealth Innovation in Europe?” Statista 2021, https://www.statista.com/statistics/1010663/countriesperceived-to-be-a-role-model-for-ehealth-in-europe/ 55 Cave. “Selecting Treatment Options and Choosing between Them: Delineating Patient and Professional Autonomy in Shared Decision-Making,” Health Care Analysis, vol. 28, 21 Sept. 2020, https://doi.org/10.1007/s10728-019-00384-8 56 “Digital Health 2020 – EU on the Move,” Federal Ministry of Health, 2021, https:// www.bundesgesundheitsministerium.de/en/eu2020/en/topics-and-documents/ digital-health-2020-en.html 57 “1319 Organisations and Individuals Have Committed to the MyData Principles for Ethical Personal Data Management,” MyData, https://mydata.org/declaration/ 58 “My Data – Our Health,” Midata, https://www.midata.coop/en/home/ 59 “White Paper on Artificial Intelligence: A European approach to excellent and trust,” European Commission, 10 Feb. 2020, https://ec.europa.eu/info/sites/info/files/ commission-white-paper-artificial-intelligence-feb2020_en.pdf 60 Mahloko, Adebesin. “A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data,” Responsible Design, Implementation and Use of Information and Communication Technology. I3E 2020. Lecture Notes in Computer Science, vol. 12067, Springer, https://doi.org/10.1007/978-3-030-45002-1_9 61 Lee, Lee. “Healthcare Wearable Devices: An Analysis of Key Factors for Continuous Use Intention,” Service Business, vol. 14, 15 Oct. 2020, pp. 503–531, https://doi.org/ 10.1007/s11628-020-00428-3 62 Kool, Dujso. “Directed Digitalisation Working towards a Digital Transition Focused on People and Values – The Dutch Approach,” Rathenau Instituut, 2018, https://www.rath enau.nl/sites/default/files/2018-11/Directed%20Digitalisation.pdf (Rathenau Instituut is an organisation in the Netherlands for  technology assessment established by Dutch government. It is a member of the European Parliamentary Technology Assessment.)

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63 “Digital Attitudes Survey, North West London,” The Healthy London Partnership and the NHS North West London Collaboration of Clinical Commissioning Groups, Sept. 2017, https://www.healthylondon.org/wp-content/uploads/2017/11/ Digital-attitudes-survey-Sept-2017.pdf 64 “About SPHN,” SPHN, https://sphn.ch/organization/about-sphn/ 65 “Outcome of SPHN First Project Call: A Focus on SIB-led Projects,” Swiss Institute for Bioinformatics, 30 Nov. 2017, https://www.sib.swiss/about-sib/news/news2017/10232-outcome-of-sphn-first-project-call-a-focus-on-sib-led-projects 66 “Clinical Research: Patients and the Public Have Their Say,” Swiss National Science Foundation, 13 July 2021, https://www.snf.ch/en/7GJ99FdMjxpeGkc1/news/ clinical-research-patients-and-the-public-have-their-say 67 Gasser, et al. “Digital Tools against COVID-19: Taxonomy, Ethical Challenges, and Navigation Aid,” The Lancet Digital Health, 2020, pp. 425–434, https://doi.org/10.1016/ S2589-7500(20)30137-0 68 “Data Driven Healthcare and The Digitalisation of Society – the Stakes for Public Health,” European Public Health Alliance, Dec. 2020, https://epha.org/wp-content/ uploads/2021/02/ref lection-paper-data-driven-healthcare-developments-andthe-digitalisation-of-society.pdf 69 Roque, et al. “A New Tool for Assessing Mobile Device Proficiency in Older Adults: The Mobile Device Proficiency Questionnaire,” Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, vol. 37, Feb. 2018, pp. 131–156, https:// doi.org/10.1177/0733464816642582; https://pubmed.ncbi.nlm.nih.gov/27255686/ 70 Soyeon, et al. “Measurement of Digital Literacy among Older Adults: Systematic Review,” Journal of Medical Internet Research, Feb. 2021, https://www.jmir.org/2021/2/ e26145/PDF

BIBLIOGRAPHY “1319 Organisations and Individuals Have Committed to the Mydata Principles for Ethical Personal Data Management.” MyData, https://mydata.org/declaration/ “2018: A Year of Growth.” Annual Report 2018, European Patent Office, https://www. epo.org/about-us/annual-reports-statistics/annual-report/2018.html “A Digital Health Decade: From Ambition to Action: 4 Pillars for a Trusted and Collaborative Health Data Space.” Digital Europe, 23 Nov. 2021, https://www. digitaleurope.org/resources/a-digital-health-decade-from-ambition-to-action/ “A Roadmap for the Next-Generation IoT in Europe.” European Commission, 16 Mar. 2022. Abassi. “Virtual Doctor’s Visits: The Promises of Telemedicine.” American Council of Science and Health, 18 Jan. 2016, https://www.acsh.org/news/2016/01/18/virtualdoctors-visits-the-promises-of-telemedicine “About SPHN.” SPHN, https://sphn.ch/organization/about-sphn/ “Ageing and Health.” World Health Organization, 4 Oct. 2021, https://www.who.int/ news-room/fact-sheets/detail/ageing-and-health “Aristotle & Happiness.” https://www.pursuit-of-happiness.org/history-of-happiness/ aristotle/ Ashford. “1 In 5 People Would Switch Doctors for Video Visits.” Forbes, 30 Jan. 2017, https:// www.forbes.com/sites/kateashford/2017/01/30/videodoctor/?sh=2b5a062d10ce Barr, et al. “Assessment of Patient Empowerment – A Systematic Review of Measures.” PloS One, vol. 10, no. 5, 13 May 2015, p. e0126553. https://doi.org/10.1371/journal.pone. 0126553 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4430483 Bell. “Can Telemedicine Be Both Cost Efficient and High Quality?” US News & World Report, 27 Feb. 2018, https://www.usnews.com/news/healthcare-of-tomorrow/ articles/2018-02-27/can-telemedicine-be-both-cost-efficient-and-high-quality

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Campbell. “Kant, Autonomy and Bioethics.” Ethics, Medicine and Public Health, vol. 3, no. 3, July–Sept. 2017, pp. 381–392. https://www.researchgate.net/publication/ 319024244_Kant_autonomy_and_bioethics Cave. “Selecting Treatment Options and Choosing between Them: Delineating Patient and Professional Autonomy in Shared Decision-Making.” Health Care Analysis, vol. 28, 21 Sept. 2020, https://doi.org/10.1007/s10728-019-00384-8 Cerezo, et al. “Concepts and Measures of Patient Empowerment: A Comprehensive Review.” Revista Da Escola De Enfermagem Da U S P, vol. 50, no. 4, 2016, pp. 667–674. https://doi.org/10.1590/S0080-623420160000500018 Charleson. “Telehealth Statistics and Telemedicine Trends 2022.” The Checkup by Single Care, 20 Jan. 2022, https://www.singlecare.com/blog/news/telehealthstatistics/ “Clinical Research: Patients and the Public Have Their Say.” Swiss National Science Foundation, 13 July 2021, https://www.snf.ch/en/7GJ99FdMjxpeGkc1/news/clinicalresearch-patients-and-the-public-have-their-say Coggon, Miola. “Autonomy, Liberty, and Medical Decision-Making.” The Cambridge Law Journal, vol. 70, 2011, pp. 523–547. https://doi.org/10.1017/S0008197311000845 Collaboration of Clinical Commissioning Groups. Sept. 2017, https://www.healthylondon. org/wp-content/uploads/2017/11/Digital-attitudes-survey-Sept-2017.pdf “Data Driven Healthcare and the Digitalisation of Society – the Stakes for Public Health.” European Public Health Alliance, Dec. 2020, https://epha.org/wp-content/ uploads/2021/02/reflection-paper-data-driven-healthcare-developments-and-thedigitalisation-of-society.pdf “Digital Attitudes Survey, North West London.” The Healthy London Partnership and the NHS North West London. “Digital Economy and Society Index 2021: Overall Progress in Digital Transition but Need for New EU-Wide Efforts.” European Commission, 12 Nov. 2021, https:// ec.europa.eu/commission/presscorner/detail/en/ip_21_5481 “Digital Health 2020 – EU on the Move.” Federal Ministry of Health, 2021, https:// www.bundesgesundheitsministerium.de/en/eu2020/en/topics-and-documents/ digital-health-2020-en.html “Digitalisation Triggers Patenting Growth.” European Patent Office, 2020, https://www. epo.org/about-us/annual-reports-statistics/statistics/2019/digitalisation-triggerspatenting-growth.html “Eligible Patients and Consumers Organisations.” European Medicines Agency, https:// www.ema.europa.eu/en/partners-networks/patients-consumers/eligible-patientsconsumers-organisations “Europe’s Digital Decade: Digital Targets for 2030.” European Commission, Apr. 2021, https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-f it-digital-age/ europes-digital-decade-digital-targets-2030_en Gasser, et al. “Digital Tools against COVID-19: Taxonomy, Ethical Challenges, and Navigation Aid.” The Lancet Digital Health, 2020, pp. 425–434, https://doi.org/10.1016/ S2589-7500(20)30137-0 Gatchpazian. “Eudaimonia: Definition, Meaning, & Examples.” Berkeley Well-Being Institute. https://www.berkeleywellbeing.com/eudaimonia.html “General Data Protection Regulation: Art. 20 GDPR Right to Data Portability.” Intersoft Consulting, https://gdpr-info.eu/art-20-gdpr/ Hasan. “State of IoT 2022: Number of Connected IoT Devices Growing 18% to 14.4 Billion Globally.” IOT Analytics, 18 May 2022, https://iot-analytics.com/number-connectediot-devices/

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International Patient Decision Aid Standards (IPDAS) Collaboration. http://ipdas.ohri.ca/ index.html Jackson. What’s Epistemic about Epistemic Paternalism? Routledge, 2021. https://philpapers. org/archive/JACWEA.pdf Jarosławski, et al. “Patient-Reported Outcome Claims in European and United States Orphan Drug Approvals.” Journal of Market Access & Health Policy, vol. 6, 7 Nov. 2018, https://doi.org/10.1080/20016689.2018.1542920 “Just the Facts: 30 Telehealth Statistics for Doctors to Know.” OrthoLive, 7 June 2018, https:// www.ortholive.com/blog/just-the-facts-30-telehealth-statistics-for-doctors-toknow/ Kant. What Is Enlightenment? Columbia University in the City of New York, 1784. http:// www.columbia.edu/acis/ets/CCREAD/etscc/kant.html Kolasa, et al. “Future of Data Analytics in the Era of the General Data Protection Regulation in Europe.” PharmacoEconomics, vol. 38, 8 June 2020, pp. 1021–1029. https://doi.org/10.1007/s40273-020-00927-1 Kool, Dujso. “Directed Digitalisation Working towards a Digital Transition Focused on People and Values – The Dutch Approach.” Rathenau Instituut, 2018, https:// www.rathenau.nl/sites/default/f iles/2018-11/Directed%20Digitalisation.pdf (Rathenau Instituut is an organisation in the Netherlands for technology assessment established by Dutch government. It is a member of the European Parliamentary Technology Assessment.) Kuhn. The Structure of Scientific Revolutions. Chicago and London: The University of Chicago Press, 4th ed., 2012. Laursen, et al. “Assessment of Short and Long-Term Outcomes of Diabetes Patient Education Using the Health Education Impact Questionnaire (HeiQ).” BMC Research Notes, 2017, pp. 2–9. https://doi.10.1186/s13104-017-25366 Lee, Lee. “Healthcare Wearable Devices: An Analysis of Key Factors for Continuous Use Intention.” Service Business, vol. 14, 15 Oct. 2020, pp. 503–531. https://doi. org/10.1007/s11628-020-00428-3 Loos. “Senior Citizens: Digital Immigrants in Their Own Country?” Observatorio, 2012, pp. 1–23. https://www.researchgate.net/publication/236631337_Senior_citizens_ Digital_immigrants_in_their_own_country Mageit. “Software Giant Palantir Made £22M Profit after NHS Data Deal.” HealthCare IT News, 25 Aug. 2021, https://www.healthcareitnews.com/news/emea/softwaregiant-palantir-made-22m-profit-after-nhs-data-deal Mahloko, Adebesin. “A Systematic Literature Review of the Factors That Influence the Accuracy of Consumer Wearable Health Device Data.” Responsible Design, Implementation and Use of Information and Communication Technology. I3E 2020. Lecture Notes in Computer Science, vol. 12067, Springer. https://doi.org/10.1007/978-3-030-45002-1_9 Marseille, Kahn. “Utilitarianism and the Ethical Foundations of Cost-Effectiveness Analysis in Resource Allocation for Global Health.” Philosophy, Ethics, and Humanities in Medicine, vol. 14, 2019. https://doi.org/10.1186/s13010-019-0074-7 McGinley. “FDA Releases Fresh Details on Internal Debate over Controversial Alzheimer’s Drug.” The Washington Post, 22 June 2021, https://www.washingtonpost.com/health/2021/06/22/aducanumab-aduhelm-alzheimers-drugcontroversy-/ Mroz, et al. “Changing Media Depictions of Remote Consulting in COVID-19: Analysis of UK Newspapers.” British Journal of General Practice, vol. 71, no. 702, Jan. 2021, https://doi.org/10.3399/BJGP.2020.0967 “My Data – Our Health.” Midata, https://www.midata.coop/en/home/

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Norman, et al. “eHEALS: The eHealth Literacy Scale.” Journal of Medical Internet Research, vol. 8, 4 Nov. 2006. https://doi.org/10.2196/jmir.8.4.e27 “OECD Family Database: CO1.2: Life Expectancy at Birth.” OECD, https://www.oecd. org/els/family/CO_1_2_Life_expectancy_at_birth.pdf “Outcome of SPHN First Project Call: A Focus on SIB-led Projects.” Swiss Institute for Bioinformatics, 30 Nov. 2017, https://www.sib.swiss/about-sib/news/news-2017/ 10232-outcome-of-sphn-first-project-call-a-focus-on-sib-led-projects “Patient Engagement: Technical Series on Safer Primary Care.” World Health Organization, 2016. https://apps.who.int/iris/bitstream/handle/10665/252269/9789241511629-eng.pdf Petrov. “49 Stunning Internet of Things Statistics 2022 [The Rise of IoT].” TechJury, 2 June 2022. https://techjury.net/blog/internet-of-things-statistics/#gref “Project Patient Voice.” FDA, 15 Feb. 2022, https://www.fda.gov/about-fda/oncologycenter-excellence/project-patient-voice Posselt, et al. “The Danish OPUS Early Intervention Services for First-Episode Psychosis: A Phase 4 Prospective Cohort Study with Comparison of Randomized Trial and Real-World Data.” The American Journal of Psychiatry, 28 July 2021. https:// doi.org/10.1176/appi.ajp.2021.20111596 Roque, et al. “A New Tool for Assessing Mobile Device Proficiency in Older Adults: The Mobile Device Proficiency Questionnaire.” Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, vol. 37, Feb. 2018, pp. 131–156. https://doi. org/10.1177/0733464816642582 https://pubmed.ncbi.nlm.nih.gov/27255686/ Schneider, et al. “Bridging the ‘Digital Divide’: A Comparison of Use and Effectiveness of an Online Intervention for Depression between Baby Boomers and Millennials.” Journal of Medical Internet Research, vol. 236, Nov. 2006, pp. 243–251. Secher, et al. “Ten-Year Follow-up of the OPUS Specialized Early Intervention Trial for Patients with a First Episode of Psychosis.” Schizophrenia Bulletin, vol. 41, May 2015, pp. 617–26. https://doi.org/10.1093/schbul/sbu155 “Shaping the Digital Transformation in Europe.” European Commission DG, Communications Networks, Content & Technology, Sept. 2020, https://www. ospi.es/export/sites/ospi/documents/documentos/Sstudy_Shaping_the_digital_ transformation_in_Europe_Final_report_202009.pdf “Shaping Europe’s Digital Future.” European Commission DG, Communications Networks, Content & Technology, Sept. 2020, https://digital-strategy.ec.europa. eu/en/news/commission-publishes-analysis-macro-economic-potential-digitaltransformation-independent Soyeon, Kyoung-A, et al. “Measurement of Digital Literacy among Older Adults: Systematic Review.” Journal of Medical Internet Research, Feb. 2021. https://www.jmir. org/2021/2/e26145/PDF Sunstein. Why Nudge? The Politics of Libertarian Paternalism. Yale University Press, 2014. The European Programme of Work, 2020–2025: United Action for Better Health, Copenhagen: WHO Regional Office for Europe, 2021. Licence: CC BY-NC-SA 3.0 IGO. “The Evolution of an Indicator and Compare Countries.” Data Visualisation Tool – Data & Indicators. https://digital-agenda-data.eu/charts/see-the-evolution-of-anindicator-and-compare-countries#chart={%22indicator-group%22:%22ehealth%22, % 2 2 i nd ic at or % 2 2:% 2 2 i _ i h i f % 2 2 ,% 2 2 br e a kdow n% 2 2:% 2 2 i nd _ t ot a l% 2 2 , % 2 2 u n i t - m e a s u r e % 2 2 : % 2 2 p c _ i n d _ i u 3 % 2 2 , % 2 2 r e f - a r e a % 2 2 :[ % 2 2 E U %22,%22DK%22]} Ursenbach, et al. “Evidence for Measurement Bias of the Short Form Health Survey Based on Sex and Metropolitan Influence Zone in a Secondary Care Population.”

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Health and Quality Life Outcomes, vol. 18, 3 Apr. 2020, https://doi.org/10.1186/ s12955-020-01318-y “Which Country Do You Consider Being a Role Model for eHealth Innovation in Europe?” Statista, 2021, https://www.statista.com/statistics/1010663/countries-perceivedto-be-a-role-model-for-ehealth-in-europe/ World Health Organization. “Ethical Considerations to Guide the Use of Digital Proximity Tracking Technologies for COVID-19 Contact Tracing: Interim Guidance.” World Health Organization, 28 May 2020, https://apps.who.int/iris/handle/10665/ 332200

4 ARE WE READY FOR HEALTHCARE 5.0?

4.1 Why Healthcare 5.0? The successful implementation of opportunities brought by the digital revolution requires more than just an understanding of technological advancements. The paradigm shift is about multi-dimensional change. As data builds a network across many devices, transversal connections are built across many scientific fields as well. Interconnectedness is the game changer brought by the digital revolution. Based on the systematic literature of 58 peer-reviewed studies published between 2001 and 2019, the successful digital transformation must encompass many spheres including the adjustment of core business and leadership, adjustments in resources and capabilities, the reconfiguration of processes and structures and finally, the implementation of a vivid digital culture.1 In order to embrace all aspects of that multi-dimensional change, a new mindset must be developed. That’s what I am trying to advocate for in this book. Masterman and colleagues contextualized paradigm shift in as many as 21 different categories, including the metaphysical (beliefs, standards or speculations), sociological (universally recognized scientific achievements) and finally, methodological (tools, techniques, methods or approaches that direct research) areas.2 For the digital revolution to introduce real changes, the end users of digital technologies must be ready not only for a given technology but also to embrace such multi-dimensional change. On that note, I must admit that I am truly a great supporter of the vision of super smart Society 5.0, promoted by Keidanren ( Japan Business Federation), which replaces Society 4.0 (information). Its inclusive nature is powered by digital technologies delivered and used in cyberphysical systems. Keidanren defines Society 5.0 as “A human-centered society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.”3 Data science is to become the fuel of Society 5.0. Its growth DOI: 10.4324/b23291-4

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pathway is determined by the general use of artificial intelligence (AI) and data sharing across services. With the Internet of Things (IoT), robotics, AI and Big Data, Society 5.0 goes one step further from the digitalization of the economy towards the digitalization of the society.4 Data will entirely transform the way we live! The vision is that new information technologies will free humans from exhausting routine work. Thanks to the improvement of using available information, Society 5.0 is to become more creative and innovative but also more efficient. This is what Henry Kissinger and Eric Schmidt, as well as Daniel Huttenlocher, wrote in their book The Age of AI. That process finally leads to the development of a new ecosystem by the integration of different sectors centred around individual needs. Society 5.0 is built on the creation of new social and business chains and, eventually, new values for society itself too.5 The key success factor is access to information. It all starts with the act of knowing. “I didn’t have any of the classic risk factors, like older age or traumatic brain injury. I had none of the known, rare ALS variants,” said Rahul after being diagnosed with an aggressive form of amyotrophic lateral sclerosis (ALS).6 Before becoming sick, Rahul would just walk into a club and say he was an international DJ,” said his friend. “He would put on this British accent. He’d say, ‘I just flew in from London, I was in Tokyo.’ People would be like, ‘Wow, c’mon in.’” His passion was actually science. Rahul was, in fact, a neuroscientist, clinician and an assistant professor of neuroradiology. As co-director of the Laboratory for Precision Neuroimaging at the University of California, San Francisco, he devoted his career to the search for the causes behind the most devastating degenerative brain diseases such as Alzheimer’s, frontotemporal dementia and … ALS. At the end of his life, he wished he knew about his rare condition earlier to “start preparing for it and make every hour count,” he said a couple of months before he died. Rahul’s testimony in both his scientific and health journeys is proof of what matters most in the field of healthcare, which is actually data. He and his team were combing various data sources from imaging, genetic and healthcare resource utilization and followed one simple mission: predict and prevent sickness. Already today, thanks to technological advancement in the connectivity of different data sources, not only can we estimate the risks of monogenic diseases, but also those that are the effect of a large number of genetic variants scattered throughout the genome as well as environmental and lifestyle factors. The future driven by data allows us to help people like Rahul to prepare themselves earlier for the consequence of rare condition but also prevent at least some of it with early treatment. Despite the fact that Rahul was not able to find predictors of his rare case of ALS, his work, enriched with personal experience, contributes significantly to the quest for effective therapies by the accumulation of research. That’s what matters. Prediction is important not only to ease the emotional stress of late diagnosis and uncertainty of the outcome, but it also has its monetary value as well.

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Rare diseases constitute heavy burden on the healthcare system. One of the most expensive drugs ever tested in clinical practice was used to treat spinal muscular atrophy (SMA). SMA is a serious and rare disease of the nerves that cause muscle weakness. It is estimated that the drug will cost approximately €1.9 million per course of treatment.7 The consequences to a lack of insight into the rare condition translates into a lack of treatment and unmet medical needs, thus resulting in the high price of orphan drugs. It is the direct result of the complexity of the research and development process, including unknown aetiology and difficulties with patient recruitment, to name a few. The accessibility of data can change everything and allow for early diagnosis. Prediction and prevention (or at least preparedness for health outcomes) would cost less in both emotional and healthcare resource utilization, both of which have a monetary value. We know that up to 80% of rare diseases have genetic origins; hence, if the right information is available, there is a great opportunity to make most of them predictable. Today, the most common test used to diagnose rare diseases is whole exome sequencing (WES).8 Generally speaking, the growing interests in genetic research received traction only some years ago with scientific initiatives such as The 1+ Million Genomes project, also referred as “1+MG,” launched on April 10, 2018.9 The role of precision medicine in the efforts towards disease prediction did not happen overnight. The very first gene sequencing took 13 years and cost around US$3 billion. It led, however, to genetic testing becoming availabile for an average person. According to The Economist, it can be done within a week at the cost of $600 in 2021.10 Market leader Illumina claims it can process 48 entire human genomes in two and a half days.11 Such innovation is the result of the fact that, in addition to whole-genome sequencing (WGS), there is now a new low-cost and fast method of next-generation sequencing (NGS). Insights into genomic data can provide  opportunities for the prediction of mutations in a patient’s genome, rare genetic variants, gene profiling, tumour-normal comparisons and others. One of many other examples of how gene sequencing can support not only the diagnosis but also the prevention of disease consequences is the identification of the gene responsible for monogenic early-onset osteoporosis (EOOP). It is a rare disease defined by low bone mineral density (BMD) that results in an increased risk of fracture in children and young adults.12 Prediction and prevention thanks to gene testing is not just a strategic imperative for rare disease. Let’s go further towards more common types of health problems. Recently the polygenic risk score (PRS) has gotten significant traction. It is a single-value estimate of an individual’s genetic liability to a trait or disease. It is calculated by the sum of an individual’s risk alleles, weighted by risk allele effect sizes derived from genome-wide associated study (GWAS) data. PRS is a new concept that has the potential to determine the cumulative effect of millions of small genetic variations on the risk of onset of specific diseases. The first research highlighting the opportunity with the PRS approach was a

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study by Khera and colleagues in 2018. It received a lot of attention because the authors were able to genetically characterize the population at a threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease and breast cancer.13 Scientific breakthroughs of that kind are possible because of data collection, and in that particular case, thanks to such initiatives as the UK biobank that holds more than 500,000 UK participants.14 Given such spectacular advancements, why do we not observe widespread use of genetic tests for prediction and prevention? The problem is that across the globe, it is hard to find the reimbursement framework for genetic testing. Julie Robillard and colleagues in 2020 highlighted the challenges with public-financing for such practice in Canada.15 The lack of economic incentive for developing clinical practices based on such tests was noted by Judith Kruse and colleagues for Europe in 2022, too.16 Still, the recent experience during the COVID pandemic shows clearly that prediction and prevention on a huge scale is not only hypothetically possible but can be successfully achieved. More than 56% of the global population was fully vaccinated (at least two doses) at the end of February 2022, so a bit more than a year since vaccination became available.17 I am sure it will be recognized as a remarkable achievement from a historical perspective. The key success driver was not only self-protection, but also the health of others as the understanding grew of the healthcare burden of disease consequences. What’s remarkable as well is that some countries like Austria or Greece introduced mandatory COVID-19 vaccines with financial fines for non-compliance.18 I personally believe it has been the greatest campaign towards disease prevention in human history. It was surely driven by extraordinary circumstances – the crisis mode. With mandatory COVID vaccines, what have we learned? Will we accept prevention as a compulsory method of disease control? As Thomas Kuhn described about the sources of paradigm shifts already in 1962, there is no change without crisis. What if we use that pandemic crisis and continue that journey towards prediction and prevention? What if we wished to extend that mandatory vaccination policy to other diseases that are preventable? As already exemplified, the overburdened state of the healthcare system is the same. The risk of reinfection for certain diseases can be added too. There are already studies of cancer vaccines using the same concept of engineering mRNA that carry cell instructions for protein production that stimulates an immune response. Such methods of delivery via vaccines are already tested in the fight against pancreatic cancer, colorectal cancer and melanoma.19 In addition to mRNA vaccine development, in October 2021, the Cleveland Clinic  initiated a phase 1 clinical trial of another vaccine across cancer-free women with high risks of having triple-negative breast cancer. 20 Today, we have enough evidence to support the tests for some mutations of genes BRCA1 and BRCA2 knowing that they are responsible for a significantly increased risk of breast cancer by up to 80% as well as ovarian and

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other cancers.9 Today, this knowledge supports a treatment pathway, but early detection can lead to early prevention as was promoted by the case of Angelina Jolie. Should we make such tests compulsory for woman who have a family history of breast cancer as we have introduced mandatory COVID vaccine tests? But what about everyone to becoming aware of the risk of chronic conditions from various lifestyle habits? Such predictions and actions thereafter could have even higher chances of successful prevention if changes in behaviours are followed.

4.2  Governing rules of Healthcare 5.0 These developments have led to my vision of Healthcare 5.0, where prevention is the mission and prediction is a basic mode of operation. How can it be achieved? In Healthcare 5.0, everyone is able to determine his or her disease risks and plan their healthcare journeys accordingly. Following the axiom of individual sovereignty, access to information does not only allows us to find health solutions fitted for our needs, but also develop insight into our own preferences and self-awareness to understand our needs. Given the understanding of the challenges and opportunities in the field of healthcare, we are well prepared and hopefully motivated to set our own health journey to the desired destination. Knowledge builds awareness but also makes us accountable for our own decisions. The growing amount of data ends the era of information asymmetry. That’s what Healthcare 5.0 is meant to achieve. How? There are two specific governing rules, each one corresponding to the respective axiom of individual sovereignty and holistic approach that defines the mode of operation of Healthcare 5.0.

4.2.1 What kind of governing rule does individual sovereignty imply? In the digital era, data has the potential to become the fuel for the self-driving car of healthcare for every patient. Therefore, the underlying rationale of Healthcare 5.0 is individual sovereignty. I define it as the patient’s ability to live a free life according to their individual desires and expectations, preserving everyone’s right to self-dignity and self-respect. It is actually one step ahead of the model of shared decision-making that advocates for the incorporation of patient preferences, beliefs and values.21 With my approach, I would like to reach the idea of the enlightenment proposed by Kant from 1784. “Enlightenment is man’s release from his self-incurred tutelage. Tutelage is man’s inability to make use of his understanding without direction from another.” To achieve enlightenment, the role of government in Healthcare 5.0 is set to ensure freedom for individuals to make cognitive health choices. That’s what the digital transformation made possible, thanks to the availability of information. It is in contrast with information asymmetry that has been the key rationale for healthcare system organization and

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the role of clinicians as the main source of information. In the era of data, the dynamics of roles and responsibilities change with more data being available to everyone. I believe that there is a need for a new governing rule that is the access to the right information at the right time regarding any health-related matter. When it has been implemented, the re-introduction of individual sovereignty will lead to the patient becoming the decision-maker. It requires the redesign of governmental engagement and definition of new healthcare system objectives. Instead of the quest for the optimal allocation of scarce healthcare resources defined from the payer perspective, it is about optimal access to healthcare services and health information from the individual perspective. Paternalism is to be replaced with individual autonomy fed with credible up-to-date information that helps each individual to make the right choices. For that to happen, Healthcare 5.0 will provide training tools, programs and digital solutions that help patients thrive with full digital literacy and tailor-made clinical knowledge about health conditions. Healthcare 5.0 is not only about medical needs, but also educational needs regarding the methods of treatment and even health behaviours that might prevent or at least minimize the risks of certain diseases throughout the lifespan. Healthcare 5.0 is not only meant to cure patients but, most importantly, to teach every individual how to avoid health problems. In most jurisdictions, we built our healthcare system based on the principle of the equity of access to healthcare defined in terms of medical needs. We even defined dignity and solidarity as additional criteria for access to treatment. We have not thought, though, about the importance of equity of access to health information. The role of the government is, therefore, to support individuals to develop self-awareness by equipping each of us with the most recent, objective and credible data. What better way to empower patients than by providing access to the right information at the right time? Healthcare 5.0 is designed in the mode of data sharing. The governing principle of equal access to information needed for decision-making may like a breakthrough. Data, however comes along with digital transformation as an opportunity we should not miss. Given that change, Healthcare 5.0 is the system centred at building the mindset of informed individuals who are prevented from becoming a patient. If someone wished to argue whether we have the right to demand such effort from the society, let me recall Kant, who in the 17th century said, “Self-incurred is this tutelage when its cause lies not in lack of reason but in lack of resolution and courage to use it without direction from another.” Sapere aude! “Have the courage to use your own reason!”

4.2.2 What does a governing rule axiom of holistic approach to healthcare imply? As previously explained, the new healthcare system does not need to be limited to a given geographical location, as healthcare services can be virtually

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introduced and accessible locally or remotely upon specific arrangements. The key element is to shift the perspective from a limited budget to unlimited technological advancements. When we finally take away the discussion about the lack of budget, the adoption of innovation will not be constrained from a jurisdictional perspective, but only a patient need perspective. As a result, in Healthcare 5.0, the price of a given healthcare service will be linked only to the outcomes it produces (more is discussed later in the reimbursement section) as patient-centric outcomes or consumer satisfaction. For the terminally ill patient, it might be just as crucial to be independent in his self-care as cognitive enhancement therapy is for young students before an important exam. Both aspirations might be perceived as secondary for the clinician treating both individuals in the pre-Healthcare 5.0 era. Still, some may ask why we should pay for such things in a healthcare system. In Healthcare 5.0, it is not our decision for others, but it is not our decision but each individual’s decision for themselves. Let us consider the terminally ill patient who prefers to live shorter life without pain, as it allows him to be independent, instead of living longer life but being bedridden. What about the student who wants to undergo cognitive enhancing therapy to achieve his job aspirations and, consequently, a greater financial status that allows him to afford more financial resources to pay for healthcare in the future? Healthcare 5.0 is the consumerdriven healthcare market. It does allow for a holistic approach with the outcomes defined from an individual perspective. Thus, the second governing rule established in Healthcare 5.0 is to pay for patient-centric outcomes. It also has other implications, such as helping to define and measure technological advancement. The digital transformation cut the physical distance and consequently redefines the boundaries of healthcare. As stated earlier, the patient becomes the decision-maker. According to Kant, enlightenment requires man leaving his immaturity, which he names as the “incapacity to use one’s intelligence without the guidance of another,” and adds that enlightenment requires nothing but freedom. That shows how the implementation of individual sovereignty leads indirectly to a holistic outlook over health. This is now the situation with a patient receiving the treatment directly without the presence of a clinician. Healthcare will be delivered to the patient instead of the patient being delivered to healthcare. Only when technological advancements do not allow remote treatment of the patient should the physical presence at the clinic be arranged. This offers the opportunity for a greater role of patient preference to influence the development of innovative technologies. In other words, the virtual ought to be the primary choice instead of the office-based option. This understanding implies that the governing rule of patient-centric outcomes is to be delivered directly to the patient unless the nature of the problem defines it differently. In summary, the first governing rule of Healthcare 5.0 is about the equity of access to the right information at the right time to allow everyone to plan his or her health journey according to their own desires and expectations. The

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delivery of information is a new type of responsibility of the healthcare system that helps everyone achieve individual sovereignty. It is linked with the second governing rule, which supports the development of the holistic approach to health. Through by the power of knowing, we create another power of understanding of our own preferences and desires. That’s the purpose of the governing rule of patient-centric health outcomes. It has to be acknowledged, however, that there is one important exemption from both governing rules. It is what we economists call externalities. It is important to realize that the patient-centric system with a holistic approach to health outcomes does not mean only that the patient has right to choose and the right to decide. It also means that the patient has the self-obligation to take care of their life to the extent to prevent its impact on others. If your actions are detremental but do not impact others, live your life freely. Only if your actions impact others should the system correct it. Otherwise, you and only you must be the judge of destiny. However, before we become aware of what our preferences, expectations and needs that define patient-centric healthcare, we must first know the facts. Hence, the principle of equitable access to information. How will both governing rules be used in Healthcare 5.0?

4.3  Description of Healthcare 5.0 4.3.1 Healthcare 5.0 – from the power of knowing to the power of action Since the birth of the healthcare system, it has been assigned the responsibility to treat patients. Healthcare 5.0 will be actually designed for healthy individuals as well and focus on how to limit the need for treatment. That’s the mission of prevention. In contrast to today’s healthcare system, Healthcare 5.0 is the source of solutions for health improvements. In Healthcare 5.0, we change that negative narrative and passive mode into a positive narrative and actionable mode. Healthcare 5.0 is a network of human-centred ecosystems. Everyone sets up his own digital healthcare tailored to individual needs and expectations. Let me elaborate on each point that facilitates the individual journey to the development of his or her digital healthcare ecosystem.

4.3.2  Healthcare 5.0 – Google health engine The creation of the mindset of individual sovereignty will start with the development of a worldwide repository of the scientific findings about all available predictive methods that could support everyone with planning the diagnosis and self-healthcare. It is a Google health search engine with a mobile application available on each smartphone. It is to be fuelled with results of clinical trials, observational studies and other kinds of research as

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well as guidelines and individual patient stories. Everything published there has to be peer-reviewed by clinical experts first. This idea is not science fiction. Today, scientists use the PubMed portal, which comprises more than 33 million peer-reviewed research papers as of March 1, 2022. There is nothing similar though for non-scientific readers. The National Library of Medicine, the owner of PubMed, does offer information for the general public – Medline Plus. It is not, however, focused on recent scientific accomplishments, but a general review of diseases, symptoms and basic recommendations. There is a health and wellness part as well, but no information on prevention is directly presented. It does claim to provide an overview of “symptoms, causes, treatment and prevention for over 1000 diseases, illnesses, health conditions and wellness issues.” However, a notice on the webpage shows “Page last updated on 1 February 2017” while I was reviewing it on March 3, 2022. No recent scientific developments are presented. Examples of similar approaches can be found in other countries like the NHS in the UK. 22 Apparently, that’s not enough to shift towards prevention. In my opinion, science should not be limited to scientists. The Google health engine is to be used free of charge by anyone anywhere, anytime. It is not only the vehicle to deliver evidence-based knowledge about healthcare, but the pathway to individual sovereignty by implementing the rule of equal access to information. How can we ensure a Google mobile health engine is really used? Some examples exist today with medical and public health information in “easy to read” versions, such as the website of the National Board of Health and Welfare in Sweden.23 How the Swedish approach differs from the earlier mentioned US and UK examples is the fact that it provides more than just basic information but is written in a style with less cognitively burdened language. However, recent scientific advancements are not presented there, either. Why can’t we have an “easy to read” abstract along with the list of implications of given research for each individual bearing the risk of disease or disease itself ? I am a strong believer in the applied science approach. My proposal includes only research meant for a specific focus on a patient’s journey through disease stages to be included in the Google health engine. Other studies, such as phase II or earlier, along with unconfirmed findings might not be available for patients. That’s why it is important to have a peer review process in place that can select scientific news that is applicable to individuals. The selection criteria for research findings to be used by the Google health engine is again to focus on awareness about specific diseases and risk factors. Most importantly, it has to be easy to use the same way we use other media outlets that provide us with news from the field of politics or culture. We need to keep it engaging for individuals to build their passion and provide some fun while reading about new healthcare solutions that truly improve their well-being. That’s the mission of the Google health engine.

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4.3.3  Healthcare 5.0 – repository of preventive measures Apart from the “easy to read” research findings fuelling the Google health engine, Healthcare 5.0 addresses the aspect of the practical applicability of science as well. To formulate specific suggestions directly towards patients based on the study findings will be an additional compulsory requirement for each study author. Therefore, the researchers will need to transfer the scientific achievements into a language understood by non-scientific individuals and ensure their research can be easily understood by either patient, caregiver or healthy individual with a certain risk of given disease. Hopefully, the Google health engine will be the source of knowledge on “how I can improve my life” through recommendations that focus on implementing new scientific solutions. This vision is therefore not only for individuals to learn and understand more about health. In Healthcare 5.0, the Google health engine also allows searching the repository of preventive tests and diagnostic measures per indication. It is a virtual repository of health solutions that includes all potential preventive methods. Similar to the research being published by the Google health engine, the repository should only provide access to health technologies that are certified and reimbursed (more about that later in that chapter). Since each researcher presenting the findings will have to propose recommendations, the appropriate links to the products and services already available in the repository will be provided for individuals to learn more about how to prevent or how to treat certain health conditions in question. That repository should be worldwide, and such institutions like the WHO should be accountable for their conduct and keeping up-to-date with relevant statistics per certification and reimbursement processes. Organizations will be responsible for linking new scientific findings with the right preventive methods or tools. I will elaborate on that aspect later in this chapter. Are we very far from the implementation of such a health search engine linked with a repository of health technologies that help us to learn and act upon research findings? I do not think so. Today, we have plenty of opportunities to learn how to diagnose, prevent and predict that can be used to populate such a repository of preventive measures provided prior certification and reimbursement. For example, Sophia Genetics, is using AI algorithms to continuously learn from thousands of patients’ genomic data. The company says its technology is helping to quickly and more accurately diagnose conditions like cancer, metabolic disorders and heart disease. Sophia Genetics claims that it can support laboratory needs with the identification of as many as 143 cancer-predisposing genes. Cancer remains a leading cause of morbidity and mortality across the globe. 24 Hereditary causes account for ~10% of cancer cases, 25 and an estimated 20% of cancer patients have a positive family history of cancer. 26 The recent transformative advances in DNA sequencing hold the promise of many more cancer predisposition gene discoveries and greater

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and broader clinical applications. There are more than 50 hereditary cancer syndromes already described. 27 Veritas Intercontinental is trying to encourage everyone to test their genetic material to be able to read it by “creating a library” about each individual. According to some sources, a separate company, Veritas Genetics, can determine a complete readout of the genetic code with a focus on more than 650 clinical outcomes.28 Veritas Intercontinetal offers a genetic test, myCancerRisk, that determines the risk of hereditary cancer based on the study of 40 genes related to the most common types of hereditary cancer as well as the “myCardio” test that analyses 100 genes to determine the risk of inherited cardiovascular disease.29 There are already a number of companies such as 23andMe, AncestryDNA, MyHeritage and Family Tree DNA that sell tests that use saliva samples returned by mail to determine consumers’ genetic ancestry and risk of developing certain illnesses. There were more than 26 million customers as of October 2019.29 The future we are heading towards is about real-time gene sequencing, which will only expand opportunities for health risk assessment. In addition to second-generation sequencing and other innovative gene data analysis, there is nanopore sequencing, a revolutionary process that can cut the cost of gene analysis and reduce the time involved from days to hours or even minutes.10 The MinION sequencer launched by Oxford Nanopore is pocket size and the company can make gene sequencers small enough to fit in a pocket rather than on a desktop. Prevention is not limited to genetics. It is about all possible ways of early detection. For example, 23andMe bought telehealth company Lemonaid Health that offers patients direct online access for a number of common for consultation through treatment. It also offers free and fast delivery of prescription medications. “By starting with genetics as the foundation, we will give patients and healthcare providers better information about health risks and treatment,” according to 23andMe Chief Executive Officer Anne Wojcicki.30 Today, there are many types of digital diagnostics used either directly on the phone or as a separate device connected to a phone. Among the most sophisticated is BiliScreen, which uses a cellphone camera to measure the bilirubin level in a person’s eyes for early diagnoses of pancreatic cancer.31 Another, nQ Medical, analyses a person’s typing patterns to diagnose Parkinson’s disease and tracks the effect of medication. According to the US Food and Drug Administration (FDA) breakthrough designation, “The neuroQWERTY software as a medical device platform intended to characterize abnormalities in psychomotor performance and fine motor function by analysing mechanical keyboard and touchscreen device interactions in adults with, or suspected of having, Parkinson’s Disease (PD).”32 ResApp Health  uses a cellphone microphone to analyse coughing sounds to accurately diagnose pneumonia and other upper-respiratory diseases.33 Prevent Biometrics’ mouth guard instantly detects an athlete’s concussion and notifies the coach.34 The ActiGraph actimetry sensor measures physical activity and sleep quality to support early detection of cardiac dysfunctions,35 the Galaxy Watch 4 checks blood pressure at any time and the

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FreeStyle Libre 2 patch worn on the back of the arm continuously measures glucose levels. According to the estimates by Deloitte, in 2022, there were more than 300 million health wearables (such as smartwatches, fitness trackers, sensors and wearable devices) on the market with the forecast to reach 440 million by 2024.36 It is not surprising if one takes into consideration that the investment in digital health startups reached $57 billion in 2021, which is double the number compared to before the pandemic.37 If we combine such advancements with computational disease models, we can plan the prevention journey more effectively. Further, in Healthcare 5.0, I hope we will be able to play with our vision of the future by starting “what if ” scenarios that apply a given reduction of estimated health risks owing to another technological advancement. The revolutionary change is on the horizon – digital twins. An example is a mouse model of human rheumatoid arthritis that was developed by a group of Swedish researchers from the University of Linkoping.38 They used a single-cell RNA sequencing technique and developed a mouse twin to study gene activity in each of thousands of individual cells from the sick mouse joints.39 In total, 7086 and 1333 cells for the joint and lymph node data were used.39 Scientists subsequently treated the digital version with thousands of drugs before selecting the best drug to treat the real mouse. The future of the digital twin approach is enormous. It goes beyond treatment selection. There are full-body scanners that produce results within 15 minutes, i.e., ten times faster than conventional MRI scans. Such an approach allows the establishment of the baseline of an individual’s genetics, chemistry, anatomy and subsequent “updates” reflecting on lifestyle and medical history over time. With the digital twin approach, we can develop integrated tools that correlate between quantitative changes and connect our body parameters with individual risk factors. Kaiser Foundation Hospitals invested in such technology in 2021.40 The Google health engine connected with a repository of preventive methods available for everyone everywhere at any time is meant for the implementation of the governing rule of equal access to information. Combing it, however, with such scientific achievements as genetic tests, digital twins or any of such digital solutions as mentioned above will allow us to move a bit further to ensure that data helps us with the planning of daily habits today working toward certain patient-centric objectives, which is a second governing rule of Healthcare 5.0.

4.3.4 Healthcare 5.0 – ambulatory care Let’s be realistic; we can’t offer various preventive tests in the clinical practice and expect that everyone will be willing to use them. That requires a mindset of willingness to take responsibility for our own life. This is the mindset of

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proactive seeker of health solutions that is not scared of some investment today for a better life in the future. It is the mindset of a sovereign individual. With the Google health engine and virtual repository of preventive measures, Healthcare 5.0 keeps the general public constantly updated about the benefits of prevention. Still, how does it make the general public act upon prevention? The answer is simple: build a system of continuous health monitoring. Once the disease risks are established with the support of predictive methods such as those already described and others, the program of health monitoring is the natural next step. Healthcare 5.0 provides it tailor-made to each individual with periodical reassessments. To ensure continuous health monitoring, the mobile phone adopts the role of the medical aid centre with 24/7 access. It is a health hub with both ambulatory as well as emergency care available in the palm of the patient’s hand. Healthcare 5.0 is not a physical healthcare system opened five days a week during office hours with doctors available upon earlier appointment. It is a virtual healthcare system defined as far as the solution to the unmet medical needs can be found unrestricted from geographical borders. Sounds scary? It is similar to what has already been happening in the sector of financial services. How often do you visit the bank? In 2020, as many as 1.9 billion individuals worldwide actively used online banking services and the forecast indicates it may reach 2.5 billion by 2024.41 In addition to that, as many as 79% of smartphone owners have used their phones for online purchases in the last six months.42 So the trust is granted, although it is not a governmental entity in that virtual world. In a similar fashion, in Healthcare 5.0, smartphones become the point of contact. Every individual will have their own healthcare menu consisting of applications available on the phone chosen according to his or her liking. In the health hub, we are not alone. Everyone has his or her own virtual assistant (VA) as the healthcare navigator. It is our first point-of-contact doctor responsible for the support with health monitoring. Already in 2017, Juniper Research estimated that the adoption of conversational AI technologies in healthcare is expected to translate to annual cost savings of approximately $3.6 billion globally by 2022.43 On that note, it must be emphasized that VA develops multiple functions in Healthcare 5.0. The first important role of VA is to support our search in the virtual repository for the right digital health technologies that address our medical needs and health aspirations. For that purpose, it anchors the digital twin technology that will define our optimal healthcare journey based on our genetic composition. Given a plethora of alternative options fulfilling the same objective, VA does also support us with a useful comparison of available tools based on predefined criteria. In Healthcare 5.0, it is the VA who understands both our genetic compositions but also our health preferences based on the observation of our behaviours and attitudes identified in the dialog with us. Hence, VA is able to set selection criteria for digital health solutions and provide us with the ranking of best choices accordingly. Let’s say that there are at least three

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different options to monitor our blood pressure. Asking you multiple questions about your preferences as well as observing your daily habits, VA may suggest that you consider either wearables worn on the finger or wrist (such technology has already been proposed by Valencell44) or Apple Watch Series 8 (not confirmed yet with this function at the time of writing this book but it has been rumoured45). Knowing your resistance to wear any devices, your VA may eventually offer you a mobile application such as Anura introduced by NuraLogix46 as it sees how diligently you use other mobile applications. In fact, VA does more than screen the virtual repository for the right preventive method, it provides you with a ranking of the options based on the understanding of your preferences as well. Once the digital solutions have been selected, VA is going to ensure their proper utilization. This brings us to the second role of VA: health monitoring or, more precisely, monitoring of your adherence and progress in the improvement of health outcomes. There are already examples of VAs used for this purpose. A study of 13 patients with type 2 diabetes mellitus and depressive disorder revealed the use of VAs can be useful and effective for improving patient medication adherence (patients answered 74.4% of the reminders received, and the HbA1c mean improved by 0.3%). The mean of medical appointments per month decreased by 0.7 appointments per month, which supports the potential use of VAs for reducing associated healthcare resources. The study was conducted between May 9, 2019 and February 9, 2020 and almost 70% of the patients (9/13) agreed with the idea of continuing using the VA after the study.47 Another example was a randomized unblinded study with 70 individuals aged 18–28 years randomized to receive either two weeks (up to 20 sessions) of selfhelp content derived from Cognitive behavioural therapy (CBT) principles in a conversational format with a text-based conversational agent (Woebot) (n = 34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,”  as an information-only control group (n = 36). The Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F = 6.47; P = 0.01), while those in the information control group did not.  Woebot led to a 22% reduction in depression symptoms in two weeks compared to an active control group.48 In another study were 192 women 18 years or older who had recently given birth. Ninety-one percent of participants reported at least high satisfaction with Woebot after 6 weeks, 64% of which rated their satisfaction “very high.”49 VA in Healthcare 5.0 can provide more advanced services, too. This leads us to the third distinctive feature of VA. By aggregating outputs arriving from multiple digital health outlets, VA issues any disease-specific and holistic recommendations if any changes in patient behaviour are required. How is it managed? VA oversees the continuous cross-analysis of data output generated from all digital solutions employed by the individual. In other words, VA is the manager of the individual’s healthcare ecosystem. The VA will track any data output and ensures it is used for analysis

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by the personalized machine learning (ML) algorithm designed especially for each one based on a holistic approach to health. In fact, it is an individual health objective function. To develop it in the first place, VA collects baseline data about the genetic composition as well as preferences and behavioural factors to define the health objective function from the perspective of individual well-being. Once data is collected from smart patches, smartwatches and smartphone apps, they are used to populate that ML algorithm on daily basis to ensure any differences between optimal scenarios and real-life achievements are continuously monitored. For that matter, the digital twin technology is adopted to ensure any departure from the established trajectory of the health objective function is captured by VA and above mentioned health recommendations are issued. This is how the health monitoring translates directly into actions and any updated information being available at any time for a given patient. Such new health recommendations work as additional health check-ups scheduled in a similar fashion to the software updates on our mobile phones. Some examples exist even today. For instance, the OMRON app, which collects and stores data from Omron blood pressure monitors, allows wireless connecting via  Bluetooth™ to easily share the cardiac monitoring historical data with a medical professional. It can be eventually connected to Alexa, so you can simply ask, “Alexa, what was my blood pressure average this month?”50 Healthcare 5.0 is real. With VA as our navigator, we can surely achieve better health outcomes. Similar solutions can be found for patients suffering from diabetes with Quantum Operation’s sensor’s “spectrum sensing technology” that is capable of measuring blood glucose through the skin while being worn on the wrist.51 Valencell, a leading company in the field of biometric sensors, has announced a new version of its cuffless and calibration-free blood pressure measurement technology, which can be integrated into wearables worn on the finger or wrist.51 Analysing the data on daily basis will be able to detect the early signs of any health issues. Namely, the collection of real-life data from continuous monitoring gives us also the opportunity to prevent disease consequences and plan healthcare needs. Today, there are tools that use computer algorithms to help patients with self-diagnosis or self-triage, however, they do not produce satisfactory results. Two independent reviews of such tools arrived at similar results. Hannah L. Semigran et al. reviewed 23 tools in 2015, while Adam Ceney looked into 12 diagnostic platforms in 2021. To determine the diagnostic and triage accuracy of online symptom checkers, the clinical vignettes (45 in 2015 and 50 in 2021) were adopted. Over a third of occasions (34% in 2015 and 37% in 2021) provided the correct diagnosis at first instance and listed it as potential diagnosis in the top 20 (2015) or top fine (2021) approximately half (58% in 2015 and 51.0% in 2021) of occasions.52 The conclusion is that such patient-led symptom trackers are not sufficient in supporting the self-diagnosis. VA in Healthcare 5.0 does it better. It offers also a form of triage that has been tried until today but not successfully. In case of alarming outputs, any

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further diagnosis requiring a hospital procedure will be remitted to the specialized health chain. VA will liaise with the patient on the choice of health chain given waiting time, distance and other factors. The exact scope of reassessment will be adjusted not only to the baseline risk profile but also to changes in health habits. The aim is to achieve similar results as for other successful industries. Let’s take the example of aerospace. The system adopted there allows the prevention of safety incidents with 15 to 30 days advance notice thanks to the smart analysis of data transmitted from sensors in a jet engine during flight.53 Why can’t such an approach be made real for healthcare? I can, and it is the reality for healthcare 5.0 thanks to the availability of data. McKinsey estimates that only 1% of the world’s data is currently being used for analytics and collaborative purposes.54 Healthcare 5.0 starts the culture of data collection. It is about building bridges across different data sources. Data are collected throughout the patient journey. Only when we can learn about symptoms and health problems will we be able to find solutions. It is about the health information exchange (HIE) or data sharing as both terms are used interchangeably. For that to happen, there is the need to first establish system interoperability. It is understood as the ability to “access, exchange, integrate and cooperatively use data.”55 There are currently numerous initiatives that show how data actually connects different healthcare providers and eventually allows us to develop better and more accurate healthcare services. There are many projects that are available already today to work towards an interoperability mode with health data transfer, such as Estonia’s X-Road, Finland’s Findata, The German Medizin Informatik Initiative and the Yale Open Data Access. There are already standards for data transfer established as well. It is about HL7 (where HL7 is a the international interoperability standard used to transfer the data between various healthcare providers ) and its newer version Fast Healthcare Interoperability Resources (FHIR). Interoperability has different layers. It is, in fact, about foundational and structural but, most importantly, semantic and organizational system organization. Especially the latter two are major influencers of change towards the data-sharing culture. The usability of data is not only about data exchange but also integrated decision-making processes. For that to happen in Healthcare 5.0, signals collected from any IoT are used in an integrated way with other data arriving, for example, from a patient’s health record. Again, there are some examples of such approaches. One example is VitalSight, a set of blood pressure cuffs and a hub that uploads results to a doctor automatically. It includes a connected blood pressure monitor used by a patient that transfers data automatically to the doctor. This technology can be integrated into an electronic health record (EHR) system, allowing doctors to receive patient data in their records or through a data analysis and automatic notification dashboard built by Omron.56 In other words, each health change is trackable and then recorded and then transferred to others that need to utilize it for continued integrity and connectivity. The best technology to develop efficiency in that process of data

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management is actually blockchain. There are already examples of how it can improve data sharing and consequently efficiency. An example is the MediLedger Project that was established in 2017 and gathers pharmaceutical companies and wholesale drug distributors together to use blockchain technology for tracking of prescription medicines.57 Apart from data management mentioned earlier, Mediledger and other examples indicate clearly that the blockchain technology can be a great tool for building the transparency of the transactions and consequently ensuring trust. An important element to mention here is both the Health Insurance Portability and Accountability Act (HIPAA) and EU General Data Protection Regulation (GDPR) rules that introduced data protection by placing some constraints on data use and reuse. As far as GDPR is concerned, I discussed it previously in an earlier chapter highlighting the importance of the explicit consent that must be ensured (not necessary for HIPPA) unless data are anonymized. The annonymisation may, however, destroy the data fidelity.58 All that to happen requires still data security and privacy safeguards. Cryptography holds the answer. It is feasible with such technologies as blockchain that offer multiple security layers on HIEs. There are, however, significant developments in support of greater usability of data in compliance with strict privacy protection rules. In January 2022, the United Nations initiated a project to test different privacy-enhancing technologies (PETs) using available data in collaboration with national statistical offices from the United States, Britain, Canada, Italy, the Netherlands and academic researchers.59 Among other methods, there is differential privacy, which introduces some additional information while still maintaining the validity of the analytical results and homomorphic encryption that remain encrypted while being analysed.60 These are considered as the most important trends in addition to methods already commonly used, such as data masking, de-identification and encryption. For some readers, this might have been a big deviation from the role of VA. It has to be noted, however, that data is the fuel of Healthcare 5.0. Without data collection, sharing and analytics, the new mode of prevention cannot be achieved. Digital technologies are, in fact, preventive tools that help on the journey towards individual sovereignty and a holistic view on healthcare needs. Does it mean we do not need medical professionals anymore? Absolutely the opposite is true.

4.3.5 Healthcare 5.0 – hospital care Before we discuss the new role of hospital care in Healthcare 5.0, let’s first accept the fact that the majority of the non-pharmaceutical healthcare innovations were designed for healthcare professionals to treat patients. The digital era opened up a new healthcare market with patients becoming the consumers. It is yet another reason why the time has come to introduce the mission of prevention directed to individuals with or, more importantly, without the support of healthcare professionals. While the technological advancements clearly indicate that patients

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take charge of their digital health monitoring solutions, clinical experts do not yet seem ready to accept the self-decision-making role of patients. The challenge is, therefore, how to adjust the dynamics of the relationship between patient and doctor to ensure the former becomes the owner of the healthcare ecosystem defined by digital health solutions. As far as planned hospital care is concerned, there are two dimensions that Healthcare 5.0 introduces as new concepts. The first relates to the specialization of care in a specific therapeutic area. The second involves the remote treatment of patients. The prerequisite for both is the removal of the geographical disposition of the healthcare system. The patient should be allowed to choose where and how he or she wants to be treated according to the individual sovereignty principle. With respect to the first concept of specialization, the doctors of Healthcare 5.0 are experts who “know more” but also “do better” and consequently “treat better” compared to the earlier healthcare system. How is it possible? Due to treating a significant number of homogenous types of patients, the doctors acquire unique knowledge based on data analytics from the evidence collected about these patients as well. The growing experience will naturally translate into those positive treatment outcomes, thanks to the learning process. In essence, it is achieved as the consequence of the acceleration of the learning curve. The importance of experience has been discussed extensively in the medical literature with respect to medical devices. For example, the analysis of 312 consecutive TF-TAVI cases performed by six interventional cardiologists between October 2006 and October 2013 indicated that the risk of short and long-term mortality was cut in half for high (more than 40 cases) vs. low experienced (less than 40) medical doctors.61 One can argue that this is against the holistic approach to health. Specialization also means, however, understanding and building links across diseases based on the available data and simulating different scenarios for a given patient treatment outcome. The oil for knowledge is the data to be used for selfimprovement through treated more similar cases but also collaborative efforts with other specialists. The latter is surely becoming important from a constant educative standpoint as well. Along with the specialization of medical doctors, healthcare providers may be interested in specializing themselves as well in a given specific therapeutic area. The set up of Healthcare 5.0 allows doctors to accumulate a growing number of cases to establish a business environment with the needed return on investment. The data helps with the knowledge growth through self learning by the study of historical cases but also sharing experience with other clinical experts treating similar patients. Healthcare 5.0’s concept of specialization based on data analytics seems inevitable. This type of innovation can be only grown with continuous access to new data. In 1970, Mission Control at NASA used physics-based modelling at Houston and Kennedy Space Center to test simulations and procedures for getting the

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ill-fated crew of Apollo 13 back to earth alive.62 That’s the mode we need to develop for each patient case. Such treatment pathway should be developed, however, by a community of experts that dedicated their research and experience towards the given health problem. Healthcare 5.0 will set up very advanced clinical centres in a fashion similar to space station-like hubs dedicated to the treatment of specific conditions. Each case treated should bring a further revision of current treatment standards to allow scenario development for future potential patients. Each patient needs to make his own decisions based on such scenario planning. The continuous development of skills and expertise thanks to data analytics can only be feasible when the team and infrastructure are set up. The medical professional should be complemented by data analytics experts and patient representatives. Such cross-functional teams may be easier partners for collaboration with developers of technologies designed for the specific therapeutic area to ensure a better understanding of the unmet medical needs and faster growth of innovation. It could also be an incentive for clinical experts who may resist the narrow specialization as well. The ability to drive collaborative efforts with manufacturers and other clinicians in the pursuit of continuous clinical aspirations leads to better treatment outcomes for specific health problems. Such cross-functional or rather this holistic approach is meant for the specialization of special care to be centred around clinical skills but also the quality of treatment outcomes. Healthcare 5.0 is the network of excellence centres developed based on the synergic effects of clinical and organizational knowledge with patient preferences in centre focus. In other words, data analytics at the hospital level do not only relate to the assessment of health technologies but also to healthcare organization with a focus on medical professionals and hospital organization. Efficiency gaps can be identified and eliminated along with growing an insightful experience in the treatment of a given health condition acquired. Today, there are examples that provide a relevant insight into the value of data analytics to increase the efficiency of healthcare organizations. The particular example is the approach taken by GE Healthcare that tries to develop a digital twin of the given organization to identify efficiency gains by providing suggestions for improvements in the model of care delivery to boost organizational performance.63 Therefore, the healthcare insurance system set up for specific therapeutic areas gives the opportunity to manage healthcare better and more cost-effectively. As far as remote treatment is concerned, there are numerous trends that can be observed, making such an approach for Healthcare 5.0 a real option as well. While digital health brings prevention tailored to the individual, specialized treatment must be delivered remotely while ensuring the best treatment outcomes. The key attribute of Healthcare 5.0 is that distance from the patient should not be considered an obstacle. Previously, the mindset of geographical borders prohibited opportunities of reaching out to the best clinical experts. With such innovations as robotic surgery, Healthcare 5.0 will make sure that

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the most innovative procedure performed by skilled doctors is available to every patient. How is it possible? One answer could be telesurgery. The very first procedure of such kind was conducted in 2001. It was a cholecystectomy performed on a patient located in New York by French surgeons in Strasbourg. At that time, the ZEUS Robotic Surgical System was used. It’s today’s successor to the da Vinci robots, which have a record of more than 7 million robotic procedures, according to data from March 2022 on the Intuitive company website.64 Although the majority of those are still performed on-site, there are examples of robotic procedures, such as colon operations and hernia repairs, led by surgeon Dr. Mehran Anvari in an entirely different part of the country, such as the one done from the console in St Joseph’s Hospital in Hamilton, Canada.65 Similarly, Dr. Tejas Patel of the Apex Heart Institute in Ahmedabad, Gujarat, India, successfully performed the first remote heart surgery on a patient who was lying on an operating table more than 30 km away in 2018.66 Finally, the first case of a very complex three-hour deep-brain stimulation implant procedure was conducted on a patient in Beijing by Dr. Ling Zhipei in Sanya City, China, using the 5G network.67 Such examples provide a lot of optimism as telesurgery can extend human capabilities beyond natural and physical boundaries. The realtime transmission of video with the digitalization of such robotic procedures will also accumulate more data that can lead to automation of certain parts of operations as well. With telerobotics-assisted surgery, it can likened to the metaphor of the self-driving car. As Greg Hager, a computer scientist at Johns Hopkins University, nicely put it, “surgical automation would progress much like the Autopilot software.”68 The collection of data is surely needed to eliminate safety concerns and improve the technology from an outcome standpoint before we can fully embark on remote surgical treatment in Healthcare 5.0. There is still some room for improvement. A study funded by the National Institutes of Health followed the results of more than 2400 women with cervical cancer and found almost double the risk of mortality after robotic minimally invasive vs. open surgery.69 So such findings help us to understand potential problems with the digital solutions and build our risk mitigation strategies. Four years after the operation, 9.1% of those who had minimally invasive surgery had died, compared with 5.3% of those who had open surgery. There are numerous hypotheses that need to be tested first for a better understanding of such findings and, consequently, improvement in the technologies. The specialization of healthcare is a natural consequence of technological advancement, but also of limited resources. For example, the World Health Organization’s Global Strategy on Human Resources for Health: Workforce 2030 report states that shortages in physicians, nurses and midwives could reach 9.9 million globally by 2030. On the other hand, there is a lot of optimism if one notes that already today there are patients willing to be treated by roboticassisted surgery (RAS).70 Across more than 1000 responders surveyed in the Middle East, 40.9% and 34.4% of respondents thought that RAS is more precise and faster than conventional surgical procedures, respectively.

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There are certain types of life-threatening conditions and services that need to be delivered in a specific time period, hence the geographical distribution of healthcare centres needs to be taken into account unless robotics can be utilized. There are examples that show how a small-scale local hospital care can be developed for urgent cases. The specialization of care is obviously not only about remote surgeries. Technological advancements also allow greater flexibility with treatment organization. We have seen it with the development of critical care with temporary hospitals for COVID-19 patients. Another example is the Philips Patient Monitoring Kit, a ready-to-go kit for critical care units that enables intensive care teams to rapidly increase the care capacity of their unit in an emergency, as well as the Philips BX100 Portable Biosensor, a remote vital signs measuring device that can be placed on the chest to measure respiratory rate, heart rate, posture, activity level and ambulation.71 There are also other technological advancements that allow materials to be delivered directly to patients. Already in 2021, drones were used to deliver medications.72 Zipline drones can travel 100 miles (160 km) round trip, fly autonomously and can carry close to four pounds of cargo. The drones can speed up to 80 miles (128 km/hour) an hour, even in bad weather conditions. The drone descends to a safe height and drops its package by parachute to a designated landing spot.73 Obviously, life-threatening conditions requiring emergency medical aid shall remain unchanged from the geographical and institutional standpoint with some technological advancements, as mentioned here. The point I am trying to convey here is that the future concept of hospital care in Healthcare 5.0 is centred around specialization and remote treatment. There are already examples today that indicate such an aspirational set up is not that far in the future, such as the Aimedis cross-border virtual hospital that stretch across three continents.74 It proudly acknowledges itself as the biggest hospital and healthcare space in the metaverse. This virtual healthcare system seamlessly merges the physical and virtual world while opening up a universe of possibilities and experiences for patients and medical professionals around the world. I believe that specialization will allow Healthcare 5.0 to be managed in a decentralized fashion and healthcare insurance bodies, as the institution that governs the monetary flow of specialized healthcare, are the future option funded by NFT ‘non-fungible token’ based on blockchain as described earlier. The economy of scale will allow not only better treatment but also a downturn in pricing dynamics and, consequently, more accessible healthcare. When healthcare providers render services to patients, they update their patients’ health data on a blockchain-enabled HIE. Because of these protected layers, patients can limit data access and choose which parts of their medical records to share with healthcare providers. In the era of Healthcare 5.0, the cloud environment will allow for data lakes to emerge and become easily available for the research and development purposes of different digital solutions. Eventually, it is not the digital solution per se but rather algorithms that VA will be searching for a given health problem.

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4.3.6 Healthcare 5.0 – safety first! In Healthcare 5.0, safety will be redefined and treated as a priority. The scope of the assessment will be adjusted to the health solution in question. In the predigital revolution era, regulatory requirements defined the scope of safety assessment. Specific pathways were established with respect to pharmaceuticals and medical devices. They were limited, though, to safety defined from a biological in the case of pharmaceuticals and a technical standpoint in the case of the latter. The growing field of digital health has introduced some regulatory changes or, more precisely, extended the rules applicable to medical devices on some types of software. For example, the Rule 11 of Annex VIII of the European Medical Device Regulation (MDR) states that if the software provides information used to make decisions towards a diagnosis or for treatment, then the software is Class IIa.75 In a similar fashion, the FDA considers software as a medical device if it is intended to treat, diagnose, cure, mitigate or prevent disease or other conditions. 35 They are categorized as Software as a Medical Device (SaMD). 36 An exemption is made if any computer program is integral to the hardware of a medical device and is then considered Software in a Medical Device (SiMD). Most SaMD are classified as Class II devices. Clinical decision support tools are regarded as medical devices depending on whether they are used for driving decisions not limited to the informing function.76 It is important to note that both the FDA and European bodies do not consider prevention seriously enough based on the review of the legislature in March 2022. While the FDA excludes solutions used for “maintaining or encouraging a healthy lifestyle,” in a similar fashion, the into following the European Medical Device Regulation (MDR) healthtech solutions for prognosis or preventing a disease or medical condition with minimal impact on the body (class I devices) have limited requirements indented for self certification.77 Generally speaking, regulatory safety consideration is limited to technical aspects. For example, the FDA guidance for de novo classification does not cover clinical aspects either. In the case of X2 Insulin Pumps the safety consideration were assesses against “electrical safety, electromagnetic compatibility, and radio frequency wireless safety testing,” 78 For medical devices that are pursuing 510(k), which is the requirement to demonstrate of substantial equivalence to another legally US-marketed device, there is even less emphasis on the need for any safety evaluation. The FDA explicitly allows for different technological characteristics as long as it “does not raise different questions of safety and effectiveness.” 79 Healthcare 5.0 re-establishes the definition of safety. With growing digital technologies and connected devices, it highlights the importance of data being the driver of solutions. It is about mitigating the risks of making an inaccurate clinical decision. First, the risk of lack of methodological rigor in the process of software development must be addressed. For instance, the developer might have chosen an aggregated sample to estimate accuracy instead of specific subgroups of interests defined by the disease severity or likelihood of treatment

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response. So although the total sample accuracy may be acceptable, it may differ across subgroups and consequently lead to a misdiagnosis or simply inaccurate clinical judgement. Second, there is risk of bias. An example in this case could be the well-disputed case of the Amazon hiring support tool that tended to select men more often than women.80 There were other examples of bias in AI risk assessment used in the justice system, proving the tendency of black defendants being judged as too high risk for criminal re-offence. There is a risk of bias in the Framingham Heart Study, which mostly involved white patients, while it has been proven that the associations between risk factors and the presence of atherosclerotic disease differ between race/ethnic groups.81 The same goes to ML algorithms used to diagnose melanoma that are trained mainly on white populations.82 These examples indicate the need for a rigorous mechanism of validation. So far, the efforts were rather limited in that respect. A study by Ebrahimian and colleagues reviewed 118 imaging-based AI/ML algorithms that were processed by the FDA.83 Seventeen (17/118) did not post any validation claims or data. Just 9/118 had validation dataset sizes of over 1000 patients. Healthcare 5.0 moves to the next level. The underlying rationale is that different health technologies are assessed similarly as long as they aim to achieve the same health outcome. This is also the reason for me to start the discussion on reimbursement and pricing, where I outline the vision of Healthcare 5.0 focused on payment by outcome. The broadness of the safety assessment is given by the definition of unmet needs the given solution tries to address and, consequently, the choice of outcome it chooses. The composition of the technology, whether it is biological, technical or software, should determine, of course, the evaluation from the user standpoint. Still, the scope of the assessment in terms of risks of wrong clinical judgement should be related to the exact decision to be made based on the use of the technology in question. The methodological approach to the validation can be adjusted to the nature of the product, but rigour and depth should be purely risk dependent in the context of unmet needs and outcomes in question. Healthcare 5.0 must protect the patient from misuse or wrong use. One in ten patients in OECD countries is unnecessarily harmed at the point of care,84 which surely does not help to build cognitive trust and satisfaction with healthcare system. More than 10% of hospital expenditure goes to correcting preventable medical mistakes or infections that people catch in hospitals across a range of OECD countries.84 These data are clear evidence of a shortfall of paternalistic system. The governmental failure of the healthcare system ocurred before the pandemic. Healthcare 5.0 introduces a broadness in safety assessment to the risks in terms of clinical judgement to capture potential biases due to skewed data or misjudgement due to limited data. It ensures the continuous improvement of already launched technologies. Developers are obliged to update their system with the availability of new data. This is the total product lifecycle (TPLC) regulatory approach that prearrrange with developers future steps regarding the product updates while

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ensuring effctive safeguiards. This also means that company resources must allow for such business continuation to happen. It is in line with the FDA’s recent development, the Software Pre-Cert Program that aims at a firm’s capability to respond to real-world performance. The FDA intends to work with pre-certified firms to quickly and effectively address software issues.85 “FDA acknowledged that the faster rate of development and potential for innovation in software-based products. It is important for public health to address these distinctive aspects of digital health technology – its clinical promise, unique user interface, ability to facilitate patient engagement with the developer, and compressed commercial cycle of new product introductions – while ensuring that existing standards of safety and effectiveness are met or exceeded.” 86 Healthcare 5.0 is using that FDA approach, especially with respect to the FDA’s five Excellence Principles established within the Pre-Cert Program, such as Product Quality, Patient Safety, Clinical Responsibility, Cybersecurity Responsibility and Proactive Culture. Still, we need to acknowledge the fact that any requests for more data will introduce even more risk. As I mentioned earlier, some disturbing examples such as the blackmailing of patients with the request of Bitcoin transfers as was the case of the Finnish mentally ill patients using Vastaamo.87 The new data security and data privacy laws, such as GDPR, govern such misuse of data. Such legislation should develop even more in Healthcare 5.0. Why? Meta (formerly Facebook) faced a number of lawsuits regarding the collection of biometric data in the period of 2010–2011 without informed consent.88 It was due to using a face-tagging technique without the permission of users.89 Healthcare is the most vulnerable sector to cyberattacks. According to recent research, a single health record is worth up to US$250 on the black market.90 It is estimated that every third cyberattack relates to healthcare.91 In the United States alone, there were, on average, 59 health data breaches per month in 2021, a record-high rate of phishing and ransomware attacks. Distributed denial-of-service (DDoS) attacks are connected to the Internet traffic in a company’s network. Cybercriminals try to overload it with vast amounts of fake data so the system fails to function. Ransom attacks in 2018 led to the cancellation of more than 19,000 appointments in the UK, with the Department of Health estimating £92 million in medical rescheduling costs and upgrading IT systems.92 Obviously, anonymization of patient data decreases risk, but it does undermine efforts for validation? If patient sociodemographics or historical data are left unknown to AI developers, how may we detect bias? There are some solutions that can be utilized in Healthcare 5.0 to meet that challenge. For example, Federal analytics combines information from distributed datasets without housing it in one central location, allowing central development and local use. The application of federated learning (FL), which is already available, can predict mortality and hospital stay by clustering the local data into a clinically homogenous group of patients of similar diagnoses and geographical location. In the learning process, the data was kept local at hospitals with locally computed results aggregated

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on a server.93 Healthcare 5.0’s use of FL could move data to the algorithm instead of moving the algorithm to the data – not only for learning but eventually for real-time analytics as well. Among other techniques already tested, secure multi-party computing (SMPC) allows data to be analysed while encrypted, keeping it in its original location with the algorithm being delivered to data owner.94 Another interesting advancement is Irish  Oblivious Software, which provides the opportunity of “trusted execution environments.” Before the datasets are sent to the analysis, it is encrypted first by their owner. The data is completely removed after the task has been performed.95 Such technologies like FL or SMPC allow greater utilization but also constant improvements with the flow of new data with new users. Combined with the convergence of data lakes and real-time data analytics, these technologies are expected to drive improvements in algorithms and subsequently respond successfully to the demands of the process of continuous validation. In summary, in Healthcare 5.0, the responsibility for safety assessment is shifted towards the regulatory body. It should be conducted from the perspective of technology while adopting the view of unmet medical needs. It is related to objective criteria defined by the risks related to the utilization of given technology and also defined from the perspective of outcomes desired by end users. It may be, therefore, be further assumed that safety in terms of healthy risk (fatal incidence) and data security are outside of the reimbursement process and belong to the responsibility of the regulatory agency. Not only that, safety assessment in Healthcare 5.0 is much broader compared to earlier healthcare systems, also covering the validation related to the accuracy of diagnosis and/or bias in the effectiveness of treatment as well as and other assessments of clinical misjudgement. Overall, safety should be further defined from a greater standpoint than only side effects. In the digital era, data security is an important component. It should not be limited to cybersecurity, but also the risks related to misuse of the technology. Since it does not relate specifically to unmet needs, it can be further included in the regulatory assessment as safety component. All in all, the link between regulatory and reimbursement is very close with respect to safety. It should be acknowledged and followed with more collaborative efforts. Although from the value assessment perspective, the technologies should be treated in the integrated healthcare model as they are to be utilized in the clinical practice. Therefore, the risk benefit analysis should become part of the pricing strategy and not the safety assessment. The outcome of the latter must be considered as “instructions for use,” especially in terms of both digital solutions and medical devices.

4.3.7 Healthcare 5.0 – financing model A recent HIMSS Market Intelligence survey found that nearly one in two respondents between the ages of 18 and 56 preferred seeing their primary care

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provider via video after the COVID-19 pandemic. I provide this example on purpose. Many journalists that interview me about digital health actually mean just telemedicine. For many observers, the establishment of remote communication with the patient is sufficient to drive change in the healthcare system in the post-pandemic era. The fact remains that the growing acceptance towards the adoption of telemedicine is a nice move, but it is just the first step. Most importantly, it is not a sufficient step to fully embrace the opportunities of digital transformation. In the course of the previous chapters, I elaborated on how data is changing healthcare patterns. We should look for ways to prevent and treat diseases with a different mindset of individual sovereignty and a holistic approach to health. Does it mean that with such a different mindset we should reorganize the financing model of the healthcare system too? Data has empowered us with a better understanding regarding diagnosing and managing diseases, but that’s what we did even in the pre-digital era. What is new, however, is that data awakens a better understanding of the importance of preventive measures. In fact, the era of data has holistically extended the borders of the healthcare system. It is not only about how to treat but also how to prevent. Adopting the axiom of a holistic definition of health, we can redefine our health outcomes. It is not only treatment success, but also the success of prevention. It is feasible as we can build the connection between our daily habits, disease onset and disease consequences. The digital health solutions give us the power to quantify that connection, too. How shall we transform this new broader and holistic concept of health into the rules of resource allocation? With the focus on prevention, the Healthcare 5.0 financing model is set to save money on treatment by investing the money into the detection of the risks of future health problems. Consequently, the model is to introduce a system of incentives for the generation of financial and non-financial savings as the result of prevention and prediction. Many years ago, I had a pleasure to work for a pharmaceutical company that developed a treatment for alcohol dependency. I must admit, it was not an easy journey to explain the value of it from the public healthcare budget holders’ perspective. Since addiction does not incur direct healthcare costs, it is not regarded as a disease, and consequently, it is not considered a problem to be solved by the healthcare system. Given the limited data availabile, the model of overconsumption of alcohol was difficult to justify from the narrow perspective of the healthcare system. At a negotiation meeting with the Ministry of Health delegates in one of the EU countries, I heard from a governmental official, “why on earth should we reimburse lifestyle products when we have patients dying from cancer?” Actually, this question fully exemplifies the underestimation of the role of prevention as the potential source of savings for the healthcare system. If the decision-maker wore a hat of holistic perspective instead, they would notice the long list of sources of potential savings and consequently consider alcohol overconsumption as the risk factor for future burdensome health problems. According to the report of the US Centers for Disease Control and Prevention 2015, it was estimated that alcohol consumption costs the United States $25 billion per year

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as the result of crime-related activity, $13 billion for collisions and $28 billion for healthcare. On top of it, it was calculated that the reduction in work productivity added $179 billion.96 Why are these numbers are important? Adopting the holistic perspective, one could potentially identify the unmet medical needs of the disease and promote solutions that prevent the most burdensome consequences of health depravations. In the case of alcohol overconsumption, an awareness campaign of the impact of addiction on work performance may be important. Are there any digital solutions for that? Actually, there is a growing body regarding cognitive behavioural therapies (CBT) for substance abuse.97 There are even randomized controlled studies indicating how CBT can improve work productivity being delivered by teleservices.98 It means that not only is there a potential solution, but its value can also be assessed from the potential of generation of savings. Surely there are many shortcuts I am using in this example. CBT cannot be administrated on its own. There is a need for a prevention program from a holistic perspective to ensure synergic effects of different treatment modalities. My point is to justify the benefits of the adoption of a holistic approach to health in the digital era. With that simplified example, I wanted to illustrate a new mode of thinking about healthcare financing. It is about gathering the full insight into the disease burden and identify the unmet medical needs, which subsequently allow us to promote the implementation of those technological solutions that prevent the most burdensome disease consequences. Therefore, Healthcare 5.0 designs its healthcare financing model by incentive: the generation of financial or non-financial savings as the result of prevention and prediction. It happens thanks to both governing rules outlined earlier. First, equal access to information allows us not only to inform individuals of diseases but also allows reimbursement authorities to gather the knowledge to be able to define the burden of disease and identify the unmet medical needs accordingly. Why should we pay for the technology if it does not solve the problem that affects the patient most? Second, the adoption of patient-centric outcomes allows us to promote the financing of the outcomes not the use of health technologies per se. Why should we pay for the pill, even a digital pill, if we are unsure of its impact on disease consequences? Shouldn’t we pay for the outcome instead? Let’s focus on each of these two separately.

4.3.8 Healthcare 5.0 – identifying unmet medical needs First and foremost, the existence of an organized form of a healthcare system is pointless if it can’t be centred around unmet needs. We cannot allow the wasting resources for manufacturing of goods nobody is interested in using. Hence, medical technology production must focus on unmet needs. The innovation should not be defined by its sophistication, but the extent it can extend the life expectancy or improve quality of life. I was bewildered many times by hearing my medical colleagues’ enthusiasm with new modes of operation of new pharmaceutical agents or medical devices. The bottom line is that it does not matter as long as it can be translated into a meaningful difference from the patient’s

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perspective. How the availability of data can change the dynamics is illustrated with yet another example. Since its launch in 2015, the mPower app has enrolled over 10,000 participants, making it the largest Parkinson’s study (yes, study!) in history – with 93% of participants never having taken part in any kind of research before. The app helps researchers better understand Parkinson’s disease by using the gyroscope and other iPhone features to measure dexterity, balance, gait and memory. Researchers have gained greater insight into the factors that make symptoms better or worse, such as sleep, exercise and mood.99 The ultimate objective of the healthcare financing model must be to investigate the burden of each health condition and list all unmet medical needs. In other words, reimbursement bodies in close collaboration with regulatory bodies should provide a catalogue of unmet needs for developers to be able to choose from and work their way to plan both evidence generation and a commercial roadmap. The catalogue needs to be approved first by representative of the patients. This brings me into another principle of the healthcare financing model of Healthcare 5.0, which is how to ensure outcome-based payment.

4.3.9 Healthcare 5.0 – ensuring an outcome-based payment model The key benefit of a patient-centric healthcare system is data-driven and, therefore, more insightful decision-making. Giving the power back to patients will allow them to be more vocal with their needs and hopefully more motivated to engage themselves in the regulatory and reimbursement processes, seeing their input being significantly influential on the outcomes. For that to happen, it is a prerequisite to establish the democratic pathways to collect patient feedback. In a data-driven healthcare system, it is as easy as it has already been used for the collection of customer satisfaction used in every field of retail. In the spirit of democratic choices, it should be multiple not just one platform. Healthcare 5.0 introduces a new requirement for reimbursement bodies to establish and continuously update the burden of each disease in their area of interest and set up catalogues of unmet patient needs. In Healthcare 5.0, the reimbursement authorities should be a therapeutically specialized accredited non-profit organization that provides global support for manufacturers for a reasonable fee. It is similar to the notifying bodies that existed in the field of medical devices. The key difference is, however, that the reimbursement bodies should build their reputation by knowledge acquired thanks to realworld evidence and high-quality expertise. It is the job of these agencies to collect relevant real-world data in the field of their therapeutic specialization so they can offer appropriate services for technology developers with respect to identifying key drivers of burden of disease or unmet patient needs, with the latter being available in the open domain in order to foster the growth of innovation. The most important is, however, to continuously collect real-world data about individuals’ experiences with any health technologies within specific therapeutic

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areas. In fact, the benefit is twofold; first, it is the real-life assessment of effectiveness, safety and usability that should inform the process of reimbursement and pricing. Second, up-to-date patient satisfaction data is a way to understand what can still be improved in how patients are treated. In other words, the continuous collection of information about patient satisfaction is the key consideration for the development of the catalogue of unmet needs. Epidemiological and demographic data are to be added to the list of data sources for the continuous assessment of unmet patient needs. Regulatory and reimbursement joint responsibility in this respect is to ensure not only the collection and analysis of the data but also to initiate an open dialogue with patients. That, again, should be extremely easy to be introduced in the digital era with the publication of the catalogues of unmet needs for anyone with a given indication of being caregiver or being clinician would be able to provide his comments and suggestions. A significant amount of similar comments could easily translate into a specific update of a drafted list before it becomes final. In the era of natural language processing, the categorization of collected feedback together with the search through social media and scientific journals should not be considered an obstacle for the reimbursement agency to lead that process. All in all, collaboration in the era of digital health means more than providing the comment upon request. It really means constant open dialogue as well as a proactive search for feedback from end users. The reimbursement bodies have to be, therefore, very well equipped with data-science skills, as an update to their catalogue of unmet needs must be justified with robust evidence. The formal process of appeal must be another route for patients to become critically vocal and serve as a guarantee for the democratization of decision-making when it comes to potential discrepancies across different unmet needs catalogues. Surely, a single voice may not be considered sufficient for a revolution. A significant percentage of dissatisfied patients or end users must sign in for such an appeal process to be initiated. Again in the digital era, the collection of voices should not be considered an obstacle for such mechanism to be introduced. The list should be updated anytime new technology is reimbursed and significant market uptake has been achieved for patients to be able to revise their experience. How will collaboration ensure the understanding of unmet needs?

4.3.10  Healthcare 5.0 – the role of patients’ community Stikker, one of the founders of De Digitale Stad (DDS), was among others who claimed that technology is never neutral and that there was always an intention in design. This is even more applicable to digital health technologies … but the intention should be to meet the expectations of end users. This brings us to another important point of discussion. Who owns the power? Today, tech giants dominate the digital world and have enormous power to use our data. How can the Internet be fixed again and become a truly democratic public infrastructure? That’s why I define my healthcare based on two main axioms,

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individual sovereignty and a holistic approach to life in order to ensure healthcare is patient-centric. Each patient will receive access to a community of patients with similar risk profiles and, in case of diseases, similar health problems. It is both motivational support and the exchange of information and experiences that will allow the patient to feel not alone, but one of many in his digital global community and … create a list of unmet needs. For those who are overwhelmed with such an idea of listing of all unmet needs, let me remind you that we already have ICD codes to categorize all clinical ­conditions. Therefore, the development of an additional catalogue on such a basis from a patient or end user perspective should not encounter a bigger challenge. It is, however, up to the technology developer to define and prove the outcome his solution delivers by addressing one or several needs from the catalogue. Data analytics allow the prediction of health risks and disease consequences. Hence, the definition of outcomes in an evidence-based manner should be relatively easy. Having access to data means the pathway towards the outcome selection should not be driven as it was in the past with the choice of a comparative treatment option, as it narrows the perspective. Instead, I strongly believe it should start with a “what if ” scenario. Therefore, Healthcare 5.0 replaces the concept of comparator with the concept of opportunity cost. It is feasible by the growing availability of real-world data that have never been used in the decision-making process in the healthcare sector. It is a clear departure from the pre-digital transformation era when we focused on the incremental treatment gain of a given treatment vs. alternative treatment options. I believe that the value of health technology in the era of data should be defined from the standpoint of opportunity cost that is understood as a forgone scenario. It requires looking beyond a specific technology to the full treatment pathway instead. Maybe this is the same as what we intend with the definition of comparator but different? In conclusion, the Healthcare 5.0 model of financing is based on the premise of generating financial or non-financial savings with the adoption of the mode of prevention. To achieve that, it promotes the idea of using the opportunities of the era of data to understand better the burden of disease and secure the financing; not the technologies but outcomes reached defined from the catalogue of unmet needs. The opportunity costs, not a choice of specific comparator, require the adoption of a holistic view on the patient’s journey too. This brings us to the next step, which is how to define the value assessment in order to address the reimbursement opportunity and pricing of health technologies in Healthcare 5.0.

4.3.11 Healthcare 5.0 – pricing and reimbursement of medical procedures The general framework for the pricing and reimbursement in Healthcare 5.0 is based on the value for money principle as it was in the pre-digital era. Both processes are, however, separated and driven by different objectives. With the dawn

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of patient-centric healthcare in the digital era, reimbursement is defined from the perspective of unmet needs, while pricing is defined from the perspective of willingness to pay. The key success factor for the adoption of innovation that meets unmet treatment needs is the transparent and open dialog between manufacturers and reimbursement bodies. The latter must act as an advisor and supporter for the former to ensure that given health solutions target the right patients, meets the patients’ needs and ensure efficiency gains against the standard of care. The separation of reimbursement and pricing allows for that to happen. Let’s recall how the financing model of Healthcare 5.0 was built around value designed from the perspective of unmet needs and financing designed as the outcome-based payment. For reimbursement, it translates into the question of whether the technology can prove it delivers outcomes that meet unmet treatment needs. Therefore, the understanding of the burden of disease should inform the meaningful outcome a new technology will deliver. How do we measure success in achieving the given outcome from patient’s perspective? More precisely, how do we ensure that the innovation will impact quality of life, life expectancy or efficiency gains for disease management? How do we define the forgone scenario as the opportunity cost for a proposed solution? What kind of aspects should be taken into consideration apart from safety? Do we need to focus on the adaptivity of innovation to clinical practice as well? In each of these domains, the focus on patient-centric outcomes requires that a new treatment foremost should aim to meet or even exceed patient expectations. The answer is simple – provide the data and, more specifically, the quality analysis of the burden of disease and insights into patient preferences. Therefore, the reimbursement bodies with data accessibility and capabilities to study patient preferences are the best partner of manufacturers in the process of defining the outcomes to be target for reimbursed based on the catalogue of unmet needs. I have difficulty denying the rationale provided by many of my colleagues for the choice of the well-established quality adjusted life years (QALY) matrix. What is the value of health technology if it cannot be quantified in terms of incremental impact on life quality and life expectancy? There is surely no contradiction between QALY and an outcome-based approach in the mode of prevention. QALY is the only way we measure the success of a given treatment modality. The latter has to be defined as specific outcome though that addresses specific unmet needs. Still, there might be instances when the choice is between shorter life expectancy and better quality of life. Let’s think about the example of a patient suffering from prostate cancer who is set to make a decision between sexual dysfunction for the rest of his life or shorter life without that problem. His choice may be different from that of the medical professionals. Thus, patient preferences are of such great importance. It would be biased to define the access to quality of life improvement, even the incremental impact of life expectancy is negligible. Thus, the value-assessment framework needs to provide evidence

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fitted to the unmet needs. Informed patients should be treated as informed customers; therefore, it is so important in the open healthcare market to ensure patient satisfaction with treatment. This changes the dynamic in many instances. It does hopefully motivate the developers to invest more in understanding how a specific product will meet the unmet needs and lead eventually to the satisfaction of an informed patient. The forgone scenario analysis should not be limited to the clinical impact. It needs to be complemented with the assessment of organizational impact related to the time of procedure as well as the required changes in the clinical pathway. Why? The rationale for the assessment of unmet needs within the field of efficiency must be understood from two perspectives. First, it is about the time gain needed for treatment. Thanks to the innovation bringing greater treatment effect, fewer side effects or shorter duration of treatment, the opportunity cost may be expected. It is the release of some resources that can be made available for treatment of other conditions. Second, it is about the pure time factor related to the fact that innovation can provide such efficiency gains, as mentioned earlier, that will allow shorter waiting times in the result, or realizing some unused resources. In other words, innovation may release new resources. Time is the additional currency to the earlier QALY value assessment. On top of the quality of life and life expectancy improvement, the owner of the new technology will have to provide evidence for efficiency gains. In Healthcare 5.0, quality of life, life expectancy and efficiency gains are the domains to be used for the definition of the outcome the technology claims to achieve. Such methodological approaches as Multiphase Optimization Strategy (MOST) and Sequential Multiple Assignment Randomized Trial (SMART) are to be utilized for the assessment of the correctness with the choice of the outcome and the success of the technology in achieving it. They are already proposed methodologies in the German guidelines for reimbursement submission of digital health applications (DiGA – in German: “Digitale Gesundheitsanwendungen”) established in 2019.100 MOST is born on the principles generated from engineering that emphasize efficiency. MOST incorporates the standard Randomised controlled trials, but before the RCT is undertaken, it also includes a principled method for identifying which components are active in an intervention and which doses of each component lead to the best outcomes. SMART, on the other hand, provides an empirical basis for an adaptive approach towards defining the right outcome. The end goal of the SMART approach is the development of evidence-based integral elements of the intervention strategy, which are then subsequently evaluated in the form of an RCT trial. Both approaches have already been presented in the first chapter of this book. In Healthcare 5.0, reimbursement is designed as the process of certification that grants access to the healthcare market by the guarantee that it meets specific predefined unmet needs and delivers pre-agreed outcomes. Defining reimbursement from the perspective of outcomes is getting more and more recognition.

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For example, recent developments in the implementation of CART-T therapies prove that. Many jurisdictions, such as France, Italy or Germany, have established registries that monitor the outcomes of treatment.101 Upon specific agreements, public payers either pay or simply discount the price of technology depending on the mortality data as well the rate of remission. Some authorities, like France grant reimbursement during a limited time period to ensure adjustment based on the availability of new data. Such examples are promising and they provide needed experiences for future outcome-based agreements. Still, Healthcare 5.0 does move a step ahead by adopting the axiom of individual sovereignty and holistic health outcomes. As a result of it, the unmet needs catalogue is born as a new tool to be utilized for the purpose of defining the outcome targeted by a given new health solution. It has to be underlined that both health or nonhealth technology are eligible for reimbursement as long as the available evidence proves how they help patients to reach certain health goals in a meaningful way. A clinical indication is not a driver of reimbursement, but truly “what it does” for patients. That’s the huge difference compared to pre-digital era. The data opens new boxes of knowledge about individual preferences defining new types of health outcomes and sets limitless boundaries of the healthcare system, defining new ways to reach them. For unmet needs to be used as guiding principle, we need to establish the categorization and standardization of processes in the healthcare sector. It is a very important responsibility of reimbursement agencies to develop and control the implementation of standards of prevention and treatment. In Healthcare 5.0, that role requires the strong collaboration with healthcare insurance providers as they build data warehouses due to the connectivity to patients’ digital tools (discussed more in the next chapter). Such standards will define the exact placement of the new technology in the health journey that meet unmet medical needs and ensure the safe and efficient use of innovation. The role of reimbursement agency is, therefore, to make such procedures evidence-based but also implementable. Early dialog with manufacturers is of great value in that respect, as it helps to define the unmet medical needs, alternative treatment pathway to be replaced or advanced with innovation and also the set of prerequisites needed for easy implementation. Only when the reimbursement is granted can the pricing negotiations be initiated. I have been extremely surprised many times to read that the evidence submitted was not sufficient to allow the judgement about a given health technology. Who’s fault is this? Why do we blame the manufacturer? It’s a poker game when we do not know what kind of cards the other side holds. The game is, however, much more risky than poker. It is about the patient’s life. The collaboration is the responsibility of all parties to ensure timely patient decision-making based on a sufficient evidence basis. Open dialog should not be, therefore, optional but a must. There is nothing more important than communication to ensure technological advancement is used to cure unmet needs.

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Therefore, in healthcare 5.0, pricing negotiations are handled by the representative of patients for each specific healthcare insurance. Apart from the importance of the outcomes targeted by the technology and the extent the technology is successful in reaching that outcome, other aspects may be important in the pricing discussions not present in the course of reimbursement. This is the reason why it is important to introduce safety assessment into the pricing discussion in order to establish trade-off scenarios or, rather, risk-benefit scenarios. The way it can be achieved is to define discrete choice experiment studies or willingness to pay studies to understand how much end users are willing to pay for the outcome delivered by a given innovation considering its safety profile. Since it is not a binary choice, the trade-off approach is the preferred option. A patient may be interested in achieving specific outcomes, but there are other aspects they may consider in setting the acceptable price tag. An example could be user-friendliness. I have chosen that particular aspect as it is a complete game changer for the healthcare system. Even if we make it accessible to a patient at the right time in a safe and effective manner, it won’t generate any success if the patient simply does not feel like using it. There are studies that look into how to design e-health to meet the expectations of patients. One example is a study across 222 participants to verify 12 design variables (e.g., navigational depth, reading level and use of navigational lists) on the usability of eHealth application websites for those with and without severe mental illness (SMI).102 The number of screens users needed to navigate to find the desired content had the greatest influence on usability (ability to find information) and efficiency (time to find information). In Healthcare 5.0, the design of health technology will be an important criterion in the process of certification before market entry. The end-user experiences must be tested in real-life settings prior to product lunch. User-friendliness is a key concept on the pathway to ensure usability and, therefore, effective health monitoring. Once the power of clinical supervision over our health is replaced with trusted partnership, there is a new responsibility of manufacturers that stretches beyond the business objective. It is a new mission for the latter to develop products that produce an effect that goes beyond superficial satisfaction and contributes to better health. Hence, user-friendliness has a new, deeper meaning. It has an influence on patient adherence. In fact, it is again about a holistic approach to healthcare, which I discussed earlier. The importance of functionality of health solutions connects directly to how we as patients derive satisfaction from simply using them, and both directly as well as indirectly (from pure user pleasure) will derive health outcomes. In all variants of welfarist economics, however, not only is non-utility information neglected. Some experts have discussed that issues pointing to current practices of extra-welfarism that do not consider “process utility,” i.e., the satisfaction that individuals obtain from the service itself, such as the readiness of assistance with low waiting time, the kindness in the care received, etc.103 A growing body

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of literature, both in the general economics literature as well as in the health economics literature, uses the term of procedural or process utility. Such considerations of procedural utility fit the welfarist economic framework well and point in the direction of the concept of consequentialism. The idea is to consider consequences from the utility consequences standpoint rooted in the preferences of individuals. This theoretical deliberation indicates again that test user-friendliness can be rooted in welfarism. How will Healthcare 5.0 implement it? There is today a growing body of evidence of the utilization of discrete choice experiment studies to test patient preferences. This is the type of robust approach that will become obligatory on the list of preliminary data collection obligations for manufacturers. Healthcare 5.0 will allow individuals to use only digital solutions with proven evidence regarding safety, effectiveness and user-friendliness. The reviews of other customers, in a similar fashion to how Amazon publishes its reviews, will be implemented as a requirement by nonprofit academic centres. In summary, Healthcare 5.0 defines the pricing and reimbursement process from the two perspectives of the unmet needs of patients and efficiency gains for the healthcare organization. The reimbursement process understood that way allows us to focus on identifying value drivers for given technology and approach the total value from the standpoint outcomes are designed to reach. The price should be, on the other hand, defined by the willingness to pay for a given risk– benefit ratio. The added value must be defined from the perspective of a simple question: how far is the new solution better than the forgone treatment option? Only when the answer to that question is fully quantified can patient satisfaction be assessed as a customer, i.e., willingness to pay. The separation of reimbursement and pricing allows for that to happen.

4.3.12 Healthcare 5.0 – healthcare insurance In October 2021, the first ever race of autonomous cars happened at Indianapolis Motor Speedway. The winner won a $1 million prize,104 driving of speeds averaging 218 km per hour. It was a vehicle steered by an algorithm developed by the Technical University of Munich (TUM). How could it happen? The car sensors received the data to allow the on-board computer in a fraction of a second to make predictions about where other vehicles are moving so that its decisions can be passed on to the steering and braking systems as driving commands. This example shows the power of data. A similar principle of decision-making based on the prediction is to be applied in Healthcare 5.0. Instead of racing cars, it introduces the VA that is fuelled with data generated by wearables, implanted and digestible sensors that are used to monitor our health parameters and vital signs. The difference between both approaches, autonomous cars and healthcare 5.0, is time. In the latter, it is defined by the lifespan with the scope of prediction long

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enough and holistic enough to support decisions of individuals so he or she can achieve the best health today and for a lifetime. Once the mode of operation is centred around prediction, it influences the financing mechanisms as well. Healthcare 5.0 is not only about the distribution of the global budget across all beneficiaries at a given moment in time with the solidarity principle. It is also about the planning of the optimal allocation of financial resources at the patient level throughout an individual’s lifespan. How do we get there? Obviously, the prerequisite is to make patients and healthcare insurers aware of the risks of disease onset, thanks to the assessment of genetic predisposition as well as individual preferences towards healthy habits. The mode of health prevention requires, therefore, a better understanding of genetic risks and constant health monitoring, including individual behaviours such as diet, sleep, stress levels, physical activity and any other influencers of health outcomes. The more we know, the greater the chances of successful prevention. Still, I guess some readers may be against such idea of too much insight. Not everyone wants to know his or her disease risks early in his or her life. What’s the importance of knowing? I can illustrate with very personal experience. I am among others who suffer from a rare condition. It took me four years and three unnecessary surgical operations, along with a lot of stress and emotional burden for myself and my parents, to find out what was wrong with me. I can’t emphasize enough how it impacted my entire life with many longterm consequences. I am not talking about the diagnosis but the lack of diagnosis and stress of the unknown. The stress caused by the scarcity of information. The stress actually of not knowing how long I am going to live and how I am going to live, etc. It is always the case that our personal experiences are the strongest motivators. I guess it is the case here, too. I was truly finally happy and my emotional suffering ended when I found out all the information about my condition, when I knew exactly how it happened and how I should take care of myself. When I finally had a diagnosis I could even connect with others with similar problems so I would not feel lonely. It helped me to understand I am not negatively different but actually positively different and, most importantly, that I will be fine as others are fine too. The end of not knowing was a huge relief. I truly have felt what prediction and prevention really do. They end information asymmetry, and most of the time, unnecessary costs for the healthcare system, patients and patients’ families. In the age of digital transformation, such challenges present themselves differently. As I explained earlier with multiple examples throughout this book, information asymmetry is to be replaced with the era of access to information. AI brings new opportunities as we can become aware of the real risks upfront … and limit the stress of not knowing. Although in Healthcare 5.0, the assessment of the baseline genetic predisposition is compulsory, it is still up to the patient to decide how much of it should be disclosed to him or her. The healthcare insurance providers are allowed, however, to possess the full knowledge. There would

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be no risk of denial of access to a healthcare plan for that particular patient, but a full responsibility to plan the patient’s journey based on the prediction and prevention mode. In Healthcare 5.0, the baseline assessment is used to define the scope of further diagnosis, prevention and regular check-ups. It allows the healthcare insurance provider to establish the health indicators needed to be reached on a specific individual’s life milestones (for example, every three, five or ten years) in two alternative scenarios, either the highest life expectancy or highest life quality. The health plan proposed to the patient is priced based on the assumption of the patient’s full compliance with received health recommendations. The healthcare insurance provider also suggests the optimal design of the VA design to ensure the best navigation via health engine and repository towards a healthy lifestyle as well as the level of desired health recommendations.

4.3.13  Healthcare 5.0 – digital health accounts In Healthcare 5.0, each patient will set up his own digital health account in a similar fashion to his savings account. Instead of a financial bank, it is, a bank of health data, including genetic risk assessment, records of any healthcare services utilization, lab tests, and most importantly, behavioural data. The patient should decide himself who has access to his or her bank. It is the patient who chooses a healthcare insurance company. How is it done in an objective and non-profit-oriented manner that ensures only the interests of the individual in question are considered? I believe it is the only role for the national Ministry of Health to take part in the new healthcare system. Financed based on a general tax system, it is the role of the government to help each citizen choose from a certified list of providers for the performance of genetic tests as well as certified academic centres for health preference assessment. The responsibility also covers support with the establishment of the digital health account and the choice of appropriate health insurance plan provider. The choice of the Ministry of Health should be linked to the jurisdiction of the choice for the income tax payment to make it easier for the individual to organize his entrance to the healthcare system. In healthcare 5.0, the individual digital health account is to be set up from the moment of adolescence. Parents should pay for the healthcare insurance for their children. The predictive analytics based on the baseline risks defined by genetic testing could not only establish the risk profile and define both the risk of incidence of specific diseases and risk of premature death, but also help to plan the profile of future medical needs. In Healthcare 5.0, not only is genetic predisposition the driver of the healthcare financing model but also patient preference towards healthy habits, dietary likes and dislikes, as well as an overall life plan. Therefore, each citizen is obliged to complete a short course about disease prevention, dietary habits and also digital literacy. The latter is needed for everyone

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to individually perform the number of tests to allow them to ensure that the health engine and Google health repository are adjusted to specific needs. Ideally, such a course should be part of the educational program for everyone before he or she reaches adolescence. It is up to the Ministry of Health to decide how to verify whether the basics of healthcare knowledge have been achieved. Additionally, however, in Healthcare 5.0, it is important to assess everyone’s preferences towards the governing principles of Healthcare 5.0, e.g., the equity of access to information and holistic definition of health. For example, everyone may have different attitudes towards the trade-off between longer life expectancy in mediocre quality of life vs. shorter life expectancy with high quality of life. It is, therefore, imperative for the Ministry of Health to help every citizen assess his disease risk preference profile. It should be done in a scientific rigorous manner with discrete choice experiment methods by the research institute commissioned for such work. Such transparency with data gathering and sharing might be considered unethical. Full access to patient’s health records by the healthcare insurance provider may be perceived as too much access and an intrusion into the patient’s privacy. One may fear that data are used against the patient’s will and/or patient’s benefits. In Healthcare 5.0, healthcare insurance providers are non-profit organizations and there are three major new policies implemented that ensure the use of data can be used in favour of patients. In the first new policy, healthcare insurance provider has an obligation to justify the price of a healthcare plan for a given individual with data-driven evidence. There are at least three data sources used to estimate the attractive value-based pricing for potential new customers. First, utilizing the governing rule of equal access to information, Healthcare 5.0 allows healthcare insurance providers to use anonymized data of their clients to build the prediction models that forecast the total lifetime medical costs for a given individual based on the genetic predisposition and patient’s characteristics (more about it in the second policy rule). Second, the analysis provided by the reimbursement agencies with respect to the assessment of lifespan for specific genetic predisposition and health outcomes’ projection for a given condition utilizes available innovative preventive and treatment options. Third, the assessment of specific individual preferences towards health monitoring and healthy habits modifiers from an individual holistic perspective (disease risk preference profile mentioned earlier) are to be taken into consideration. If the proposed healthcare insurance plan or any modifications are deemed by an individual as too expensive, he or she has a right to appeal directly to the reimbursement agency. The latter has an obligation to investigate each case and, most importantly, benchmark questionable healthcare plans against others of similar patient characteristics. It is both the responsibility of reimbursement agencies for ad hoc verification as well as a mechanism of regular control throughout all granted healthcare insurance plans with pre-defined frequency. If the appeal to the reimbursement agency fails, the patient has a right to appeal to the court with a pre-defined

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pathway established. The first policy is, therefore, the “data transparency for price settings.” Essentially, it is about the value-based pricing of the healthcare insurance plan with the objective to decrease the risk of an unjustified pricing policy. The transparency with reimbursement agency oversight ensures any risk of overpricing is limited. In the second new policy in Healthcare 5.0, healthcare insurance providers operate within a specific therapeutic area. With growing opportunities to treat patients remotely, the diversification towards excellence in each specific area is feasible. The key departure point is to operate beyond geographical restrictions established by borders of a given jurisdiction altogether and replace it with a digital healthcare cyber-physical ecosystem managed by a given healthcare insurer with entry criteria defined by the same healthcare needs. Newcomers are offered access to the knowledge about their health conditions and also something as pragmatic as support with the setup of the virtual navigator and the selection of preventive methods. The specialization of healthcare insurers brings the opportunity to establish a network of individuals who are sharing similar preferences and similar health problems irrespective of their physical location. Healthcare 5.0 strives for the development of such a digital ecosystem. It is essentially the platform on which to exchange experiences with others. It is the value a community of shared interests facing similar health challenges and/or looking for similar solutions to their diseases. Hence, it is the power of data that connects patients with others in the form of the digital community. Something like that has already happened; it was the DDS, the Digital City, one of the first online community networks that operated on a European scale. DDS was founded in the fall of 1993 by a small group of individuals and launched in January 1994 in Amsterdam.105 The initiative was considered as one of the precursors of the Internet. The vision was actually to create a virtual public space based on democratic principles. Geographical boundaries are non-existent for technological innovation. There are examples of such relationships with peers in support of disease management of hypertension when the mobile application can include the option of sending notifications to friends who will remind the patient about the incidence of non-adherence.106 We launch pharmaceuticals and medical devices within each jurisdiction separately due to differences in purchasing power and, consequently, pricing differences. In Healthcare 5.0, the connectivity of devices brings countless opportunities for the development of networks with boundaries defined only by shared needs. It seems natural to establish networks of individuals with similar health problems or similar preferences, so they could exchange ideas and connect. The digital era allows an unlimited exchange of experiences and opinions across individuals. Consequently, it can foster the development of communities connected, not by the principle of regional closeness, but by homogeneity in the nature of health problems. It is where synergy effects emerge for the benefit of greater understanding and consequently can lead to the generation of new ideas and new solutions. The second policy rule

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states “Data powers the exchange of information within digital networks of patients.” Give the power of data to citizens and help people to connect with each other. We need to establish a data-driven society – give citizens power by giving the citizens data! In the third new policy in Healthcare 5.0, healthcare insurance providers act as prognostic prediction disease centres. The ongoing collection and analysis of data captured from the patient’s health journey are possible thanks to the access to digital accounts of patients. In Healthcare 5.0, healthcare insurance providers build data warehouses that allow data analytics to flourish with the aim of prediction and prevention. The data power translates into very accurate health recommendations for each individual to support his goal of better health outcomes. How does it work? With the global outreach of healthcare insurance providers, the learning curve is accelerated with an accumulation of data. The economy of scales leads to greater efficiency. In Healthcare 5.0, access to data introduces a different term of efficiency, however – the avoidance of incidence. The healthcare insurers will try to attract newcomers by asking “how many patients suffering with disease X manage to avoid or postpone its terrible consequences?” Such a mode of operation means that the access to digital accounts is used in both ways: to use the data and to insert the data. The use of data from digital health accounts helps the decentralized system with global outreach to grow. It allows healthcare insurers to develop a significant pool of homogenous patients that, in turn, powers data warehouse to a scale not seen before. Data analytics machinery develops ML algorithms with precision not observed before. The access to a digital health account also allows each individual to send tailored information generated by ML predictions. It is the forecast of his or her future health status in the same fashion as the weather forecast is presented. The difference is, however, that in contrast to weather, the health forecast is modifiable. The adoption of ML algorithms to a specific patient profile allows healthcare insurance companies to provide each customer, as well as their virtual navigators, with automatic constant updates about the required changes in disease management and will activate prompts for actions again automatically to a given digital account. Therefore, in return for the access to personal health data, patients get timely and tailor-made health recommendations for the implementation or modification of preventive measures. It is a free service for each specific health problem to increase awareness and also to influence the patient’s daily actions towards prevention. I can’t stress enough the importance of the system of data analytics to be built by the healthcare insurance provider. It is the only agent who is able to collect holistic data from every data outlet about the patient’s health. Such an approach driven by the innovation of Healthcare 5.0, including the digital health account and connecting the patient to a healthcare insurance provider, is a breakthrough solution that brings healthcare to another level not seen before. No healthcare system had ever utilized the opportunity to connect data from many sources simultaneously. The synergy effect of data merging has enormous potential.

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Based on the collected evidence, it is obliged to continuously collaborate with reimbursement agencies to learn more and more about the connection between daily habits and risks of disease onsets and disease deterioration as well. It is the evidence that helps to provide patients with new health recommendations, as mentioned earlier, but also indicates to the reimbursement agencies the areas of unmet needs that, in turn, can be used to inform research and development plans of health technologies developers. The third policy rule is that data transparency is used to ensure efficiency, defined as the avoidance of health impairments. The system of digital health accounts collecting both health and behavioural data across the pool of homogenous patients powers the ML models of prediction and prevention that, in turn, issue early monitoring warnings and health recommendations for each individual automatically. Finally, data transparency allows the control of the healthcare insurance company budget. Why? In Healthcare 5.0, defined by the axioms of individual sovereignty and a holistic approach to health, the patient is the customer. In the public paternalistic healthcare system, the patient is the only beneficiary of healthcare services. Before Healthcare 5.0, it was the public decision-makers who owned the process. The dynamic is as different as that between a child and an adult. The latter is independent. The important driver of Healthcare 5.0 is the choice. The competition grows on the soil of free entry and free exit rules. Therefore, it is important that each individual has a right to choose and withdraw. She or he may change insurance companys at any time without financial consequences. Healthcare 5.0 is a digital account that can be given access to another healthcare insurance company at any time. That brings us to another important consideration, which is how to ensure the goal of excellence in the environment of a not-for-profit organizations. When I worked as CEO of the organizational unit owned by the Swedish County Council (CC) of Kalmar Region, the company was responsible for the recruitment of Polish doctors for Sweden. I will never forget that every Monday morning, the CC office was visited by the journalists who were granted open access to any documents. Why? The principle was that the CC is using public money, i.e., tax money; hence, the general public has a right to know … The same idea will be introduced in Healthcare 5.0. Every customer or journalist has a right to know and get access to any data she or he desires in full transparency mode. In addition to that, the Management Board of each healthcare insurance organization will be elected by the customers of that given company. Not only that, they will compete in the global competition for the nomination for the best Management Board. The winner will get a significant financial prize. The democratic voting system will be introduced in a similar fashion to a parliamentary election to both elect the Management Board and also to grant awards. In fact, the managers of healthcare insurance should be recognized as the representatives of the general public in defining the details of such rules. The most important is to highlight the fact that healthcare insurance companies should be non-profit organizations driven by the voice of

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their customer. Surely, in light of the abandonment of national regulation of the healthcare system, we need some form of alternative legal framework or, as Thomas Hobbes named it, a “social contract” for such a new form of democracy to grow. Otherwise, “Scarcity would cause us to fight only for our own survival,” he says.107 In the era of a smart society, I believe that scarcity is no longer that challenging or at least it is more manageable compared to the past. Data can help us to become more aware and actionable, understand opportunities and identify alternative pathways to overcome problems of scarcity. Most importantly, data can help us to innovate. Still, a new social contract in the digital era is needed. It is meant to replace governmental regulations defining the paternalistic role of the third-party in the healthcare arena and instead focus mainly on building the network based on data sharing and ensuring data privacy protection against misinformation and misuse. The forth policy rule is data transparency towards greater control against misuse of information and towards excellence despite non-profit interests. Following this deliberation, it is important to still elaborate further on the potential negative consequences of the lack of a centralized national body such as the Ministry of Health that protects against inadequate access to healthcare services. There are three specific mechanisms, or rather mitigation strategies, introduced by Healthcare 5.0 that function as safeguards for the group of the least disadvantaged.

4.3.14 Healthcare 5.0 – standardization to mitigate the risk of inadequate care across the globe Healthcare 5.0 sets the quality standards of care to be implemented for each patient irrespective of his or her non-clinical characteristics as previously discussed in the special care chapter. It relates to both prevention and treatment. The objective is to ensure rigorous operating procedures that make healthcare services standardized in a high quality manner to achieve health outcomes. It is the positive consequence of the adoption of the holistic approach to health. As it was discussed in detail earlier, the reimbursement process does not consider anymore just the accessibility to a given technology but the efficiency of its successful implementation into the clinical pathway. The underlying rationale is that high-quality health outcomes can only be guaranteed if there are standards to follow embracing the full healthcare journey from prevention to cure for each patient irrespective of his or her geographical location or other non-clinical characteristics. Let me provide an example of CART-T gene therapies. According to an international, multi-centre, observational cohort study  of more than 900 participants, up to 27% of patients required an intensive care stay up to four days after they received the treatment due to side effects related to cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome (ICANS). The understanding of that complexity urged clinical experts to look beyond the provision of the CART-T gene and voted for a holistic approach

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towards a new standard of care development. Critical care management is an integral part of CAR T-cell therapy and should be standardised.108 If one considers the fact that up to 31% of those in intensive care received life-saving treatments within 24 hours of admission, we need to clearly acknowledge that the success of such innovations as CART-T needs to look beyond the administration of technology itself. Not only does Healthcare 5.0 ensure access to the technology, but also the standard of care is successfully upgraded from a holistic standpoint for the health outcomes to be achievable. What if the patient received the treatment without the proximity to the services needed for sideeffects management? With the above deliberation, we can clearly note that standardization is the way to mitigate the risk of inadequate care. The success of the innovation does not depend only on the availability of the given technology but, in fact, also other technologies and, most importantly, the way they are implemented into clinical care. All of it is needed irrespective of the patient’s geographical or any other non-clinical characteristics. Health outcomes require appropriate standards to be placed for each patient in the same way, no matter where she or he resides.

4.3.15 Healthcare 5.0 – collaborative synergy between the real and digital world to mitigate the risks of miscare of the disadvantaged Healthcare 5.0 introduces the rationale of free access to the basic set of healthcare services available irrespective of financial affordability of any individual. That specific set of medical services should be defined by the committee formed by the representatives of enrollers within a given healthcare insurance organization in a similar fashion to the process of public engagement mechanisms for priority setting systems that already exist in some countries like England or New Zealand.109 There are at least four groups of services that are taken into consideration in the design of that basket of basic services in the equity of access mode. The first determines the rationale driven by the case of emergency. It is about events that are unrelated to individual health risks but rather relate to unpredicted accidents or also the cases of cybercrimes that prevent access to digital services. Under such consequences, any life-threatening conditions should be treated by emergency care units that are in the collaborative network with a given healthcare insurance as it was before Healthcare 5.0. The second group services are concerned with the underlying rationale of human dignity. It could be any basic medical aid needed in the event of cognitive dysfunction, in other words, in the event of the lack of a self-decision-making process. Such a set of healthcare services should not, however, impose principles that diminish the right to decide one’s own healthcare use under any other situation when he is able to make a pure judgement of his own health situation. Cognitive dysfunction may require human interaction between clinical experts and patients. Again, the collaboration between healthcare insurance companies with local

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teams may be required. The third group concerns healthcare services with the rationale of unpredictability. It is to be utilized in the event of the sudden diagnosis of any unpredictable diseases. It is a broad category that encompasses any procedures, pharmaceuticals and hospital care that are unrelated to epidemiological predisposition defined by the risk profile; hence, they were difficult to predict on an individual level. The rationale of the free access principle with potential physical interactions should apply here, too, as it was the case before Healthcare 5.0. The fourth group relates to healthcare services accessible by patients in remote areas. In Healthcare 5.0, the primary channel of solutions delivery for given health problems is remote and digital anyway. Still, there are services that need to be delivered “here and now” either in hospital settings or in physical presence such as life-threatening conditions mentioned earlier. Health insurance does not have to limit their offering to local communities but only oblige them to deliver specific healthcare services within a specific time frame (see earlier section about standards). Healthcare 5.0 brings a different dimension of a good local network of healthcare providers they agree to collaborate with. Consequently, it is up to the ability of the health insurance to contract local healthcare providers for those medical services that are not to be delivered online. There are instances when distance might be shortened by immediate air transfer. So what’s the solution for the potential lack of access for the disadvantaged? The above four categories indicate clearly the need for collaborative synergy between digital and physical healthcare systems. It may be indeed challenging to ensure organizational collaborative settings between global outreach with local physical needs. All four groups of services relate to specific circumstances that require a guarantee of unrestricted access to healthcare services as well. It is, again, the digital connection and fast data transfer that provides the solution. Data transparency, on the other hand, provides the greatest advocacy and protection of any wrongdoing in any of the above cases, too. Coupled with the implementation of the standards of care mentioned earlier, data transparency will allow us to easily track back any potential misconduct with respect to patients by the inspection of the reimbursement agencies. As noted earlier, it is the supervising organization who holds the responsibility of such control.

4.3.16  Healthcare 5.0 – data-sharing culture In the field of health economics, we discussed extensively the risk of rejection of health insurance due to the cream-skimming mechanism (avoiding costly patients). It is surely a challenge in any privately owned healthcare system when profit orientation is explicitly or implicitly introduced, especially when the search for efficiency gains is expected. In Healthcare 5.0, there are two feasible routes to minimize the incentives for cream skimming. First is the law that does not allow denial of access for any potential enroller. Second, it is in the healthcare insurance company’s interest to have a diversified portfolio of patients as

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that’s the only way it will manage to power the warehouse of data and provide the greatest accuracy and validity in the prediction of health outcomes for its machinery algorithms. Healthcare insurance companies must strive for excellence in order to attract new customers by their proven credibility towards the forecast of disease progression. The individual is not only considered as enroller but also data source, too. Does it mean that healthcare insurance companies have to enroll anyone but could dissolve at any time if patients turn non-compliant with health recommendations? Shall Healthcare 5.0 allow the rejection of such patients or at least increase their health insurance premium in case of unhealthy behaviours, such as overconsumption of alcohol or smoking? It should be up to healthcare insurance to propose such solutions. So what kind of principles for incentives shall we allow? We can try to adopt the Pigouvian solution with internalities (in particular through the implementation of sin taxes) for those who are not compliant. Alternatively, we can develop a healthcare financing model, applying the principles of sharing the cost of disease. It is in a similar fashion as the ongoing debate about the cost of climate change that applies the theory of Coasian bargaining, which pays particular attention to the problem of property rights. In other words, who is responsible for externalities such as the case of an increase in infection rates due to low rates of vaccination110? Following that logic in Healthcare 5.0, we need to address the ownership of healthcare costs and define the contract for when some hospitalizations and additional treatment costs are incurred by the patients neglecting to follow health recommendations. In Healthcare 5.0, the objective with cost sharing is incentives towards healthy behaviour. In other words, it is about awareness, not fiscal reasons. Maybe instead of punishing, we can encourage data sharing for financial benefit as well? If we adopt the assumption of 35 billion IoT available today, we can surely estimate there are, on average, four connected devices per human being.111 This speaks on its own on the power of data! Why not make this data donation an incentive, as it was eluded to earlier? Not only will it motivate the collection of high-quality data, but it will also increase awareness of the role of prevention. In Healthcare 5.0, data are saleable assets, which include two purposes. First, to help the patient reduce his costs of healthcare by allowing him to sell his own data. Second, to support innovation by allowing both researchers and regulatory authorities to use the data for development and validation, respectively. For example, each patient could define his or her NFT designed by his data and sell it to researchers and pharmaceutical companies. It is a way to motivate patients to keep good quality data and indirectly journey towards compliance. The NFT market is absolutely growing. In January 2022, the biggest marketplace, Opensea, has reached the value of USD$13.3 billion just five years after it was founded. For instance, famous digital artist Mike Winkelmann, better known as “Beeple,” crafted a composite of 5,000 daily drawings to create perhaps the most famous NFT of the moment, “EVERYDAYS: The First 5000 Days,” which sold at Christie’s for a record-breaking $69.3 million. There

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are already companies like Aimedis that are promoting the idea of an NFT marketplace for medical data business-to-business. My idea for Healthcare 5.0 is to go one step further and allow each patient to develop his or her NFT based on medical data and sell it. Built on blockchain technology with appropriate standards, it can truly revolutionize the healthcare market. To mitigate the risk of treatment denial requires us to implement a data-sharing culture. It highlights yet another important element worth considering from the standpoint of the data-driven environment in Healthcare 5.0. Each patient from the Day 1 generates data. So, to overbalance the fear of rejection of healthcare services, we need to become aware that we, as the nation of patients, are also a nation of data providers. The Healthcare 5.0 mode of prediction and prevention needs access to our data to fulfil its mission of avoidance of health problems. The system machinery requires fuel, such as our data, to be able to reach its mission successfully. Hence, who should worry about rejection? In conclusion, this chapter presents an individual futuristic vision of healthcare system 5.0. It is born on the idea of a smart Society 5.0 promoted by Keidanren. It is understood as a human-centred society functioning in a system that highly integrates cyberspace and physical space. In contrast to Health 4.0, which focuses on the adoption of new digital solutions into the healthcare system, the 5.0 version is mostly referred to as the personalized healthcare model with a customer-centric mindset. It is the ecosystem born on the unlimited pool of data but, most importantly, on the recognition of the fact that the patient is the sole decision-maker. It is the vision of the future of healthcare without national borders and limitations of accessibility. Healthcare 5.0 is digital, where the patient is virtually connected with other individuals sharing similar problems and experiences while learning from each other. Many examples of advancements in NGS, nanotechnology, AI, accessibility of data, and finally digital therapeutics and robotics give numerous examples of how Healthcare 5.0 can materialize into the vision of individual healthcare ecosystem managed by each patient according to his or her expectations and preferences. It is the system of health self-control where the patient makes own choices of preventive modalities. At the same time, Healthcare 5.0 has built-in mechanisms of digital safeguards that prevent both disease onset and disease consequences. The knowledge-based mode of decentralized healthcare is guaranteed by new institutions taking responsibility for safety, effectiveness and efficiency gains.

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5 CASE STUDY

Now that the concept of Healthcare 5.0 has been outlined, it is time to test the feasibility of its implementation among potential future users.

5.1 Study objective, questionnaire and methodological approach The overarching objective of the study was to address the question, “What drives the attitudes towards digital health?” The societal preferences were across 320 responders aged 20–39 years. The study had three objectives. The first objective was to test a specific hypothesis that responders are willing to accept digital health against the opportunity of greater life expectancy. The second was to verify whether there are any sociodemographic characteristics that drive the greatest acceptance of digital health and strength of these preferences. Finally, it was to identify any specific characteristics of digital health that are of highest and lowest acceptance and how they differ across the studied population. It was cross-sectional questionnaire study conducted online. There were five sections. While the first covered sociodemographic questions, the second described the futuristic hypothetical vision of Healthcare 5.0 (digital model). The third section was the main part. It was the hypothetical experiment adopting the social welfare function (SWF) to study responders’ attitudes towards the choice between the current (analogue) and digital models (objective one and two). The fourth laid out direct questions to test the preferences towards the specific features of Healthcare 5.0, while the final section related to the attitudes towards the different financial consequences of compliance towards the solutions provided within Healthcare 5.0 (objectives three and four). The questionnaire is presented in the appendix. DOI: 10.4324/b23291-5

128  Case study

5.1.1  Part I The objective of the first part was to study the sociodemographic characteristics in order to provide insights into the heterogeneity of preferences in a descriptive fashion. Among other determinants, it included gender, age, education and experience with serious sickness of his or her own or close relatives. Additionally, questions about self-reported financial independence, self-reported health and satisfaction with the healthcare system, on the Likert scale of 1–10 from the lowest to the highest rank, were investigated as well. Knowledge about the financial contribution to the healthcare system was measured as well as access to free preventative care programs, free choice of doctors, waiting time to family doctor and specialists and hospital care was asked. Finally, out-of-pocket (OOP) payments were investigated.

5.1.2  Part II Methodological approach The second part addressed objectives two and three. In short, it was to verify what determines the preferences towards the choice of the digital healthcare model instead of the analogue option. The responder had to choose between two different types of healthcare models: “healthcare system of today” (analogue model) or the “healthcare system of the future” (digital model) (see Table 5.1). The latter is described in detail (see Appendix). The analogue model assumed the life expectancy based on available demographics for specific ages and genders in Poland. In its characteristics, the responder was told to utilize his or her own experiences in terms of the access to treatment, waiting times and all other aspects to ensure that the analogue model reflected the healthcare system of today in the responder’s mind as a comparison to Healthcare 5.0. The latter estimated life expectancy based on the responder’s individual health risk profile and took into consideration the individual autonomy in prevention with the support of digital solutions utilizing a virtual assistant (VA) and remote specialized healthcare. Each responder was randomly assigned to one of 16 scenarios. In each scenario, the individual was confronted with a hypothetical choice between the analogue and digital models that differed in assumptions regarding minimum and maximum life expectancy (see Appendix). In this study, the SWF approach was used. This construct is more and more frequently adopted in many areas of welfare economics, including health inequality, tax theory, growth theory and, recently, in the analysis of the implications of climate change.1 SWF allows us to understand societal preferences towards trade-offs between alternative courses of action. The intention is to define the importance (weight) to be assigned to potential gains achieved with one health policy relative to another. This is represented as the marginal rate of substitution (MRS) along the relevant welfare curve. MRS is the rate at which

Case study 129

some amount of one good can be exchanged for another good while maintaining the same level of utility (satisfaction). It represents the slope of an iso-welfare curve (indifference curve). As such, the overarching principle was not to establish the level of social welfare but simply the shape of the SWF. The methodology proposed by Johannesson and Gerdtham 2 was adopted without the assumption of constant elasticity of substitution (CES). In other words, no assumption was made towards the risk aversion of change from one healthcare model to another. The following regression model was chosen: [P/(1 − P)] = a + b1 * marginal trade-off + b2 * relative difference (1) where P is the probability of choosing the digital healthcare model and 1 is a marginal trade-off defined as the difference in minimal life expectancy between both models divided by the difference in maximum life expectancy between both models. Relative difference is the percentage difference between maximum and minimum life expectancy in the digital model. The hypothesis to test was that increased marginal trade-off would decrease the probability of choosing the digital model. Following the hypothesis outlined in objective one, the probability that respondents would choose the digital model was expected to vary with the changes along the marginal trade-off. Therefore, the marginal trade-off was hypothesized to have a negative sign. The median marginal trade-off is achieved when the probability of choosing the digital model is equal to 0.5 (i.e., median responder is indifferent between analogue and digital models ). Since the marginal trade-off variable was entered as a linear variable, the median marginal trade-off equals the mean marginal trade-off. Consequently, by setting the probability of choosing Y to 0.5 in Equation (1), it was possible to estimate the mean marginal trade-off: Mean marginal trade-off (MTO) = (−a − b2 * relative difference)/b1 The logistic regressions were estimated by the maximum likelihood method. In the previous studies performed by Johannesson and Gerdtham, the marginal tradeoff turned out to be influential in the choices made by respondents. Following objective two, it was tested whether any sociodemographic characteristics would turn out to be statistically significant. For that particular purpose, both t-test and Chi-square tests were performed to assess whether there any other factors describing the study population were impactful on the choices made by responders. Following objective two, mean MTO was calculated for specific subpopulations defined by any sociodemographic characteristics that influenced responders’ choices for the digital model in a statistically significant manner. The lower the number, the more the society was eager to take upon risk and adopt the digital model, not being afraid of losing the basic life span allocation in the analogue model. The higher the number, the less likely the responder was willing to switch to the digital model from the analogue. Following the study of Johannesson and Gerdtham, two goodness-of-fit measures were reported as well: individual prediction and the likelihood ratio

130  Case study TABLE 5.1  Digital/analogue model

Digital model

Analogue model

Max 40 or 36 Min 20 years (all scenarios) Average 35 years

Max 37 or 33 years Please select randomly the Min 21 years (or 20, 22, 23) numbers for both models, so Average 34 years each participant answers once at random choice of numbers

index. Individual prediction is the percentage of binary responses correctly predicted by the equation. The likelihood ratio test was also used to compare the general model with all the explanatory variables, with a restricted model only including statistically significant variables.

5.1.3  Part III The third objective was to investigate specific digital model’s characteristics most and least likely to be accepted and how they differ across a studied population. It consisted of a set of six specific close-ended questions investigating the preferences towards different variants (levels) of the attributes of the digital healthcare system introduced in earlier part of the questionnaire (see Table 5.2). The underlying assumption was that healthcare models can be described by their characteristics (or attributes) and that an individual’s valuation depends on the levels of the attributes. It is also assumed that individuals behave rationally. In order not to cognitively overburden the responders, every feature of digital healthcare was asked separately.

5.1.4  Part IV Preferences towards financial nudges aim at improving compliance with the digital model. The fourth part adopted close-ended questions to test attitudes towards the financial consequences of non-compliance and financial reward for compliance towards the health recommendations of the VA as well as the willingness to share health data.

5.2 Results The Digital Health study was carried out on a professional, commercial Internet panel by the leading research company in the Polish market, “Inny Format.” The panel operates in accordance with industry standards3 and the General Data Protection Regulation (GDPR). It includes over 300,000 people aged 15 and above. Three hundred twenty individuals were recruited to participate in that particular survey. The Digital Health study was conducted in January–March 2022 in Poland. The study was representative with respect to the age group

TABLE 5.2  Description of digital healthcare models

Please let us define your optimal digital model

Which of the below options do you think is best? Please rank the below options from 1 being most preferred to 3 being least preferred

Instead of GP AI-driven virtual assistant (VA)

1. VA has passed the exam of medical school equal to GP 2. VA is certified by the government 3. VA is certified by the government and passed GP exam

Relationship with VA

1. VA is allowed to initiate contact anytime if health symptoms are required 2. VA is allowed to connect with you only in pre-agreed time slots 3. You are only able to connect to VA

24/7 Health monitoring

1. 2. 3. 4.

Diagnosis tests

1. Majority of diagnostic tools delivered to your home 2. Majority of diagnostic tests done in clinic with a travel requirement, but allowing you to meet healthcare professionals on the spot for help

Free choice of specialist from the network of specialists worldwide. Only video consultation with simultaneous translation if needed

1. Your home country 2. Selection from worldwide catalogue

Medication

1. All delivered to your home and only ordered by the VA in agreement with you to ensure best care for your health 2. You are allowed to buy medication without prescription whenever you want

Hospital care

1. Worldwide options to find the best care with the shortest waiting time, but procedure is conducted remotely with robotics navigated by surgeons 2. Hospital of your choice with physical the presence of healthcare personnel on site

Wrist wearable to be worn 24/7, invisible sensors attached to your arm Wrist wearable to be worn 24/7 plus invisible sensors (stickers) on your skin Chip installed under your skin Sensors on your skin and at home

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132  Case study

20–39. The questionnaire study was developed by the author, Katarzyna Kolasa. It was pilot tested by Inny Format in face-to-face interviews with 10 responders.

5.2.1  Part I The sociodemographic characteristics are presented in Table 5.3. The financial independence (Table 5.4) and self-assessment health (Table 5.5) are both set at 6.8 on the scale of 1 (worst) to 10 (best). Satisfaction with the healthcare system was estimated at 3.93, set similarly on the same Likert scale (Table 5.6). A majority claimed that they did not know the monthly financial contribution (62%) and did not have access to any free-of-charge prevention (67%) (Tables 5.7 and 5.8). The OOP expenses are presented in Table 5.9. While 42% declared less than 5%, 30% noted the amount between 5% and 10%, with the remaining paying more out of their monthly disposable income. The basic description of both groups selecting analogue and digital models, respectively, is presented in Table 5.10. The digital model was more often selected compared to the analogue, 58% vs. 42%, in a statistically significant manner. Among 16 scenarios, there were 12 and 6 with the a greater amount of responders selecting the digital and analogue model, respectively. Among the descriptive variables tested for their impact on the choice of digital vs. analogue model, both experience with a relative’s sickness and work status were found statistically significant in decision-making. The share of responders willing to choose the digital model was 17 and 14 percentage points greater among the working population and those with experience of severe health problems in the family, respectively (Table 5.10). Additionally, the preferences towards the digital TABLE 5.3  Sociodemographic characteristics of the participants

Sex

Total Sex

Female Male Sum Age 20–29 years 30–39 years Education Secondary or lower Higher Professional status Employed Unemployed Respondent’s severe disease Yes No Severe disease in the family Yes No

Female

Male

Quantity

50%

50%

100%

320

100%   50% 63% 43% 48% 52% 45% 72% 59% 45% 52% 48%

  100% 50% 37% 57% 52% 48% 55% 28% 41% 55% 48% 52%

100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

    320 110 210 150 170 259 61 108 212 192 128

TABLE 5.4  Financial independence of the participants

 

Total Sex Age Education Professional status Respondent’s severe disease Severe disease in the family

What is the subjective assessment of your financial independence on a scale from 1 (completely dependent) to 10 (completely independent)?

  Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No

1 (completely dependent)

2

3

4

4% 3% 4% 6% 2% 6% 2% 2% 13% 3% 4% 3% 5%

3% 4% 2% 4% 2% 3% 2% 1% 11% 3% 3% 4% 2%

5% 3% 7% 6% 4% 7% 4% 4% 8% 6% 5% 6% 4%

5% 8% 2% 9% 2% 5% 4% 4% 8% 7% 3% 4% 6%

5

6

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

7

8

9

10 (completely independent)

17% 19% 14% 15% 17% 15% 18% 16% 18% 17% 17% 19% 13%

16% 17% 15% 17% 15% 14% 18% 19% 3% 17% 16% 17% 15%

10% 7% 14% 9% 11% 9% 12% 12% 3% 10% 10% 10% 10%

19% 14% 24% 15% 21% 13% 24% 22% 8% 15% 21% 16% 24%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 160 160 110 210 150 170 259 61 108 212 192 128

Mean 6.88 6.57 7.19 6.39 7.14 6.31 7.39 7.32 5.00 6.69 6.98 6.78 7.04 Case study 133

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TABLE 5.5  Health self-assessment of the participants

 

What is your health self-assessment on a scale from 1 (very bad) to 10 (excellent)? 1 (very bad)

Total Sex Age Education Professional status Respondent’s severe disease Severe disease in the family

  Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No

                         

2

3

4

5

6

7

8

9

10 (excellent)

1%   1% 1% 0% 1% 1%   3% 1% 0% 1%  

3% 4% 2% 4% 2% 1% 4% 3% 3% 6% 1% 3% 3%

5% 6% 4% 7% 4% 8% 2% 4% 8% 10% 2% 7% 2%

12% 12% 12% 15% 10% 17% 8% 12% 10% 17% 9% 12% 12%

18% 18% 18% 15% 19% 19% 17% 16% 26% 25% 14% 21% 13%

25% 28% 23% 25% 25% 23% 28% 25% 26% 22% 27% 23% 29%

20% 21% 19% 20% 20% 14% 26% 22% 15% 11% 25% 18% 23%

11% 6% 16% 7% 13% 10% 12% 12% 5% 5% 14% 11% 11%

5% 4% 6% 5% 5% 8% 3% 6% 3% 4% 6% 4% 8%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 160 160 110 210 150 170 259 61 108 212 192 128

Mean 6.87 6.71 7.03 6.65 6.99 6.73 6.99 7.00 6.34 6.16 7.24 6.68 7.16

TABLE 5.6  Healthcare system satisfaction evaluation

 

  Female Male Age 20–29 years 30–39 years Education Secondary or lower Higher Professional status Employed Unemployed Respondent’s severe disease Yes No Severe disease in the family Yes No Total Sex

How much are you satisfied with the healthcare system on a scale from 1 (worst) to 10 (best)? 1 (worst)

2

3

4

5

6

7

8

9

10 (best)

18% 18% 18% 22% 16% 17% 19% 17% 20% 21% 16% 20% 14%

14% 15% 13% 10% 16% 11% 16% 14% 13% 13% 14% 15% 12%

17% 16% 18% 18% 16% 15% 19% 18% 13% 19% 16% 19% 13%

11% 14% 9% 13% 10% 10% 12% 12% 10% 9% 12% 10% 13%

16% 16% 17% 20% 14% 18% 15% 16% 16% 17% 16% 14% 20%

11% 11% 11% 7% 13% 13% 9% 11% 11% 10% 11% 9% 14%

6% 6% 6% 5% 6% 4% 8% 6% 7% 6% 6% 7% 4%

3% 3% 3% 2% 3% 4% 1% 2% 3% 2% 3% 3% 2%

3% 3% 4% 3% 4% 5% 2% 3% 5% 2% 4% 2% 5%

1%   3%   2% 3%   1% 2% 1% 1% 1% 2%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 160 160 110 210 150 170 259 61 108 212 192 128

Mean 3.93 3.78 4.08 3.68 4.06 4.28 3.62 3.89 4.08 3.67 4.06 3.67 4.31 Case study 135

136  Case study TABLE 5.7  Monthly financial contribution awareness

 

Total Sex Age Education Professional status Respondent’s severe disease Severe disease in the family

Do you know how much your health insurance is?

  Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No

Yes

No

38% 36% 41% 31% 42% 34% 42% 43% 18% 42% 37% 42% 34%

62% 64% 59% 69% 58% 66% 58% 57% 82% 58% 63% 58% 66%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 160 160 110 210 150 170 259 61 108 212 192 128

vs. analogue model seemed to differ across the study population with respect to satisfaction with the healthcare system. Those who were more positive than the median responder tended to prefer the digital model (Table 5.10). It was on the border of being statistically significant.

5.2.2  Part II The allocation of responders across different trade-off scenarios along MTO and Relative Difference (RR) calculated for each scenario is illustrated in Table 5.11. TABLE 5.8  Access to free healthcare prevention programs

 

  Female Male Age 20–29 years 30–39 years Education Secondary or lower Higher Professional status Employed Unemployed Respondent’s severe disease Yes No Severe disease in the family Yes No

Total Sex

Do you get any health prevention program free of charge? Yes

No

No idea

5% 6% 3% 5% 5% 5% 4% 5% 2% 6% 4% 5% 4%

67% 64% 69% 63% 69% 62% 71% 69% 54% 67% 67% 67% 66%

29% 30% 28% 33% 27% 33% 25% 25% 44% 27% 30% 28% 30%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 160 160 110 210 150 170 259 61 108 212 192 128

TABLE 5.9  Healthcare out-of-pocket payments as part of disposable income

 

Total Sex Age Education Professional status Respondent’s severe disease Severe disease in the family

How much do you spend on average per month (apart from health insurance) on protecting your health (doctors, tests, medicines, etc.)?

  Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No

Less than 5% of salary

5%–10% of salary

More than 10% of salary

No idea, hard to say

42% 37% 47% 40% 44% 34% 49% 42% NA 34% 47% 39% 48%

30% 33% 27% 30% 29% 27% 32% 30% NA 36% 26% 32% 27%

12% 18% 6% 10% 12% 13% 11% 12% NA 19% 8% 14% 8%

16% 12% 20% 20% 14% 26% 9% 16% NA 11% 19% 15% 17%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% NA 100% 100% 100% 100%

259 116 143 86 173 107 152 259 NA 85 174 155 104 Case study 137

138  Case study TABLE 5.10  Healthcare system selection – sociodemographic characteristics

 

Healthcare system selection (all scenarios) Digital system Analogue system

Total Sex Age Education Professional status* Respondent’s severe disease Severe disease in the family* Subjective assessment of financial independence Subjective health self-assessment Assessment of the healthcare system in Poland

  Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

Quantity

58% 54% 62% 55% 60% 61%

42% 46% 38% 45% 40% 39%

100% 100% 100% 100% 100% 100%

320 160 160 110 210 150

56% 61% 46% 63% 56% 64% 50% 58%

44% 39% 54% 37% 44% 36% 50% 42%

100% 100% 100% 100% 100% 100% 100% 100%

170 259 61 108 212 192 128 122

60%

40%

100%

145

62%

38%

100%

122

57%

43%

100%

117

55%

45%

100%

155

67%

33%

100%

129

*Statistically relevant.

The probability of selecting a digital health model for different marginal trade-off is presented in Table 5.12. The results of logistic regression can be found in Table 5.13. The goodness-of-fit is presented in Table 5.13 as well. Only work status was found statistically significant in its impact on the probability of the selection of the digital model. Among the subgroup of those who are working and had previous experience with diseases among relatives, the coefficient for MTO had a positive sign. This is both an indication of strong preference towards the choice made for the digital model or the other way around depending on the responder’s preference. The subgroup of the mean MTO was estimated at –5.49 years.4 It can be interpreted as a very strong preference towards the digital model irrespective of the length of life expectancy. It is important to note is that MTO was not statistically significant either. Median responders would be willing to accept a loss of up to five years in the shortest life expectancy as well as give up one year in the maximum life expectancy before considering a change to his or her choice from digital to analogue model.

Case study 139 TABLE 5.11  Allocation of responders across the scenarios along marginal trade-off and relative difference estimation

 

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 Scenario 8 Scenario 9 Scenario 10 Scenario 11 Scenario 12 Scenario 13 Scenario 14 Scenario 15 Scenario 16

Digital model

Analogue model

Max

Min

Mean

Max

Min

Mean

Quantity

40 40 40 40 40 40 40 40 36 36 36 36 36 36 36 36

20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20

30 30 30 30 30 30 30 30 28 28 28 28 28 28 28 28

37 37 37 37 33 33 33 33 37 37 37 37 33 33 33 33

20 21 22 23 20 21 22 23 20 21 22 23 20 21 22 23

28.5 29 29.5 30 26.5 27 27.5 28 28.5 29 29.5 30 26.5 27 27.5 28

6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

5.2.3  Part III Overall, the majority of responders selected the requirement for both the family medicine exam along with governmental certification as the requirement for a VA (Table 5.14). No difference can be noticed between the digital and analogue model voters in that respect. Forty-three percent wanted the VA to initiate contact whenever there was any health rationale and 23% allowed the VA to connect in pre-defined time slots, leaving the remaining part of the study population in favour of patients taking that initiative instead (Table 5.15). The majority of the latter option was in the group of analogue model voters. Overall, a VA being active was the preferred choice more among digital health than analogue model proponents. Wearables and sensors on the body were generally preferred against chips and sensors under the skin across the entire group of responders, irrespective of the choice of the model (Table 5.16).

TABLE 5.12  Probability of selection of the digital model for different MTO   −3.00 −2.00 −1.00 0.00 0.14 0.29 0.33 0.43 0.67 1.00 0.00 0.14 0.29 0.43 1.00 100%       52.78 46.70 53.80 76.00 72.22 52.94 62.50   46.70 53.80 72.22   80% 51.85 42.11 47.37 61.90     71.42   58.33 57.89         57.89

140  Case study TABLE 5.13  Coefficients of the logistic regression analysis of the probability of choosing

the digital model Logit regression results Visible on your skin: Y No. observations: Model: Logit Df residuals: Method: MLE Df model: Date: 27.04.2022 Pseudo R-squ.: Time: 19.55.09 Log-Likelihood: Converged: True LL-Null: Covariance type: Non-robust LLR P-value: Const COEF STD ERR Z P > |Z| −0.0545 1.211 −0.045 0.964 Marginal trade-off 0.1579 0.115 1.369 0.171 Relative difference −0.0021 0.013 −0.165 0.869 Working status 0.6023 0.289 2.081 0.037 Illness of relatives 0.3215 0.246 1.306 0.192 Goodness-of-fit (accuracy per below description is equal to 0.611)

(0.025 −2.429 −0.068 −0.028 0.035 −0.161

321 316 4 0.01959 −214.15 −218.43 0.07312 0.975) 2.32 0.384 0.023 1.169 0.804

Accuracy is one metric for evaluating classification models. Informally, accuracy is the fraction of predictions our model got right. Formally, accuracy has the following definition:

Accuracy =

Number of  correct predictions Total number of  predictions

For binary classification, accuracy can also be calculated in terms of positives and negatives as follows:

Accuracy =

TP + TN , TP + TN + FP + FN

where TP = True Positives, TN = True Negatives, FP = False Positives, FN = False Negatives.

As far as the digital health solutions for hospital settings are concerned, supplies delivered the home was preferred over the hospital settings on a slight margin (Table 5.17). However, it was the most chosen option out of two available among those with preferences towards a digital model. The opposite was true for those who chose the analogue model (Table 5.17). Across the total studied population, the preference towards specialized care globally vs. country-specific was set at 50% vs. 50%. Still, the choice towards global outreach was preferred by digital health voters, whereas the local option was selected by responders favouring the analogue model (Table 5.18). Similarly, the choice towards medications ordered by the VA and delivered home was the overall more preferred choice. In fact, it was the option chosen by the majority of those who selected the digital model while self-purchase was chosen by majority of analogue model voters (Table 5.19). The option of remotely conducted hospital procedures with support of robots was selected by a minority of responders. The dislike of such an approach was greater among those who chose the analogue model. The physical presence of medical personnel on site was the option chosen by majority (Table 5.20).

TABLE 5.14  Characteristics of the most preferred model – AI-driven VA instead of GP

 

Most preferred model – AI-driven virtual assistant (VA) instead of a family doctor (GP) VA passed the VA is certified by the medical school VA is certified by government and passed the exam same as GP the government medical school exam as GP 40% 38% 43% 38% 42% 36% 42% 44% 36% 39% 44% 40% 40% 41% 38% 34% 45% 41% 42% 43% 35%

10% 10% 10% 8% 12% 12% 9% 9% 10% 11% 5% 12% 8% 11% 8% 12% 8% 11% 8% 9% 12%

50% 53% 47% 54% 46% 52% 50% 47% 54% 50% 51% 48% 51% 48% 54% 54% 47% 48% 50% 48% 53%

100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

Case study 141

  Digital model Analogue model Female Male Age 20–29 years 30–39 years Education Secondary or lower Higher Professional status Employed Unemployed Respondent’s severe Yes disease No Severe disease in the Yes family No Subjective assessment of Low rating – below median financial independence High rating – above median Subjective health Low rating – below median self-assessment High rating – above median Assessment of the healthcare Low rating – below median system in Poland High rating – above median Total Model selection (all scenarios together) Sex

Quantity

142  Case study

TABLE 5.15  Communication with the VA

 

Communication with the VA – most preferred model VA is allowed to initiate contact anytime if health symptoms are required

Total Model selection (all scenarios together) Sex Age Education Professional status Respondent’s severe disease Severe disease in the family Subjective assessment of financial independence Subjective health self-assessment Assessment of the healthcare system in Poland

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

43% 49% 34% 42% 43% 41% 43% 41% 44% 42% 46% 45% 41% 44% 41% 34% 46% 45% 42% 41% 43%

VA is allowed to connect with you only You are only able to in pre-agreed time slots connect to VA 26% 28% 24% 28% 25% 29% 25% 29% 24% 27% 25% 26% 26% 25% 28% 32% 26% 20% 29% 27% 26%

31% 23% 43% 31% 32% 30% 32% 29% 33% 32% 30% 29% 33% 31% 31% 34% 28% 34% 29% 32% 31%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

TABLE 5.16  Evaluation of 24/7 health monitoring

 

Total Model selection (all scenarios together) Sex Age Education Professional status

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

Wrist wearable to be worn 24/7 plus invisible sensors attached to your arm

Wrist wearable to be worn 24/7 plus invisible sensors (stickers) on your skin

40% 41% 38% 39% 41% 38% 41% 40% 40% 39% 44% 45% 37% 40% 41% 47% 40% 44% 37% 40% 42%

38% 36% 41% 42% 34% 40% 37% 41% 36% 38% 39% 31% 42% 36% 41% 39% 37% 38% 41% 38% 36%

Chip installed Sensors on your under your skin skin and at home 11% 11% 10% 9% 12% 10% 11% 11% 11% 11% 10% 9% 11% 10% 12% 7% 12% 10% 9% 11% 12%

11% 11% 11% 10% 13% 12% 11% 9% 14% 12% 7% 15% 9% 15% 6% 8% 11% 8% 14% 11% 10%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

Case study 143

Respondent’s severe disease Severe disease in the family Subjective assessment of financial independence Subjective health self-assessment Assessment of the healthcare system in Poland

24/7 Health monitoring – most preferred model

144  Case study

TABLE 5.17  Diagnostic tests – delivery selection rating

 

Diagnosis tests – most preferred model Majority of diagnostic tests done in clinic with a Majority of diagnostic tool travel requirement but allowing face-to-face help kit only delivered home from a healthcare professional on site

Total Model selection (all scenarios together) Sex Age Education Professional status Respondent’s severe disease Severe disease in the family Subjective assessment of financial independence Subjective health self-assessment Assessment of the healthcare system in Poland

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

52% 59% 42% 54% 49% 50% 52% 52% 51% 51% 56% 48% 53% 53% 49% 56% 48% 55% 48% 50% 51%

48% 41% 58% 46% 51% 50% 48% 48% 49% 49% 44% 52% 47% 47% 51% 44% 52% 45% 52% 50% 49%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

TABLE 5.18  Preference toward specialized care globally vs. country-specific rating

 

Total Model selection (all scenarios together) Sex Age Education Professional status Respondent’s severe disease Severe disease in the family

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or Lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

Your home country specialist

Selection from worldwide catalogue

50% 42% 60% 48% 52% 56% 46% 48% 51% 47% 59% 47% 51% 44% 58% 48% 53% 54% 45% 48% 53%

50% 58% 40% 53% 48% 44% 54% 52% 49% 53% 41% 53% 49% 56% 42% 52% 47% 46% 55% 52% 47%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

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Subjective assessment of financial independence Subjective health selfassessment Assessment of the healthcare system in Poland

Free choice of specialist from the network of specialists worldwide; only video consultation with simultaneous translation if needed – most preferred model

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TABLE 5.19  Medication delivery rating

 

Medication delivery – most preferred model All delivered home and only ordered You are allowed to by VA in agreement with you to buy medication without ensure best care for your health prescription whenever you want

Total Model selection (all scenarios together) Sex Age Education Professional status Respondent’s severe disease Severe disease in the family Subjective assessment of financial independence Subjective health selfassessment Assessment of the healthcare system in Poland

Quantity

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median

53% 66% 36% 50% 57% 63% 49% 52% 55% 55% 46% 48% 56% 57% 48% 58% 48%

47% 34% 64% 50% 43% 37% 51% 48% 45% 45% 54% 52% 44% 43% 52% 42% 52%

100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145

Low rating – below median High rating – above median Low rating – below median High rating – above median

44% 61% 55% 55%

56% 39% 45% 45%

100% 100% 100% 100%

122 117 155 129

TABLE 5.20  Selection of hospital care – virtual vs. physical rating

 

Total Model selection (all scenarios together) Sex Age Education Professional status Respondent’s severe disease Severe disease in the family

Worldwide options to find the best care with shortest waiting time but procedure conducted remotely with robotics navigated by surgeons

Hospital at your choice with physical presence of healthcare personnel on site

37% 46% 24% 35% 38% 37% 36% 41% 33% 37% 36% 35% 37% 36% 38% 39% 34% 35% 36% 36% 39%

63% 54% 76% 65% 62% 63% 64% 59% 67% 63% 64% 65% 63% 64% 63% 61% 66% 65% 64% 64% 61%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

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Subjective assessment of financial independence Subjective health selfassessment Assessment of the healthcare system in Poland

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

Hospital care – most preferred model

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TABLE 5.21  Willingness to change the healthcare system when it is free

Would you be willing to opt out from the current system for a new digital model provided that you are allowed to pay less each year?

 

Total Model selection (all scenarios together) Sex Age Education Professional status Respondent’s severe disease Severe disease in the family Subjective assessment of financial independence Subjective health self-assessment Assessment of the healthcare system in Poland

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

No

Yes

50% 28% 80% 54% 46% 56% 47% 53% 47% 48% 57% 53% 49% 46% 56% 56% 48% 54% 44% 52% 43%

50% 72% 20% 46% 54% 44% 53% 47% 53% 52% 43% 47% 51% 54% 44% 44% 52% 46% 56% 48% 57%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

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5.2.4  Part IV The studied population was indifferent as far as the choice of digital model in case of it costing less than current system in terms of their financial contribution. Still, 72% and 20% voted in favour of such a proposition from digital and analogue model proponents, respectively(?) (Table 5.21). In the case of a greater financial contribution than current one, 67% of the studied population was against (Table 5.22). The response to data-sharing for research with the opportunity of paying less for healthcare was different for both groups. It was considered a preferred option by 62% and 31% of digital and analogue model voters, respectively (Table 5.23).

5.3 Discussion There has been limited research so far regarding the digital transformation of the healthcare system. The results found on PubMed with key search criteria as “digital health” and “preferences” or “attitudes” provided less than 40 hits each.5 Research has mainly remained centred around the technological and organizational developments. For example, the systematic scoping review of the adaptation of digital health solutions in the cardiovascular field identified 27 studies that analysed such barriers as Internet access, user-friendliness, organizational support, workflow efficiency and data integration, but nothing related to societal preferences.6 Given the understanding of the current state of adoption of digital technologies, the conducted study, although pilot in nature, should be considered as an interesting opportunity to shed new light on the public preparedness for the digital disruption of the healthcare sector. Although the study was limited in terms of the number of responders as well as geographical settings, it does provide some insights into the factors determining the societal preferences towards the adoption of digital health. There are four important findings worth highlighting, with first two relating to objectives one and two, while the remaining two relating to objective three. The first finding relates to the first and second objective and more precisely the sociodemographic characteristics that determine the choice of digital model to the greatest extent. Based on the descriptive statistical analysis, it can be concluded that the majority of young Poles aged between 20 and 39 who have experience of illness in the family and are already active in the labour market were in a statistically significant manner more willing to switch to the digital model defined by specific characteristics. The second finding involved a regression analysis, which reinforced earlier results of descriptive statistics while revealing more insights into the drivers of the preferences of studied responders. As far as the sociocharacteristics are concerned, only past experience with ill relatives was a strong predictor of preference towards the digital model. Surprisingly, it turned out that the choices of either the current

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TABLE 5.22  Willingness to change the healthcare system when payment is required

 

Total Model selection (all scenarios together) Sex Age Education Professional status Respondent’s severe disease Severe disease in the family Subjective assessment of financial independence Subjective health self-assessment Assessment of the healthcare system in Poland

Would you be willing to opt out from the current system for a new digital model provided that you have to pay more each year?

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or lower Higher Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

No

Yes

67% 52% 89% 71% 64% 72% 65% 70% 65% 66% 72% 69% 67% 65% 70% 70% 63% 71% 63% 68% 62%

33% 48% 11% 29% 36% 28% 35% 30% 35% 34% 28% 31% 33% 35% 30% 30% 37% 29% 37% 32% 38%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

TABLE 5.23  Data-sharing willingness among participants

 

Total Model selection (all scenarios together) Sex Age Education Professional status Respondent’s severe disease Severe disease in the family Subjective assessment of financial independence Subjective health self-assessment

  Digital model Analogue model Female Male 20–29 years 30–39 years Secondary or lower HIGHER Employed Unemployed Yes No Yes No Low rating – below median High rating – above median Low rating – below median High rating – above median Low rating – below median High rating – above median

No

Yes

51% 38% 69% 54% 48% 55% 49% 55% 48% 50% 54% 52% 50% 48% 55% 57% 48% 57% 51% 58% 42%

49% 62% 31% 46% 53% 45% 51% 45% 52% 50% 46% 48% 50% 52% 45% 43% 52% 43% 49% 42% 58%

Quantity 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

320 186 134 160 160 110 210 150 170 259 61 108 212 192 128 122 145 122 117 155 129

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Assessment of the healthcare system in Poland

Would you like to share your data?

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(analogue) or digital model are fixed and not influenced either by the potential life expectancy gains or losses. On the one hand, among the six scenarios where the analogue model was selected, there were two with life expectancy forecasts in favour of digital model. The analysis of preferences of responders who in different trade-off scenarios chose the digital model irrespective of potential health gains or losses leads to similar conclusions. It seems that the preferences are not influenced by future circumstances, but they are rather designed by past experiences. The third finding showed there were two specific aspects of the digital model that received least acceptance from responders. Governmental authorization was not satisfactory for the VA to be introduced as the replacement of a family doctor, while intrusion into the human body with sensors for health monitoring was met with low acceptance as well. The greatest differences between groups of voters in favour of the digital and analogue models indicated some potential trust issues of the latter. They were less prone to accept remote performance of surgical procedures and more willing to buy pharmaceuticals on their own. The fourth finding showed proponents of the analogue model clearly disliked the financial modifications aimed at attracting them to switch to the digital model. On the other hand, the responders who chose the digital model not only were more willing to accept financial nudges but also seemed to be more prone to sell health data. The latter was mostly rejected by proponents of the analogue model. In summary, it can be concluded that preferences towards the healthcare system organization are driven more by past experiences and potentially predefined beliefs, and less so by opportunities brought with the digital transformation. Consequently, the study results tend to suggest that preferences regarding the healthcare system organization are formed more on conceptual level and mainly by external socio-cultural environmental factors than intrinsic characteristics of the healthcare system. It is a very important finding that indicates that a revolutionary paradigm shift will require the evolution of the new attitudes towards the new concept of the healthcare system organization for those who still remain in the comfortable zone of the previous (analogue system). We need to redefine the concept of healthcare. Hence, the results of this study fully justify my choice of the focus for this book being centred on the development of new mindset. It is only when we succeed with a new mindset that we will be able to embrace the future opportunities of digital health. It seems that there is significant risk that some healthcare consumers are not ready for that revolutionary change of rejecting such benefits as extended life expectancy or lower financial contributions. On the positive note, however, there is majority of young voters in favour of a digital transformation no matter the potential gains or losses. Their commitment to becoming empowered consumers of the healthcare system is surely the example to be utilized to attract the changes for others too. Although this study has its valuable insights, it is not free from limitations. First, it has been conducted on a limited sample in the Polish setting. Hence, generalizability of results for other jurisdictions may be somewhat limited. Second, conclusions are driven by the questionnaire design. The digital transformation

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has broad implications and it was not feasible to study all of them; that’s the reason for the limitations of study generalizability to other settings as well. For example, the exchange of life expectancy for other gains and losses in the trade-off exercise may yield different results. Hence, the limitation of the current research from the standpoint of its reproducibility. Despite these limitations, there are important innovative aspects of the conducted study worth highlighting. First, to my understanding both SWF and discrete choice experiment (DCE) approaches were used for the first time to understand responders’ preferences towards specific features of the digital healthcare system. These are the techniques very often used for eliciting preferences in the healthcare sector. The word “discrete” indicates that the choice is discrete in its nature, meaning that it is only possible to choose one alternative. It assumes that a patient given a choice will prefer one option over another and will attempt to maximize their satisfaction or “utility.” It simulates actual decision-making because it requires respondents to make trade-offs in a choice context. Second, to my knowledge, this was the first attempt of its kind to discuss the digital transformation in the healthcare sector to such great extent as the scope of the current study. As a consequence, however, the study results are not easy to validate against other research of a similar nature. The overarching objective of the study was to address the question of what drives attitudes towards digital health. Drawing on the conclusions, there are some recommendations for future research that can be elicited on how to support greater adoption of digital health for better health outcomes. The surprising finding was that there is low propensity to shift the preferences from either the digital or analogue model for better life expectancy. Therefore, the recommendation is that it is more important to understand the sources of resistance towards the digital transformation among those who oppose the change. There are two major points of departure for future research. First, it is to identify approaches that would decrease the fear of change. Interestingly, almost as twice as many of the proponents of the digital model compared to proponents of the analogue model were in favour of remote surgical procedures. At the same time, the latter group also expressed much greater preferences towards the continuous reliance on human interaction in the hospital settings. Such findings are similar to other studies like the one conducted in Australia among 525 patients with a planned admission to the hospital in 2018. It indicated differences in approach towards the methods of communicating with the healthcare team. Nearly 60% of patients preferred to return to the hospital to consult about symptoms, while only 30% felt comfortable to connect remotely communicating about their recovery to ensure individual needs are appropriately met.7 Still, the pandemic made remote connection with healthcare providers a necessity, not a choice. Among many examples that indicate that a radical shift can be in the telemedicine platform is Amwell, the telehealth software vendor, who noted a 2000% increase in visits on its platform in the first three months of the pandemic alone.8

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Surely telemedicine is most cited in the context of successful changes towards the digital transformation of healthcare; however, the premise of this book and the conducted research goes further than that. The most needed changes are actually related to the mindset shift of healthcare customers to allow them to more easily embrace technological advancements. That performed study confirmed earlier deliberations related to the importance of trust-building activities. To develop a holistic healthcare system based on individual sovereignty, we need greater collaboration so patients do not feel left insecure and on their own. Not surprisingly, the Accenture US survey from 2000 highlighted two important findings leading to the greater adoption of digital solutions. It is about the active engagement of healthcare professionals and trust building into data protection.9 “Strategic leaders will need more information on how to build wearable devices into pathways while making the best use of investment, particularly concerning the direct provision of information from patient to clinician.”10 What is surprising, however, is that kind of collaboration is not flourishing as of yet to its full extent. An example is the systematic scoping review of the published research regarding the adoption of home-based cardiac telerehabilitation that focused on the collaborative efforts in the development of technology and, more precisely, on usability, utility, acceptability and acceptance among patients.11 It turned out that out of 18 home-based cardiac telerehabilitation programs studied, less than one-fifth of included end users’ preferences in the design and development. The same was found in the systematic literature review of the use of AI in support of the care of persons with Alzheimer’s disease and related dementias (ADRD). Thirty studies were reviewed.12 The findings indicated clearly that the quality of conducted research on patients’ preferences was low with many just descriptive, exploratory in nature, small convenience samples and lacking theoretical guidance. It is a bizarre finding if one considers the importance of unmet needs and user-friendliness as discussed earlier. It is pretty difficult to expect the growing acceptance of digital health if the patients’ preferences are not taken into consideration in the design of such solutions. Subsequently, the mindset of being ready to adopt advancements of the digital transformations will remain remote. On that note, it is important to recall another systematic literature review that identified 22 studies assessing 17 web-based electronic symptom self-reporting systems (e-SRS) dedicated to oncological patients.13 The lack of technology compatibility was unsurprisingly a significant barrier to patients’ adoption of home-based e-SRS. The lack of self-confidence and trust in self-managing their health were qualitatively defined as important factors contributing to the use of e-SRS. A very similar conclusion of deficit in customer-driven design was found in the systematic review published in 2021 on technology acceptance in healthcare. Of 142 empirical studies, 21 were published in 2016 but only 10 in 2019. The deficiency relates to studying relevant insights that have some geographical discrepancy. Of all identified publications, more than half were related to Asian

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settings, with Taiwan alone being the key jurisdiction with as many as 30 studies published. The analysis showed that behavioural intention to use technology is the most analysed factor in evaluating the acceptance of different technologies in healthcare, which indicates the importance of the perceived ease of use and perceived usefulness of various technologies.14 Surprisingly, in only 14 of 142 studies did patients actively participate in the design of the technology. Looking more deeply into what constitutes the preferences towards the digital solutions, another interesting example is the analysis of 21 qualitative studies across patients with chronic musculoskeletal pain regarding their willingness to engage in tele interventions. The following identified factors are listed as barriers for adoption of such a new digital service: (1) impersonal, (2) technological challenges, (3) irrelevant content and (4) limited digital (health) literacy, whereas the empowerment and “at my own pace, space and place” were identified as enablers for patients’ willingness to use teleservices.15 It seems intuitive that technology compatibility and perceived benefits of using the system are truly the important factors that help acceptance of the new technology and subsequently develop a mindset of engagement in pursuit of better health outcomes in the digital era. The review of 20 publications including the views of 349 participants (age range was 51–94 years) was synthesized to understand the user experience with a wearable device as well as the factors that contribute to the acceptance and use of wearable devices.16 Again, the results are very intuitive and relate to the user’s expectations, needs, ease of use and functional ability of the user. The user’s appraisal of these factors determines the level of value added by the device to the life of each user. A systematic literature review focusing on the role of mobile app interventions for hypertension self-management was published from 2013 to 2020.17 Of 21 studies included, only seven interventions provided two-way communication between users and a healthcare professional and just four studies clearly defined engagement. What should be very much positively noted is the encouraging results from those few studies. It was actually proven that there was a statistical relationship between engagement and biomedical outcomes (i.e., weight or blood pressure change), indicating that higher engagement was associated with significantly better biomedical results. The growing collaborative efforts to foster patient engagement not only allow the technology to become more user-friendly and meet the patient’s needs, it helps to develop empowerment, which leads to individual sovereignty too. Has anyone tried to recover from heart-break? If yes, you know what I am referring to. What’s the best cure? For me, it has always been to become more independent and self-focused. I used to travel, meet new people or try a new hobby to fill the void. Essentially, it is nothing more and nothing less than empowerment. When we feel our independence, we can develop the feeling of ownership. For me, that is the greatest difference between empowerment and individual sovereignty. The latter is the stage of fullness defined by ownership.

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So, can the empowerment of individual sovereignty actually reduce fear or change? Let’s hope so. It brings me nicely to the second recommendation, which is how to embrace change. It is more precisely about data sharing and openness for financial nudges. Interestingly enough, compared to the proponents of the analogue model, the enthusiasts of the digital model were almost three times more often willing to accept solutions related to compliance with health recommendations or data sale for research purposes. Looking for potential solutions to change the attitudes towards more engagement and openness of those who remain reluctant to adopt such a data-sharing mode, we can reference the results of other studies. Among the mix of 1963 Brazilian and Danish young responders (only 12.56% above 37 years), as many as 85.38% expressed their interest to share personal health data for research purposes. Still, two-thirds of study participants worried about ethical issues, profit-making without their consent, cyberattacks and blackmail and expressed other concerns as well. Interestingly enough, previous participation in health research studies and awareness of examples of existing repositories in a statistically significant manner were identified as key influencers on the attitudes towards digital health research.18 The future is bright as long as we decrease the fear of change and embrace it. It requires more effort defined not only on the technological advancements and their adoptability from system preparedness interoperability, but most importantly on mindset change. There is still work to be done in order for those who fear change allow to development of this much-needed trust and engagement. Our focus should not be only on those who are already enthusiastic about digital health but most importantly on those who are less so. The solutions should address their unmet needs, which might be completely different and not disease specific. It is the only way to transform healthcare towards a more sustainable future, but also to mitigate the risks of the inequality of access. Let’s start with equity of access to information and a holistic view of healthcare. While we succeed with the mindset in that respect today, we will be able to adopt Healthcare 5.0 tomorrow without fear for change. In this chapter, the cross-sectional questionnaire study was presented. Its objective was the proof of concept study with 320 representatives of generation Millennium and Z already active in the labour market. The responders were recruited from Poland. The choice of Central Eastern Europe provided a very special insight into the mindset of those who were born in the era of smartphones as well as mindset of those who are observing healthcare challenges on a greater scale than in more affluent Western European countries. It is the generation of potential beneficiaries of Healthcare 5.0. It can be concluded that preferences towards the healthcare system are driven more by past experiences and potentially pre-defined beliefs, and less so by opportunities brought with the digital transformation. There is an urgent need to initiate educational endeavours to teach future users of Healthcare 5.0 how to benefit from digital health solutions!

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Appendix Questionnaire Part 1. General questions Please provide following information. Age, Gender, student/self-employed/employed, any chronic health condition yes/no, any history or severe disease such as cancer, etc., yes/no, any experience with close family member with severe health condition yes/no, your health self-assessment on the scale on 1 (worst)–10 (best), your self-assessment of financial independence on the scale on 1 (worst)–10 (best). How much are you satisfied with the healthcare system on a scale of 1 (worst) to 10 (best)? Please describe how you perceive the current healthcare system: 1. How much do you anticipate to pay monthly in form of health taxes of healthcare insurance? …. (please provide % of salary) 2. Do you get any profilax program free of charge? If yes please describe 3. How much do you expect to have to wait for GP visit? Less than a week More than a week Longer 4. Do you have to pay for it? Yes/no 5. How much do you expect to wait for specialist visit? Less than a week More than a week Longer 6. Do you have to pay for it? Yes/no 7. How much do you anticipate to wait for hospital visit? Less than a month Between one and three months Above three months 8. Do you have to pay for it? Yes/no 9. On average are you free to choose your doctor? No Rather no Rather yes Yes 10. How much do you expect to spend OOP monthly overall as part of your salary on average? Less than 5% Between 5% and 10% More than 10%

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Part 2. Digital healthcare model description The digital healthcare of tomorrow. Let’s imagine a new healthcare digital system (digital model) has been made available as alternative to the established one as per your description from your answers provided earlier (analogue model). The digital model is based on digital technologies which offers fast access to online healthcare services, less human interactions but more focused on your healthcare needs, defined by continuous health monitoring with multiple mobile app and body sensors. The digital model is the virtual healthcare system that allows you to choose from the most innovative health solutions and the most up-to-date scientific data directly from your phone. There are almost no waiting times and free access to any modern treatment defined by your health needs delivered mainly remotely. But it is the system that requires you to have a full autonomy in deciding about your health matters. You will need to activate yourself and become your own decision-maker in your healthcare journey. The key principle is that you will get access to the majority of healthcare services and health recommendations individualized specifically for your on your phone or delivered home to you. You will become your own decision-maker. There is no family doctor available for you. Instead a VA will be in constant contact with you. The most important thing is that VA is not human being. It is artificial intelligence developed based on the scientific research that has certified qualifications. It is able to define your clinical pathway based on your health symptoms with the support of clinical studies and clinical guidelines. You will get pop-up messages when you need to change your diet or sleep habits including monitoring of your addiction such as alcohol and nicotine. She (he) will advise you on any diagnostic tests and any other treatments adjusted to your health problems and organize them remotely for you (with specific diagnostic kit delivered home to you for self-use). Thanks to availability of constantly updated databases from many scientific sources, VA will provide you with best personalized healthcare recommendations updated frequently and adjusted to your health needs to allow you to take care of yourself. To activate it, you will need to perform number of tests including genetic tests at the starting date to assess your health risks, healthcare needs and health habits. A tailored-made health monitoring program will be then developed for you to help you to prevent or at least minimize health risks as well. Based on your health continuous monitoring and analysis of your data, you will get a set of health recommendations to follow to prevent and monitor your health. For instance, you will be equipped with wrist wearable (or chip) that monitors your heart rate, temperature and sleep quality and physical activity as well. You will need to wear it 24/7. You will have to take photos of your daily meals with a special app on your phone too. There may be additional sensors installed at your home too. There is a compulsory annual or biannual health check-up when you have

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to provide blood sample from your finger with the equipment delivered home to you. Sometimes ultrasound performed with your phone will be compulsory. VA ordinates your medications and prompts you to ensure your full compliance. The VA will book your appointments for video consultations with specialists only in case of unresolved issues, disease with major consequences, risk of cancer or life-threatening condition. If so, you will get a list of number of worldwide experts to choose from available in healthcare system. In the unlikely event you need hospital visit for surgical procedure, it will search for you the best option according to your preferences such as waiting time, experience and distance to home. The majority of hospital procedures will be conducted at closest to your choice of location. Most likely, it will be performed remotely with support of robotics by the best clinicians. The time in the hospital will be limited to the bare minimum as VA will order additional sensors monitoring at your home bed after hospital stay constant connection with the healthcare professional in case of emergency.

Part 3 hypothetical question Which healthcare system would you choose the “healthcare system of today” as you described earlier (analogue model) or the digital model as it was described above provided that both would cost you the same. Assume however that your choice of one of them will impact your life span. Please assume that the analogue model estimates the life expectancy based on generally available statistics for your age and gender and takes into consideration multiple determinants such as access to treatment, waiting times, etc. characterizing your healthcare system of today as you described earlier. The digital model estimates your life expectancy based on your individual health risk profile and takes into consideration your autonomy in controlling your health habits as described above. You are able to achieve higher life expectancy compared to the analogue model if you become fully proactive in taking responsibility for your health and compliant with all health recommendations throughout your live or worse if you don’t follow them (min values). Which model would you choose given that the remaining life expectancy in both max and min is as shown in the matrix below? Take into account the fact that both the average life expectancy and distribution (i.e., the difference in life expectancy between max and min for both models) when you make your choice.

Part 4 Description of healthcare model Please choose the best description of the digital healthcare model that meets your preferences. Your decision about digital healthcare model. Please note that your healthcare program will be defined so that your allocated amount is sufficient to cover potential future costs of your health issues as well as healthcare system organization in the solidarity mode that ensures the basic services for everyone.

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Would you be willing to opt out from the current system for a new digital model provided that you are allowed to pay less each year if you’re fully compliant with annually released healthcare recommendations tailored to your health risks profile (up to 20%)? Would you be willing to opt out from the current system for a new digital model provided that you have to pay more each year if you’re not fully compliant with annually released healthcare recommendations tailored to your health risks profile (up to 20%)?

NOTES 1 García-Miralles, Esteban. “The Crucial Role of Social Welfare Criteria and Individual Heterogeneity for Optimal Inheritance Taxation,” The BE Journal of Economic Analysis & Policy, vol. 20, no. 2, 29 Jan. 2020, https://doi.org/10.1515/bejeap-2019-0274 2 Johannesson, Gerdtham. “A Note on the Estimation of the Equity-Efficiency TradeOff for QALYs,” Journal of Health Economics, 1 June 1996, pp. 359–368, https://doi. org/10.1016/0167-6296(96)00005-7 3 PKJPA = Program Kontroli Jakości Pracy Ankieterów = Interviewer Quality Control Program; OFBOR = Organizacja Firm Badania Opinii i Rynku = Polish Association of Public Opinion and Marketing Research Firms. 4 Mean marginal trade-off = (0.0545 + 0.0021 * relative_difference – 0.6023*is_ working – 0.3215*had_hard_disease)/–0.1579. 5 https://pubmed.ncbi.n lm.nih.gov/?ter m=%28%28preferences%5BTitle%2F A b s t r a c t % 5 D % 2 9 +A N D + % 2 8 d i g i t a l % 5 B T i t l e % 2 FA b s t r a c t % 5 D % 2 9 % 29+AND+%28health%5BTitle%2FAbstract%5D%29&filter=pubt.systematicreview 6 Whitelaw, et al. “Barriers and Facilitators of the Uptake of Digital Health Technology in Cardiovascular Care: A Systematic Scoping Review,” European Heart Journal. Digital Health, vol. 2, 4 Feb. 2021, pp. 62–74, https://doi.org/10.1093/ehjdh/ztab005; https://pubmed.ncbi.nlm.nih.gov/34048508/ 7 Alexander, et al. “Patient Preferences for Using Technology in Communication about Symptoms Post Hospital Discharge,” BMC Health Services Research, vol. 21, 15 Feb. 2021, https://doi.org/10.1186/s12913-021-06119-7 https://bmchealthservres. biomedcentral.com/articles/10.1186/s12913-021-06119-7 8 Finnegan. ”Telehealth Booms Amid COVID-19 Crisis; Virtual Care Is Here to Stay,” Computer World, 27 Apr. 2020, https://www.computerworld.com/ article/3540315/telehealth-booms-amid-covid-19-crisis-virtual-care-is-here-tostay.html 9 Safavi K, Kalis B. How can leaders make recent digital health gains last? US findings. Accenture 2020 Digital Health Consumer Survey. 2020. [2021-01-30]. https://www. accenture.com/_acnmedia/PDF-130/Accenture-2020-Digital-Health-ConsumerSurvey-US.pdf#zoom=40. 10 “Shaping the Future of Digital Technology in Health And Social Care,” The King’s Fund, Apr. 2021, https://www.kingsfund.org.uk/sites/default/files/2021-04/Shaping %20the%20future%20of %20digital%20technolog y%20in%20health%20and% 20social%20care.pdf 11 Ramachandran, et al. “Technology Acceptance of Home-Based Cardiac Telerehabilitation Programs in Patients with Coronary Heart Disease: Systematic Scoping Review,” Journal of Medical Internet Research, vol. 24, 7 Jan. 2022, https://doi. org/10.2196/34657 12 Xie, et al. “Artificial Intelligence for Caregivers of Persons with Alzheimer’s Disease and Related Dementias: Systematic Literature Review,” JMIR Medical Informatics, vol. 8, 20 Aug. 2020, https://doi.org/10.2196/18189

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13 Cho, et al. “Acceptance and Use of Home-Based Electronic Symptom Self-Reporting Systems in Patients with Cancer: Systematic Review,” Journal of Medical Internet Research, vol. 23,12 Mar. 2021, https://doi.org/10.2196/24638 14 AlQudah, et al. “Technology Acceptance in Healthcare: A Systematic Review,” Applied Sciences, 9 Nov. 2021, https://www.mdpi.com/2076-3417/11/22/10537/htm 15 Fernandes, et al. “At My Own Pace, Space, and Place: A Systematic Review of Qualitative Studies of Enablers and Barriers to Telehealth Interventions for People with Chronic Pain,” Pain, vol. 163, Feb. 2022, pp. 165–181, https://doi.org/10.1097/j. pain.0000000000002364 16 Moore, et al. “Older Adults’ Experiences with Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis,” JMIR mHealth and uHealth, vol. 9, 3 June 2021, https://doi.org/10.2196/23832 17 Cao, et al. “mHealth Interventions for Self-management of Hypertension: Framework and Systematic Review on Engagement, Interactivity, and Tailoring,” JMIR mHealth and uHealth, vol. 10, 2 Mar. 2022, https://doi.org/10.2196/29415 18 Nunes Vilaza, et al. “Public Attitudes to Digital Health Research Repositories: Cross-Sectional International Survey,” Journal of Medical Internet Research, Oct. 2021, https://doi.org/10.2196/31294

REFERENCES Alexander, et al. “Patient Preferences for Using Technology in Communication About Symptoms Post Hospital Discharge.” BMC Health Services Research, vol. 21, 15 Feb. 2021, https://doi.org/10.1186/s12913-021-06119-7 https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-021-06119-7 AlQudah, et al. “Technology Acceptance in Healthcare: A Systematic Review.” Applied Sciences, 9 Nov. 2021, https://www.mdpi.com/2076-3417/11/22/10537/htm Cao, et al. “mHealth Interventions for Self-Management of Hypertension: Framework and Systematic Review on Engagement, Interactivity, and Tailoring.” JMIR mHealth and uHealth, vol. 10, 2 Mar. 2022, https://doi.org/10.2196/29415 Cho, et al. “Acceptance and Use of Home-Based Electronic Symptom Self-Reporting Systems in Patients with Cancer: Systematic Review.” Journal of Medical Internet Research, vol. 23, 12 Mar. 2021, https://doi.org/10.2196/24638 Fernandes, et al. “At My Own Pace, Space, and Place: A Systematic Review of Qualitative Studies of Enablers and Barriers to Telehealth Interventions for People with Chronic Pain.” Pain, vol. 163, Feb. 2022, pp. 165–181. https://doi.org/10.1097/j. pain.0000000000002364 Finnegan “Telehealth Booms Amid COVID-19 Crisis; Virtual Care Is Here to Stay.” Computer World, 27 Apr. 2020, https://www.computerworld.com/article/3540315/ telehealth-booms-amid-covid-19-crisis-virtual-care-is-here-to-stay.html García-Miralles. “The Crucial Role of Social Welfare Criteria and Individual Heterogeneity for Optimal Inheritance Taxation.” The BE Journal of Economic Analysis & Policy, vol. 20, no. 2, 29 Jan. 2020, https://doi.org/10.1515/bejeap-2019-0274 Health Consumer Survey US FINDINGS, https://www.accenture.com/us-en/insights/ health/leaders-make-recent-digital-health-gains-last “How Can Leaders Make Recent Digital Health Gains Last?” Re-Examining the Accenture 2020 Digital. h t t p s : // p u b m e d . n c b i . n l m . n i h . g o v / ? t e r m = % 2 8 % 2 8 p r e f e r e n c e s % 5 B T i t l e % 2 FA b s t r a c t % 5 D % 2 9 +A N D + % 2 8 d i g i t a l % 5 B T i t l e % 2 FA b stract%5D%29%29+AND+%28health%5BTitle%2FAbstract%5D%29&filter=pubt. systematicreview

162  Case study

Johannesson, Gerdtham “A Note on the Estimation of the Equity-Efficiency Trade-off for QALYs.” Journal of Health Economics, vol. 15, 1 June 1996, pp. 359–368, https://doi. org/10.1016/0167-6296(96)00005-7 Moore, et al. “Older Adults’ Experiences with Using Wearable Devices: Qualitative Systematic Review and Meta-Synthesis.” JMIR mHealth and uHealth, vol. 9, 3 June 2021, https://doi.org/10.2196/23832 Nunes Vilaza G, Coyle D, Bardram JE “Public Attitudes to Digital Health Research Repositories: Cross-Sectional International Survey.” J Med Internet Res, 2021;23(10): e31294 doi: 10.2196/31294 PMID: 34714253 PMCID: 8590194 OFBOR = Organizacja Firm Badania Opinii i Rynku = Polish Association of Public Opinion and Marketing Research Firms. PKJPA = Program Kontroli Jakości Pracy Ankieterów = Interviewer Quality Control Program. Ramachandran, et al. “Technology Acceptance of Home-Based Cardiac Telerehabilitation Programs in Patients with Coronary Heart Disease: Systematic Scoping Review.” Journal of Medical Internet Research, vol. 24, 7 Jan. 2022, https://doi.org/10.2196/34657 “Shaping the Future of Digital Technology in Health and Social Care.” The King’s Fund, Apr. 2021, https://www.kingsfund.org.uk/sites/default/files/2021-04/Shaping%20 the%20f ut ure%20of %20d ig ita l%20technolog y %20in%20hea lth%20and%20 social%20care.pdf Whitelaw, et al. “Barriers and Facilitators of the Uptake of Digital Health Technology in Cardiovascular Care: A Systematic Scoping Review.” European Heart Journal. Digital Health, vol. 2, 4 Feb. 2021, pp. 62–74, https://doi.org/10.1093/ehjdh/ztab005; https:// pubmed.ncbi.nlm.nih.gov/34048508/ Xie, et al. “Artificial Intelligence for Caregivers of Persons with Alzheimer’s Disease and Related Dementias: Systematic Literature Review.” JMIR Medical Informatics, vol. 8, 20 Aug. 2020, https://doi.org/10.2196/18189

CONCLUDING REMARKS

No matter the jurisdiction, there are two key foundations of the healthcare system structured similarly across borders. The first is about the relationship between the patient and the clinician. The second is about the relationship between clinician and budget holder (insurance company or government). Both are based on the same underlying rationale – the axiom of information asymmetry. To fill the gap of not-knowing, medical professionals are trust agents for patients with respect to the treatment outcomes and third-party agents with respect to decisions on optimal the treatment available within the payer’s budget. As far as the first is concerned, the individual sovereignty that we normally execute in different decision-making processes, searching for the ways to meet our consumer’s needs, is replaced with the principle of paternalism in healthcare settings. As a consequence of not knowing (information asymmetry), we turn to medical professional for help in the event of health issues. With the underlying trust into the clinician’s knowledge, we assign him or her almost full accountability for the decision-making with respect to our health. Most of the time, they are the sole source of any information about our health problems. In turn, we are asked to be compliant with the doctor’s recommendations. As far as the relationship between clinicians and budget holders is concerned, the latter is assigned full responsibility to allocate our health funds with the principle of solidarity to protect everyone from the catastrophic healthcare payments in the event of disease onset. With the assumption of information asymmetry, both the uncertainty about our future healthcare needs and uncertainty about treatment outcomes are assumed. Third-party agents, either private or public are our safeguard who protects us from the financial stress and the risk of bankruptcy in the event of a major health issue. It is believed that third-party agents need to ensure enough financial resources available to treat everyone in case of the medical need. Therefore, we accept that our financial contribution will DOI: 10.4324/b23291-6

164  Concluding remarks

be pooled with others in solidarity mode to protect one another in the event of costly health problems. As a result, the holistic view on health is replaced with pragmatic public budget perspective in the process of allocation of limited healthcare resources. So, we lived our lives freely under the veil of uncertainty. Due to limited financial resources, we were taught that access to healthcare is a privilege granted only when symptoms of any health abnormality appear and are justifiable. We named family doctors as gatekeepers to protect from the overconsumption of medical services (named as moral hazard). The paternalistic system meant a mainly reactive mode focused on the optimal allocation of limited healthcare resources and patient compliance with doctor’s recommendations. Despite some initiatives targeting the prevention of specific health problems, individual sovereignty, understood as proactiveness to prevent incidences of disease, is not within the remit of healthcare system. Since there is a perceived risk of the misuse of limited healthcare resources, healthcare needs are defined from a third-party payer perspective. Namely, we structured the financing system towards medical technologies defined from the standpoint of their meaningfulness assessed by clinicians. Given limited healthcare resources, we needed to ensure that we offer medical aid at least with basic needs for everyone. With the pragmatic budget-holder perspective, we established the allocative criteria to maximize health gains as well. As a result, the greater the incremental treatment effect understood in a clinically meaningful manner, the greater the likelihood for a given medical technology to be reimbursed and accessible to patients. Consequently, I believe that the healthcare system was too focused on treating the disease within the basket of medical technologies designed from the perspective of the limited financial resources of the third party. The budget-holder perspective incentivizes innovation that can treat disease, not innovation that can lower the incidence of disease. The latter is related to individuals’ behaviour and well-being before they became patients or, more precisely, before they become eligible to access the healthcare system. Everything has changed with the arrival of the digital revolution and the COVID pandemic. There will be a paradigm shift if we can open our minds to change. The emergence of data shows us that the healthcare system can do much more than just treat our diseases in a paternalist manner. The growing amount of data gives us the opportunity to redefine the role of prevention. Not only can the digital era help us to predict, but also consequently extend the border of the healthcare system. The digital revolution teaches us that healthcare is not only how to treat but about how to holistically predict. Data-driven healthcare allows us to input personal behavioural data into machine learning algorithms to prevent and predict health problems. It can save lives and budgets by shifting the focus from treatment to prevention. In the data-driven healthcare system, evidence allows patients to limit the uncertainty and let everyone live life with full potential

Concluding remarks 165

to prevent negative circumstances and also full awareness of the consequences of not being healthy. In the era of data, the underlying rationale of the healthcare system as we know it – information asymmetry – can be retired. It is time for a new digital reality to establish new rules. In this book, I presented my own vision: Healthcare 5.0. The ecosystem of a digital network centred around patients born on the new axioms of individual sovereignty and a holistic view on health. At the core, there is the individual’s right to direct her/his own life. The value of healthcare services is defined holistically from the perspective of the patient. There will still be uncertainty in the beginning of the implementation of Healthcare 5.0, but will diminish with the growing availability of data and analytics capacity. Eventually, uncertainty will be replaced with prediction driven by the likelihood ratio. In other words, information asymmetry will be replaced with people’s right to make informed choices about their medical care. In Healthcare 5.0, the patient owns his individual ecosystem. The third-party agent will remain, but its role is to support each manufacturer in the holistic development of healthcare products. According to the WHO, “… Health systems are responsible for delivering services that improve, maintain or restore the health of individuals and their communities ….”1 In my opinion, the definition should be even more holistically focused on the maximization of individual capacity to live one’s life according to their self-made definition of happiness and motivation. Healthcare technology should not be a product designed with the criteria defined from the perspective of a budget holder whose limited financial resources simply limit the definition of health. Instead, it should be designed by the patient himself from the plethora of innovative solutions designed thanks to repository of unmet needs. The healthcare system will be a part of daily life to ensure we increase awareness of the risks and benefits of our daily habits. It is the system of continuous disease prevention based on algorithms that the patient uses on his own with his own health data. My vision of the future Healthcare system – Healthcare 5.0 – is a network of individual ecosystems. Each function as a self-driving car designed based on unmet needs and fuelled by the personal data of each patient. The key axiom of Healthcare 5.0 is a holistic view on health with individual sovereignty. It leads to the empowerment of patients. There are two specific governing rules: equal access to information and patient-centric health outcomes. Both are implementable thanks to available data and tools that allow everyone to achieve good health with less reliance on external clinical help and pharmaceuticals! Healthcare 5.0 is about getting smarter and living longer by using data-driven algorithms. Therefore in Healthcare 5.0, it is essentially data analytics that becomes the decision-making machine with the input provided by patients and validated by clinicians. Data being the fuel for the self-driving car changes the way the research and development of new health technologies as well as healthcare services is conducted before they are being made available to patients.

166  Concluding remarks

Healthcare 5.0 is built on a digital network not linked to any specific doctor’s office, hospital or pharmacy. The digital transformation changes the definition of the healthcare system from physical facilities to cloud-based data storage and data analytics algorithms. It is the ecosystem without boundaries. All our live activities are translated into data used to inform our health objective function developed with multiple determinants, each one using a separate AI algorithm. The complexity of our health function is defined by our genetic predisposition and behavioural preferences to ensure the risk of onset specific diseases are mitigated and health habits developed to capture health gains. Continuous data collection feeds and updates our health function, informing each of our AI algorithms with a reinforcement learning function. The personalized healthcare ecosystem sets up risks and incentives that help everyone to predict, prevent and treat. The virtual medical assistant gives us support and motivation and guides us towards the healthcare technologies and services relevant for our particular needs. In order for Healthcare 5.0 vision to be implementable, there is still a long way to go. Improvements are needed, especially with the digital literacy and cybersecurity. More importantly, there is a need for a mindset shift to develop the cognitive trust in data and in the new daily habits for a happy life in harmony with technology. This is essentially what this book tried to argue. The paradigm shift to Healthcare 5.0 requires a new social contract that helps each citizen to design his own healthcare ecosystem. It is like designing your own self-driving car. It may be different for anyone, as each has their own perception of individual sovereignty and a holistic understanding of health. The mindset shift is required to lead us from a paternalistic to an individual sovereignty healthcare system. It requires everyone to embark on the journey to understand our own individual needs, desires and expectations. In essence, the unmet needs will be the cornerstone of Healthcare 5.0. It is personal unmet needs, life objectives and expectations that will design an individual ecosystem focused not on how to treat, but rather how to prevent. As such, it won’t be clinicians but data that will tell us whether we are successful at preventing diseases. Hence, we need to design Healthcare 5.0 as self-driving car, so it captures the right non-health related information that truly informs our health objective function. In data-driven healthcare, the success of treatment depends on prevention. Multiple factors related to patient behaviour will be taken into consideration. Still, data-driven healthcare services need to be adjusted to our needs and our liking. For patients to become the decision-makers, healthcare has to be redesigned to become patient-centric and holistic. This will be a healthcare system of connected devices with algorithms, healthcare services and technological solutions used by each one. The fuel of Healthcare 5.0 is data. For the system to become smarter and more supportive in our health pursuits, data is essential. Digital technologies are hungry for data and interconnectivity across different data outlets. They require our information as input and produce data as the output. Our self-driving car will continuously require new information to navigate safely and connect

Concluding remarks 167

with other vehicles to avoid a crash and ensure the fast transfer of data across all active cars on the road. Therefore, it is not technology that constitutes the success of Healthcare 5.0. It is data. In fact, each individual contributes to its success by generating data through the greater adoption of digital solutions. Individual sovereignty helps us to develop a holistic healthcare ecosystem. In the digital system, it means more data by the greater adoption of digital technologies. As long as we allow our data to be further utilized for future research and development of future digital technologies, we contribute to the success of Healthcare 5.0. Hence, I actually believe that a mindset shift towards individual sovereignty and a holistic perspective on health will not only be possible thanks to the digital revolution, but it will also be the accelerator of the digital revolution too! Data will provide the insights into our unmet needs that can be used by technology developers to invent a new innovation. No more limited healthcare resources. Data are limitless. Data will help to fuel the innovation and make new technologies more reliable and less expensive. Healthcare 5.0 is built on the source of renewable energy. It is our sustainable future. Let’s not waste this chance!

Note 1 WHO. (2000). The world health report 2000. Health systems: Improving performance. Geneva, Switzerland: WHO.

INDEX

Note: Bold page numbers refer to tables. ActiGraph actimetry sensor 79 allocative efficiency 2, 3 Alzheimer’s disease and related dementias (ADRD) 154 ambulatory care 80–85 amyotrophic lateral sclerosis (ALS) 70 analogue model 128–129, 130, 132, 136, 138–140, 149, 152–153, 156, 158–159 Aristotle 44 Arrow, K. J. 1–2, 4–6 artificial intelligence (AI) 57, 70, 78, 81, 91–92, 104, 114, 141, 154, 158, 166 Bentham, J. 2 Bergson–Samuelson social welfare function 3 BiliScreen 79 blue zones projects 25–26 bone mineral density (BMD) 71 Buettner, D. 25–26 cardinally utility 3 CART-T therapies 101, 110–111 Central Eastern Europe 156 Codes of Conduct 56 cognitive behavioural therapies (CBT) 21, 82, 95 cognitive trust 40, 53, 55–57, 91, 166 consequentialism 4, 10, 45, 57, 103 constant elasticity of substitution (CES) 129

consumer wearable health device (CWHD) 57 CoronaMelder app 59 cost-effectiveness efficiency 2 COVID-19 pandemic 11, 13–15, 23–24, 29, 54, 59, 72, 89, 94 Cunningham, S. 4 Danish Twin Study 25 data sharing 18, 51, 53, 70, 74, 84–85, 110, 112–114, 149, 151, 156 data transparency 107, 109–110, 112 decision-making x, 2, 4–6, 10, 12, 14, 16–17, 26, 29, 40–42, 54–56, 59–60, 73–74, 84, 86, 96–98, 101, 103, 111, 132, 153, 163, 165 De Digitale Stad (DDS) 97, 107 delivery selection rating 144 depression 23–25, 28, 82 digital communication 49 Digital Competence framework (DigComp) 52 Digital Economy and Society Index (DESI) 51 Digital Health study 130, 138–140, 149 digitalization 18, 29–30, 70, 88 digital model 127–130, 130, 132, 136, 138, 139, 140, 140, 149, 152–153, 158–160 digital natives 52

Index 169

digital revolution 41, 69, 164, 167; cognitive trust 55–57; to holistic health 57–60; to individual sovereignty 52–57; mindset shift 49–52 digital transformation 19, 28–30, 41, 50, 55, 57, 59–60, 69, 73–75, 94, 98, 104, 149, 152–154, 166 discrete choice experiment (DCE) 106, 153 distributed denial-of-service (DDoS) attacks 92 early-onset osteoporosis (EOOP) 71 Electronic Health Records (EHR) 51, 84 electronic symptom self-reporting systems (e-SRS) 154 EMA 54–55 Emma, C. 56 Estonia 15, 55, 84 European Health Interview Survey 24 Fast Healthcare Interoperability Resources (FHIR) 84 FDA 54–55, 79, 90–92 Federal Office of Public Health (FOPH) 58 financial contribution 128, 132, 136, 149, 152, 163 financial independence 128, 132, 133, 157 first episode of nonaffective psychosis (FEP) 45 FL 92–93 fractional factorial design 47 Framingham Heart Study 91 free of charge 77, 136, 148 General Data Protection Regulation (GDPR) 55, 85, 92, 130 General Practice Data for Planning and Research (GPDPR) 53 Gerdtham, U. 129 google health engine 76–78, 80, 81 GP Extraction Service (GPES) 53 happiness (Aristotle) 44 healthcare 5.0 69–73, 127–128, 156, 165–167; ambulatory care 80–85; community of patients 98; data sharing culture 112–114; digital health account 105–109; financing model 93–95; google health engine 76–78; governing rules of 73–76; hospital care 85–89; insurance 103–105; outcome-based payment 96–97; power of action 76;

pricing and reimbursement in 98–103; repository of preventive measures 78–80; risks and disadvantages 111–112; safety 90–93; unmet medical needs 95–96 Healthcare Information and Management Systems Society (HIMSS) 54 healthcare, origins of 1–2 healthcare system 1–7, 39, 58; digital transformation 17–21; satisfaction evaluation 135; sociodemographic characteristic 138 health economics 1–7, 40, 49, 103, 112 health education impact questionnaire (HeiQ) 42 health information exchange (HIE) 84–85, 89 Health Insurance Portability and Accountability Act (HIPAA) 85 health self-assessment 134 Hedonistic Utilitarianism approach 44 HIMSS Market Intelligence 93–94 holistic approach 24–30, 39–40, 44–48, 50, 58–59, 73–76, 83, 86–87, 94–95, 98, 102, 109–111 holistic health 39–41; digital revolution to 57–60; mind shift towards 44–49 hospital care 85–89 Human Capital and Employment Unit ( Joint Research Centre) 52 Huttenlocher, D. 70 IMF 13 Indiana Cancer Pain and Depression (INCPAD) trial 28 individual sovereignty 3, 5, 29–30, 39–41, 155–156; digital revolution 17–21, 52–57; governing rule 73–76; greater reliance on 13–17; mindset shift towards 41–44; paternalism and 9–12 information asymmetry 1, 4–5, 9–10, 12, 15, 22, 29–30, 41, 54, 73, 104, 163, 165 insurance provider 101, 105–108 International Patient Decision Aid Standards (IPDAS) 42 Johannesson, M. 129 Kant, I. 42–44, 50, 53, 73–75 Keidanren 69, 114 Kissinger, H. 70 Kolasa, K. 132 Kuhn, T. S. 1, 6–7, 15, 25, 39–41, 54, 72

170  Index

libertarian paternalism 11–13 life expectancy 22–23, 25, 39, 49, 95, 99–100, 105–106, 127–129, 138, 152–153, 159 logistic regression 129, 138, 140 machine learning (ML) 83, 91, 108–109 Manual of Political Economy (Pareto) 3 marginal rate of substitution (MRS) 128–129 market failure 4–6, 10–13, 15, 22, 41, 54 maximum likelihood method 129 McKinsey 52, 84 MDPQ see Mobile Device Proficiency Questionnaire (MDPQ) mean marginal trade-off (MTO) 129, 136, 138, 139 means paternalism 44 Medical Device Regulation (MDR) 90 medication delivery rating 146 MediLedger Project 85 mental capacity 43 Mental Health Coalition 55 methodological consequentialism 45 MIDATA 57 Mill, J. S. 2, 43–44 mindset shift 40, 166–167; digital revolution and 49–52; to holistic health 44–49, 57–60; to individual sovereignty 41–44, 52–57 Ministry of Health 59, 94, 105–106, 110 Mobile Device Proficiency Questionnaire (MDPQ) 52, 60 mRNA 72 MTO see mean marginal trade-off (MTO) Multiphase Optimization Strategy (MOST) 48, 100 myCancerRisk 79 MyData Global 56 neoclassical welfare 3, 6, 11 next-generation sequencing (NGS) 71 NHS 12, 53–54, 56, 77 Nintendo Switch fitness games 29 nudge 12, 130, 152, 156 NuraLogix 82 OECD 15, 24, 27, 91 OMRON app 83 1+ Million Genomes project (1+MG) 71 OPUS study 45, 47 out-of-pocket (OOP) 128, 132, 137

paradigm shift 1, 6, 17, 25, 39–40, 42, 49, 53–54, 57, 60, 69, 72, 152, 164, 166 Pareto, V. 3 Parkinson’s Disease (PD) 79 paternalism 1, 3, 6, 9–15, 26, 29, 39–40, 42–44, 74, 163 patient empowerment 14, 17–18, 20–21, 29–30, 40–42, 49, 57, 60 Patient Empowerment Programme (PEP) 16 patient engagement 16, 20, 41–42, 54, 92 Patient Reported Outcomes (PRO) 16–17, 28, 55 Philips BX100 Portable Biosensor 89 Philips Patient Monitoring Kit 89 polygenic risk score (PRS) 71 privacy-enhancing technologies (PETs) 85 QALY 5–6, 16, 21–22, 26–28, 39, 44, 46, 99–100 questionnaire study 127–128, 130, 152, 156–160 rationality 42–43 RCT 46, 48, 100 relative difference 129, 136, 139 ResApp Health 79 revealed preference 11 Ring Fit Adventure 29 robotic-assisted surgery (RAS) 88 Schmidt, E. 70 secure multi-party computing (SMPC) 93 Sequential Multiple Assignment Randomized Trial (SMART) 100 Smith, A. 2 social welfare function (SWF) 127–129, 153 society 5.0 69–70, 114 sociodemographic characteristics 46, 127–129, 132, 132, 138, 149 Software as a Medical Device (SaMD) 90 Software in a Medical Device (SiMD) 90 Software Pre-Cert Program 92 Sophia Genetics 78 spinal muscular atrophy (SMA) 71 State Secretariat for Education, Research and Innovation (SERI) 58 Stikker, M. 97 Summaries of Product Characteristics (SmPCs) 55 Sunstein 44 Supervisory Committee Digital Support 59

Index 171

Swiss Academy of Medical Sciences (SAMS) 58 Swiss Personalized Health Network (SPHN) 58 Switzerland 58, 59 technical efficiency 2 Technical University of Munich (TUM) 103 Thaler, R. 12 total product lifecycle (TPLC) regulatory approach 91–92 transparency 18, 54, 85, 106–107, 109–110, 112 Tweet2Quit 21 23andMe 79 24/7 health monitoring way 143 user-friendliness 102–103, 154 utility maximization 3–4, 9 vaccine hesitancy 13–15, 29, 53 Valencell 82, 83 value of statistical life (VSL) 27

Veblen, T. 2 Veritas Genetics 79 Veritas Intercontinental 79 virtual assistant (VA) 81–85, 89, 103, 105, 128, 130, 139–140, 141–146, 152, 158–159 virtual vs. physical rating 147 Wearable Sensor Health Technology (WSHT) 19 welfare approach 44 welfarism 4, 22, 103 whole exome sequencing (WES) 71 whole-genome sequencing (WGS) 71 willingness 80, 130, 148, 150, 151, 155 willingness-to-pay approach (WTP) 4, 16, 99, 102–103 Winkelmann, M. 113–114 Woebot group 82 Wojcicki, A. 79 World Health Organization (WHO) x, 15, 20, 22, 24, 26, 41, 55, 78, 88, 165 ZEUS Robotic Surgical System 88