Digital health has undergone an astounding transformation since the beginning of the COVID-19 pandemic. Almost all fields of medicine have adopted digital technologies to deliver patient care. Rapid advances in artificial intelligence, Big Data, augmented reality, Internet of Medical Things, connected devices, robotics, and algorithms will revolutionize digital health in almost all fields of medicine in the future. With the widespread use of smartphones, downloadable or internet-based applications (apps) will play a major role in the diagnosis of diseases, and monitoring and management of patients. However, the implementation of digital health is not without challenges and concerns. These include security and privacy of patient data, lack of a universal legal and regulatory framework, accountability, data ownership, and health inequity, among others. Despite these challenges and concerns, it is undeniable that digital health has revolutionized patient care and will continue to do so. The chapters of this book are examples of such revolution, challenges, and concerns. A multidisciplinary team of clinicians and researchers provide a balanced discussion of the benefits and challenges of digital health in ophthalmology, oncology, chronic obstructive respiratory diseases, transfusion medicine, stroke, opioid crisis, and the care of elderly. Also, there are chapters addressing the concerns of health inequity, and the risks and security of patient-generated data. This is a timely book not only for clinicians, but also for everyone who is interested in transformation of health care to digital health care.

Contents

• Foreword
• Preface
• List Of Contributors
• 1. Electronic Patient-Generated Health Data for Healthcare
  Maurice Mars and Richard E Scott.
  • INTRODUCTION
  • PATIENT-GENERATED HEALTH DATA AND ASSOCIATED NOMENCLATURE
  • A PROPOSED PGHD PROCESS MODEL
  • DETAILED DESCRIPTION OF THE ‘PGHD MODEL’
  • BENEFITS OF PGHD
  • CHALLENGES OF PGHD
  • PGHD: LEGAL, REGULATORY, AND ETHICAL ISSUES
  • FUTURE
  • CONCLUSION
  • REFERENCES
• 2. Development and Experience with Cancer Risk Prediction Models Using Federated Databases and Electronic Health Records
  Limor Appelbaum, Irving D. Kaplan, Matvey B. Palchuk, Steven Kundrot, Jessamine P. Winer-Jones, and Martin Rinard.
  • INTRODUCTION
  • THE GOAL OF DEVELOPING CANCER RISK PREDICTION MODELS
  • THE RATIONALE FOR USING REAL-WORLD DATA TO BUILD MODELS
  • MAXIMIZING THE POTENTIAL OF EHR DATA FOR MODEL DEVELOPMENT
  • OUR INITIAL EXPERIENCE WITH LOCAL HOSPITAL DATA
  • OVERCOMING THE LIMITATIONS OF SINGLE-INSTITUTION DATA USING A FEDERATED NETWORK DATABASE
  • OPPORTUNITIES FOR REAL-TIME MODEL DEPLOYMENT AND ASSESSMENT
  • CONCLUSION
  • REFERENCES
• 3. Digital Health for the Opioid Crisis: A Historical Analysis of NIH Funding from 2013 to 2017
  En-Ju D. Lin, Madeleine Schroeder, Yungui Huang, and Simon Lin Linwood.
  • INTRODUCTION
  • TECHNOLOGY INNOVATIONS TO ADDRESS OPIOID CRISIS
  • TECHNOLOGY FUNDING TRENDS
  • CONCLUSION
  • REFERENCES
• 4. Telehealth in Ophthalmology
  Jenay Yuen, Sarah Pike, Steve Khachikyan, and Sudha Nallasamy.
  • INTRODUCTION
  • HISTORY OF TELEOPHTHALMOLOGY
  • DIABETIC RETINOPATHY
  • AGE-RELATED MACULAR DEGENERATION
  • GLAUCOMA
  • ANTERIOR SEGMENT AND EXTERNAL DISEASES
  • NEURO-OPHTHALMOLOGY
  • PEDIATRIC OPHTHALMOLOGY
  • CONCLUSION
  • REFERENCES
• 5. Applying Information and Communication Technology to Promote Healthy Aging in Older People: Japan’s Challenges and Perspective
  Aiko Osawa, Shinichiro Maeshima, and Hidenori Arai.
  • INTRODUCTION
  • WHAT IS DIGITAL HEALTH?
  • DIGTAL HEALTH IN JAPAN
  • DIGITAL ASSISTIVE DEVICES FOR HEALTH AND LONGEVITY
  • THE CHALLENGE OF COVID-19 DISASTERS IN AN AGING SOCIETY
  • DEVELOPMENT OF THE HOME EXERCISE PROGRAM
  • ONLINE SALONE APP
  • TELEREHABILITATION
  • CONCLUSION
  • REFERENCES
• 6. Moving Toward Explainable Decisions of Artificial Intelligence Models for the Prediction of Functional Outcomes of Ischemic Stroke Patients
  Esra Zihni, Bryony L. McGarry, and John D. Kelleher.
  • INTRODUCTION
  • THE BENEFITS OF MEDICAL-AI FOR ISCHEMIC STROKE
  • ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING
  • ARTIFICIAL INTELLIGENCE-BASED MODELS FOR FUNCTIONAL STROKE OUTCOME PREDICTION
  • EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI)
  • THE NEED FOR XAI FOR CDSS USED IN ACUTE STROKE CARE
  • CONCLUSION
  • REFERENCES
• 7. GER-e-TEC Study: An Innovative Geriatric Risk Remote Monitoring Project
  Abrar-Ahmad Zulfiqar, Mohamed Hajjam, Amir Hajjam, and Emmanuel Andres.
  • INTRODUCTION
  • TELEMEDICINE APPLICATIONS IN NURSING HOMES IN FRANCE
  • THE GER-E-TEC PROJECT
  • CONCLUSION
  • REFERENCES
• 8. Transfusion Medicine: From AB0 to AI (Artificial Intelligence)
  Cees Th. Smit Sibinga.
  • INTRODUCTION
  • HISTORICAL MILESTONES
  • WHAT HAPPENED NEXT?
  • THE IMPACT OF WORLD WAR I AND II
  • ACCELERATING DEVELOPMENTS
  • INFECTIOUS DISEASE OUTBREAKS AND THEIR IMPACT
  • BLOOD SUPPLY AND CONSUMPTION: HOW ABOUT A DIGITAL FOOTPRINT?
  • BLOOD TRANSFUSION CHAIN
  • THE WAKE OF TRANSFUSION MEDICINE
  • A VEIN-TO-VEIN DIGITAL FOOTPRINT
  • IMPLEMENTATION: FACTS, CHALLENGES, AND FICTION
  • CONCLUSION
  • REFERENCES
• 9. Digital Health Equity
  Katharine Lawrence.
  • INTRODUCTION
  • HEALTH (IN)EQUITY IN THE DIGITAL AGE
  • APPROACHING AND ADVANCING DIGITAL HEALTH EQUITY
  • CONCLUSION
  • REFERENCES
• 10. Telemedicine in the Management of Chronic Obstructive Respiratory Diseases: An Overview
  Miguel T. Barbosa, Cláudia S. Sousa, and Mário Morais-Almeida.
  • INTRODUCTION
  • TELEMEDICINE IN THE MANAGEMENT OF COPD
  • TELEMEDICINE IN THE MANAGEMENT OF ASTHMA
  • MOBILE APPLICATIONS AND WEBSITES
  • CONCLUSION
  • REFERENCES

Professor Simon Lin Linwood, MD, MBA, is the Chief Medical Information Officer at the UCR Health and UCR School of Medicine, Riverside, CA, USA. He obtained his executive education in Artificial Intelligence Strategy and Implementation from Harvard University. He was named Innovator of the Year by CHIME in 2018. He is a national thought leader on digital transformation and acknowledged for providing vision and leadership in information technology to drive business growth in large, complex, and highly distributed environments. He has supervised and mentored many PhD students on data-driven innovation in healthcare, such as deep learning for risk prediction in population health to control costs, and methods to reduce physician documentation burden. He received Mentor of the Year Award in 2016. His areas of expertise include digital transformation, data-driven innovation, data science, data lake, artificial intelligence, and natural language processing, among others. He has extensively published on digital health, artificial intelligence/machine learning, data science, clinical informatics, genome informatics.

Digital Health

ISBN: 978-0-6453320-1-8

DOI: https://doi.org/10.36255/exon-publications-digital-health

Edited by

Simon Lin Linwood, MD, MBA, School of Medicine, University of California, Riverside, CA, USA

Published by

Exon Publications, Brisbane, Australia

Copyright© 2022 Exon Publications

Copyright of individual chapters belongs to the respective authors. The authors grant unrestricted publishing and distribution rights to the publisher. The electronic versions of the chapters are published under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). https://creativecommons.org/licenses/by-nc/4.0/. Users are allowed to share and adapt the chapters for any non-commercial purposes as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source. The book in its entirety is subject to copyright by the publisher. The reproduction, modification, replication and display of the book in its entirety, in any form, by anyone, for commercial purposes are strictly prohibited without the written consent of the publisher.

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First Published in April 2022

Printed in Australia