Table Of ContentAI-First
Healthcare
AI Applications in the Business and Clinical
Management of Health
Kerrie L. Holley &
Siupo Becker, M.D.
AI-First Healthcare
AI Applications in the Business and
Clinical Management of Health
Kerrie L. Holley and Siupo Becker, M.D.
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AI-First Healthcare
by Kerrie L. Holley and Siupo Becker, M.D.
Copyright © 2021 Kerrie L. Holley and Siupo Becker. All rights reserved.
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978-1-492-06315-5
[LSI]
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
1. Myths and Realities of AI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
AI Origins and Definition 2
AI and Machine Learning 4
AI Transitions 11
AI—A General Purpose Technology 15
AI Healthcare Myths 17
Myth: AI Will Cure Disease 21
Myth: AI Will Replace Doctors 25
Myth: AI Will Fix the “Healthcare Problem” 27
Myth: AI Will Decrease Healthcare Costs 29
AI Myths 33
Myth: AI Is an Existential Threat 34
Myth: AI Is Just Machine Learning 35
Myth: AI Overpromises and Underdelivers 36
Myth: True Conversational AI Already Exists 37
Myth: AI as Overlord 38
AI Technology Myths 39
Myth: AI Algorithms Are Biased 40
Myth: AI Sees, Hears, and Thinks 40
Myth: AI Diagnoses Diseases Better Than Doctors 41
Myth: AI Systems Learn from Data 43
Myth: AI Is a Black Box 43
Myth: AI Is Modeled After the Brain 45
AI-First Healthcare 45
iii
2. Human-Centered AI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Toward Human-Centered AI 49
AI Centaur Health 50
Human-Centered AI 53
Intersection of AI and Humans 54
AI and Human Sociocultural Values 58
AI Understanding Humans 61
Humans Understanding AI 64
Human Ethics and AI 66
Human-Centric Approach 67
Making Human-Centered AI Work 69
Summary 71
3. Monitoring + AI = Rx for Personal Health. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Prescription (Rx) for Personal Health 76
Three Realms Influencing Healthcare 78
Ambient Computing and Healthcare 81
Continuous Monitoring Using AI 83
Continuous Monitoring 84
Beeps, Chimes, Dings, and Dongs 85
Health Continuum 86
Application of IoT and AI to Medical Care 88
IoT and AI 88
Health Determinants and Big Data 92
Summary 93
4. Digital Transformation and AI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Digital Transformation of Healthcare 97
Path A: Creating Digital Operations and Processes 99
Path B: Building New Capabilities 100
Path C: Transforming Business Processes 101
Paths to the Digital Transformation of Healthcare 102
Digital Healthcare 102
AI Applied to Digital Healthcare 104
AI, Digitization, and Big Tech 105
Preventive and Chronic Disease Management 106
AI and Prevention 107
AI and Chronic Disease 108
AI and Mental Health 110
AI and Telemedicine 111
Medication Management and AI 113
Medication Adherence 114
iv | Table of Contents
Digital Medication 115
AI and Digitization Applied to Administrative Tasks 117
Summary 119
5. An Uncomfortable Truth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Healthcare Waste 122
Healthcare Spend and AI 123
Treatment Decisions and AI 126
Administrative Costs 131
Administrative Processes and Waste 133
Job Security and AI 135
Clinician Time 136
Ambient Clinical Intelligence 137
AI Use in Diagnostic Imaging and Analysis 137
Summary 142
6. Emerging Applications in Healthcare Using AI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Improving Human Health 146
Improving Human Lives 147
Making Technology Work for Healthcare 148
Ambient Intelligence 148
From a Patient’s Perspective 151
From a Doctor’s Perspective 153
From a Hospital System’s Perspective 156
From an Insurer’s Perspective 160
Emerging Applications and Services 162
Coordination of Care Platform 163
Disease State Management Platform 164
Human to Machine Experience Services 165
Customer Journey Platform 166
Clinician Decision Support Tools 167
Ambient Intelligence Environments 167
Digital Twin Platform 168
Real-Time Healthcare 169
Internet of Behaviors 170
Summary 171
7. AI at Scale for Healthcare Organizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Achieving AI at Scale 173
Transforming Healthcare 178
The Chasm 182
Invisible Engines—Healthcare Platforms 183
Table of Contents | v
The Road to a Healthcare Platform 186
Ecosystems 190
Application Programming Interfaces (APIs) 191
Summary 191
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
vi | Table of Contents
Preface
The number of books describing artificial intelligence (AI), machine learning, deep
learning, natural language processing, and the full constellation of AI technologies
could fill a library. Coupled with the ever-growing list of articles, videos, and blogs,
there is no lack of content. Clinicians, computer scientists, technologists, physicians,
philosophers, and journalists each tackle different AI issues and challenges.
However, we couldn’t find a book that discussed AI from a medical doctor and a tech‐
nologist’s paired perspectives. This is a book that tracks the journey of a physician
and a technologist working together, discussing AI’s opportunity while explaining AI
for the consumption of a clinician, an IT worker, a user, an executive, or a business
stakeholder. Our goal is for you to understand the possibilities for improvements in
healthcare supercharged by artificial intelligence.
While discussing this vast potential, we strove to maintain the awe of AI while
grounding the reader in the reality of AI today. We hope that Chapter 1 will provide
you with confidence that you understand the myth versus the reality. More impor‐
tantly, this first chapter seeks to familiarize you with the language of AI—what is a
model, an algorithm, a neural network, and more—without your needing to brush up
on linear algebra or computer science.
Chapter 2 integrates human-centered design, with providers and technologists work‐
ing together to build smart systems to ensure that AI is used to do good. Chapter 3
helps the reader see how combining AI with sensing and monitoring, given the
growth of intelligent objects, affords unparalleled opportunity for accelerating per‐
sonalized medicine. Chapter 4 describes digital transformation and AI, and the utility
of AI for digital transformation.
Several opportunities exist for using AI to reduce the amount of waste in healthcare
and to reduce medical errors, as addressed in Chapter 5. Chapter 6 describes several
AI solutions that, when realized, materially affect the quadruple aim of healthcare.
Last, Chapter 7 provides a road map for how organizations realize AI’s benefits not
just for a single instance but at scale.
vii
The state of healthcare is top of mind because it touches most aspects of our lives.
The COVID-19 pandemic exposes the slow train wreck of dumb systems frustrating
doctors, provider systems, and patients. There is no “silver bullet,” no quick fix, no
one-size-fits-all solution, but the potential to transform healthcare with AI is now
possible. We hope this book provides a blueprint for organizations in their journey to
leverage AI to make healthcare better for everyone.
Conventions Used in This Book
The following typographical conventions are used in this book:
Italic
Indicates new terms, URLs, email addresses, filenames, and file extensions.
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viii | Preface
