The Thinking Healthcare System

Description:

The Thinking Healthcare System: Artificial Intelligence and Human Equity is the first comprehensive book detailing the historical, global, and technical trends shaping the evolution of the modern healthcare system into its final form―an AI-driven thinking healthcare system, structured and functioning as a global digital health ecosystem. Written by the world’s first triple doctorate trained physician-data scientist and ethicist, and author of three AI textbooks and over 350 scientific and ethics papers, this indispensable resource makes sense of how technology, economics, and ethics are already producing the future’s health system―and how to ensure it works for every patient, community, and culture in our globalized, digitalized, and divided world.

Providing clear descriptions and concrete examples, this book brings together AI-accelerated digital health ecosystems, data architecture, cloud and edge computing, precision medicine, public health, telemedicine, patient safety, health political economics, multicultural global ethics, blockchain, and quantum health computing, among other topics. Healthcare and business executives, clinicians, researchers, government leaders, policymakers, and students in the fields of healthcare management, data science, medicine, public health, informatics, health and public policy, political economics, and bioethics will find this book to be a groundbreaking resource on how to create, nourish, and lead AI-driven health systems for the future that can think, adapt, and so care in a manner worthy of the world’s patients.

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Table of contents :

Front Cover
The Thinking Healthcare System
The Thinking Healthcare System: Artificial Intelligence and Human Equity
Copyright
Contents
1 – Healthcare systems: challenges, crises, and cures
1.1 Background, purpose, and structure: why is this worth your time?
1.2 Rationale: avoiding the graft rejection and omitted variable traps
1.3 The book’s defining value-add+audience
1.4 Foundational definitions and concepts
1.5 Historical development
1.6 Value-based healthcare systems: health’s future?
1.7 Politics, economics, and regulation
1.8 Present problems: poor quality, safety, prevention, and cost
1.8.1 Present problems: poor quality
1.8.2 Present problems: poor safety
1.8.3 Present problems: poor prevention
1.8.4 Present problems: poor cost
1.9 Emerging solutions: digital, personalized, globalized, fair
1.9.1 Emerging solutions: digital
1.9.2 Emerging solutions: personalized
1.9.3 Emerging solutions: globalized
1.9.4 Emerging solutions: fair
1.10 AI: from survival to sustainable healthcare systems
References
2 – AI+healthcare systems: efficiency and equity
2.1 Objectives and scope
2.2 AI overview
2.3 Healthcare AI overview
2.4 Digital transformation of healthcare: healthcare AI’s data infrastructure and system integration
2.4.1 Structure of the modern healthcare system
2.4.2 Digitalization of healthcare: from the digital revolution to AI data infrastructure
2.5 Healthcare AI’s R&D process
2.5.1 AI R&D core areas
2.5.2 AI R&D pipeline design: trustworthy, ethical, and effective AI
2.6 Healthcare AI governance and workflow
2.6.1 Healthcare AI governance
2.6.2 Healthcare AI workflow
2.7 Healthcare AI in system design and operation
2.8 Front runner for the future’s AI-driven healthcare system
2.9 The genome of the AI-powered future healthcare system
References
3 – AI+precision medicine: data science and multiomics
3.1 Precision versus personalized medicine
3.1.1 Conceptual distinction
3.1.2 Genomic matching with umbrella, basket, and N-of-1 trials
3.2 Precision medicine’s historical development
3.3 Precision medicine versus public health: fighting for healthcare’s future
3.4 AI+healthcare Big Data=precision medicine: Big Data, chaos theory, and AI overfitting
3.4.1 Healthcare Big Data: clinical and organizational structures
3.4.2 Healthcare AI analytics: model fit conceptual overview
3.4.3 Healthcare AI analytics: AI-HealthSIPs and model-informed decisions
3.4.4 AI-HealthBD: impact on future healthcare system design
3.5 Value-based system approach to precision medicine: open practically, closed conceptually
3.5.1 Open healthcare system model in AI-HealthBD
3.5.2 Early scaling of PrMed value use case of AI-HealthBD
3.5.3 AI-HealthBD data “oceans,” value barriers, and countermeasures
3.6 AI-enabled omics in precision medicine: 60% social + 30% genes + 10% medical=health determinants
3.6.1 Mutiomics barriers and breakthroughs
3.6.2 Translational multiomics, pharmacogenetics, and radiogenomics use cases
3.7 AI-enabled data science+multiomics=Personalized medicine’s future
References
4 – AI+public health: effective and fair collaboration
4.1 History, concepts, and terms
4.1.1 Recap
4.1.2 Quarantines to vaccines
4.2 “Global health” reformulation and anticolonial resistance
4.2.1 PubHealth’s conceptual reformulation as “global health”
4.2.2 Anticolonial critique of global health
4.3 The great COVID reset
4.3.1 From COVID-19 stress test for PubHealth to AI pressure for PubHealth redesign
4.3.2 Post-COVID ethical AI reset for PubHealth
4.4 Emerging trends framing ethical AI-enabled PubHealth
4.5 AI-enabled PubHealth piloted applications
4.6 AI health as healthcare system-based AI-PubHealth: sovereignty, solidarity, success
4.6.1 AI health conceptual update
4.6.2 AI health: PubHealth’s contribution to sovereignty, solidarity, and survival
References
5 – AI+telehealth: plugging into the digital ecosystem
5.1 Telehealth overview
5.1.1 Conceptual framework
5.1.2 Telehealth=eHealth+telemedicine
5.2 Post-COVID surging usage+investments
5.3 Digital ecosystem: disparities and developments in health infrastructure and regulations
5.3.1 Global digital ecosystem
5.3.2 Digital health ecosystem
5.3.3 Digital health infrastructure+telehealth blockchain
5.3.4 Regulations
5.4 Bridging telehealth’s digital divide with edge computing
5.4.1 The digital divide’s threat to disparities and telehealth
5.4.2 Cloud-fog-edge computing in IT architecture
5.4.3 Edge telehealth reducing disparities
5.5 Bridging telehealth’s digital divide with AI-enabled collective intelligent network connectivity
5.5.1 Connectivity disparities
5.5.2 Nonterrestrial networks
5.5.3 AI-enabled connectivity
5.6 AI-enabled telehealth uses cases expanding healthcare systems’ borders
5.6.1 Value-based strategic expansion
5.6.2 System design with AI-enabled and geospatial informed telehealth
5.6.3 Telehealth implications for patient safety
References
6 – AI+patient safety: adaptive, embedded, intelligent
6.1 Patient safety: debates and definitions
6.1.1 Overview of scope and aims
6.1.2 Conceptualizing patient safety
6.1.3 WHO’s patient safety framework
6.2 Patient safety: development versus defeat
6.2.1 Is (equitable) patient safety failing?
6.2.2 AI-enabled patient safety: definitions to development to deliverables?
6.3 Human-centered, standardized, AI-enabled patient safety
6.3.1 Patient safety as human-centered design thinking
6.3.2 Standardizing AI-enabled patient safety as system strategy
6.4 AI-enabled patient safety use cases: drug safety, clinical reports, and alarms
6.4.1 AI pivot
6.4.2 AI drug safety
6.4.3 AI clinical reports
6.4.4 AI alarms
6.5 Automating AI-enabled patient safety: embedded, ambient, command center, and blockchain intelligence
6.5.1 Integrating scaled AI safety processes in healthcare systems
6.5.2 Embedded, ambient, and command center safety intelligence
6.5.3 Data security and privacy: blockchain
6.6 AI challenges to patient safety: bias, reproducibility, explainability, effectiveness, and design solutions
6.6.1 Standardizing bias reduction, reproducibility, explainability, and effectiveness
6.6.2 AI design solutions in the safe (future) healthcare system
References
7 – AI+political economics in healthcare: globalized, digitalized, divided
7.1 Evolutionary biology, digitalization, and globalization of healthcare political economics
7.1.1 Evolutionary biology
7.1.2 Industrialization and digitalization
7.1.3 Globalization
7.2 Overview of macro (ideological) and micro (financial) political economics pressuring modern healthcare systems redesign
7.2.1 Macropolitical economic forces pressuring healthcare system redesign
7.2.2 Micropolitical economic forces pressuring healthcare system redesign
7.3 Sustainable political economic design of AI-enabled healthcare
7.3.1 Strategic features: inclusive globalism
7.3.2 Structural features: democratic welfare model and disparities
7.3.3 Adaptive features: affordable AI ROI
7.4 International political economic models of AI-enabled healthcare: nationalized, privatized, and globalized
7.4.1 China: centralized nationalized model
7.4.2 UK, India, the United States: democratic nationalized and privatized models
7.4.3 Friend-shoring of international healthcare systems: globalized model of value blocks
7.5 Local political economic models of AI-enabled healthcare: Big Tech+Big Insurance=Big Medicine?
7.5.1 Big Tech digitalizing health care: “digital colonization” and “open healthcare” as horizontal-vertical integration
7.5.2 Big Insurance: buying all of healthcare
7.5.3 Big problems in healthcare takeovers: graft (corporate) versus host (healthcare) rejection
7.6 “Resilient integration”: comprehensive end-to-end structures
References
8 – AI+health ethics: moral interoperability and pluralism
8.1 No ethics, no AI, no healthcare
8.2 Logical, existential, and societal “suicide?” practical case for AI healthcare ethics
8.3 Postcolonial globalization of AI healthcare ethics
8.3.1 Early global standard setting
8.3.2 WHO codification of global AI ethics standards
8.4 International moral interoperability: superficial vague principles to substantive pluralistic cooperation
8.4.1 Data interoperability to moral interoperability
8.4.2 Healthcare moral interoperability in multidimensional world orders
8.4.3 Resilient end-to-end integration of ethical healthcare AI
8.5 Structural design re-engineering of moral interoperability in ethical healthcare AI for a divided and digitalized world
8.5.1 Theoretical overview of structural redesign
8.5.2 Societal, technical, and political economic contexts
8.6 Applied healthcare AI ethics: AiCE+Personalist Social Contract
8.6.1 AI ethics: technical to global public health
8.6.2 Embedded AI ethics by design versus retrofitting the global data architecture
8.6.3 AiCE+Personalist Social Contract=resilient, global, pluralist, and practical AI ethics by design
8.6.4 Personalist Social Contract: strategy, structure, and content
8.6.5 AiCE+Personalist Social Contract: pluralist application for global healthcare AI ethics
References
9 – The future’s (AI) thinking healthcare system: blueprint, roadmap, and DNA
9.1 Patients: persons, digits, or both
9.2 Emerging blueprints for the future’s health ecosystem: form+function
9.2.1 Structural pillars: data, well-being, and integration
9.2.2 Structural features: AI, ambient, and collaborative
9.3 Fleshing out the details: the future’s AI-enabled health ecosystem
9.3.1 Resilient integration in the health ecosystem DNA: data+well-being=efficiency+equity
9.3.2 Practical key to the future’s AI health ecosystem: complementary ecosystem pairs
9.4 Health ecosystem: practical emerging cases
9.4.1 Global, strategic, and structural: AI-powered health
9.4.2 Local, operational, and functional: maturing enterprise-wide AI-powered health
9.4.3 Emerging transformation trends: dignity-security, strategic empathy, adaptive empowerment networks, embedded clinical trial …
9.5 The future’s (AI) health ecosystem DNA: H=AE2
References
Index