The Thinking Healthcare System
Artificial Intelligence and Human Equity
- 1st Edition - February 17, 2023
- Imprint: Academic Press
- Author: Dominique J Monlezun
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 9 0 6 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 9 0 7 - 4
The Thinking Healthcare System: Artificial Intelligence and Human Equity is the first comprehensive book detailing the historical, global, and technical trends shaping the evolu… Read more
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Request a sales quoteThe 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.
- Details the first comprehensive, global, and multidisciplinary analysis of the AI-driven transformation of modern healthcare systems into their definitive digitalized form that will dominate the future
- Provides clear descriptions and concrete examples of AI-informed value-based healthcare, digital health ecosystems, data architecture, cloud and edge computing, precision medicine, public health, telemedicine, patient safety, health political economics, multicultural and embedded global ethics, blockchain, AI security, health security, digital twins, and quantum health computing
- Serves as a practical blueprint, roadmap, and system DNA for creating the future’s healthcare system that integrates efficiency and equity to accelerate the treatment (and in some cases even cures) for some of our world’s most urgent, immediate, and impending global health challenges and crises
Researchers, students, practitioners, and policy makers in the fields of bioinformatics, health policy, public health, and economics
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Chapter 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
- 1.9. Emerging solutions: digital, personalized, globalized, fair
- 1.10. AI: from survival to sustainable healthcare systems
- Chapter 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
- 2.5. Healthcare AI's R&D process
- 2.6. Healthcare AI governance and 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
- Chapter 3. AI+precision medicine: data science and multiomics
- 3.1. Precision versus personalized medicine
- 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
- 3.5. Value-based system approach to precision medicine: open practically, closed conceptually
- 3.6. AI-enabled omics in precision medicine: 60% social + 30% genes + 10% medical=health determinants
- 3.7. AI-enabled data science+multiomics=Personalized medicine's future
- Chapter 4. AI+public health: effective and fair collaboration
- 4.1. History, concepts, and terms
- 4.2. “Global health” reformulation and anticolonial resistance
- 4.3. The great COVID reset
- 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
- Chapter 5. AI+telehealth: plugging into the digital ecosystem
- 5.1. Telehealth overview
- 5.2. Post-COVID surging usage+investments
- 5.3. Digital ecosystem: disparities and developments in health infrastructure and regulations
- 5.4. Bridging telehealth's digital divide with edge computing
- 5.5. Bridging telehealth's digital divide with AI-enabled collective intelligent network connectivity
- 5.6. AI-enabled telehealth uses cases expanding healthcare systems' borders
- Chapter 6. AI+patient safety: adaptive, embedded, intelligent
- 6.1. Patient safety: debates and definitions
- 6.2. Patient safety: development versus defeat
- 6.3. Human-centered, standardized, AI-enabled patient safety
- 6.4. AI-enabled patient safety use cases: drug safety, clinical reports, and alarms
- 6.5. Automating AI-enabled patient safety: embedded, ambient, command center, and blockchain intelligence
- 6.6. AI challenges to patient safety: bias, reproducibility, explainability, effectiveness, and design solutions
- Chapter 7. AI+political economics in healthcare: globalized, digitalized, divided
- 7.1. Evolutionary biology, digitalization, and globalization of healthcare political economics
- 7.2. Overview of macro (ideological) and micro (financial) political economics pressuring modern healthcare systems redesign
- 7.3. Sustainable political economic design of AI-enabled healthcare
- 7.4. International political economic models of AI-enabled healthcare: nationalized, privatized, and globalized
- 7.5. Local political economic models of AI-enabled healthcare: Big Tech+Big Insurance=Big Medicine?
- 7.6. “Resilient integration”: comprehensive end-to-end structures
- Chapter 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
- 8.4. International moral interoperability: superficial vague principles to substantive pluralistic cooperation
- 8.5. Structural design re-engineering of moral interoperability in ethical healthcare AI for a divided and digitalized world
- 8.6. Applied healthcare AI ethics: AiCE+Personalist Social Contract
- Chapter 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
- 9.3. Fleshing out the details: the future's AI-enabled health ecosystem
- 9.4. Health ecosystem: practical emerging cases
- 9.5. The future's (AI) health ecosystem DNA: H=AE2
- Index
- Edition: 1
- Published: February 17, 2023
- No. of pages (Paperback): 322
- No. of pages (eBook): 322
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443189067
- eBook ISBN: 9780443189074
DM
Dominique J Monlezun
Dominique J Monlezun MD, PhD, PhD, MPH is a practicing physician-data scientist and AI ethicist. He serves as a Mayo Clinic physician, Professor of Cardiology for two American academic medical institutions, Professor of Bioethics for two United Nations-affiliated universities, and the Principal Investigator and Senior Data Scientist and Biostatistician for over 100 research studies associated with Harvard University, the National Institutes of Health, and dozens of low- and middle-income countries.
Affiliations and expertise
Mayo Clinic, Rochester, MN, USARead The Thinking Healthcare System on ScienceDirect