Название: The Thinking Healthcare System: Artificial Intelligence and Human Equity
Автор: Dominique J. Monlezun, MD, PhD, PhD, MPH
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 324
Язык: английский
Формат: pdf (true), epub
Размер: 29.9 MB

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.

AI-enabled data science+multiomics=Personalized medicine's future: We have explored in this chapter how personalized medicine's contemporary pillars of Data Science and multiomics are undergoing a fundamental revolution with the larger AI-powered digital revolution in healthcare. AI-accelerated analytics and data storage are rapidly expanding the data infrastructure of healthcare systems by opening them up to the larger global data ecosystem through more streamlined and efficient technical and organizational communication and infrastructure integration. This AI-HealthBD paradigm uniting Data Science (leveraging AI on population-level data to better personalize higher value healthcare for individual patients) and mutiomics (integrating the more detailed molecular data of patients with their social and environmental data) is increasingly demonstrating to healthcare systems how they can have more comprehensive, accurate, and precise understanding of populations and unique patients. And thus systems are seeing the growing successes of this emerging paradigm to improve value-based healthcare delivery not only directly (through improved prevention, diagnosis, prognosis, and treatment) but also indirectly (through AI linking this PrMed to its necessary complement of PubHealth). Thus in this chapter we have considered the emerging healthcare system model for the future: AI+healthcare Big Data (data science+multiomics)=precision medicine. This took us to the organizational synthesis of the proposed model: AI-enabled and integrated precision medicine+public health=healthcare's future. We will now pivot to investigating the PubHealth portion of this formula in the subsequent chapter as we progressively move from the slowly learning healthcare system model of today to the thinking healthcare system model of tomorrow. We will be paying particular attention to the concrete technical applications for AI, barriers to their deployment, and the health inequities worsened and improved by their applications.

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

Contents:
Chapter 1. Healthcare systems: challenges, crises, and cures
Chapter 2. AI+healthcare systems: efficiency and equity
Chapter 3. AI+precision medicine: data science and multiomics
Chapter 4. AI+public health: effective and fair collaboration
Chapter 5. AI+telehealth: plugging into the digital ecosystem
Chapter 6. AI+patient safety: adaptive, embedded, intelligent
Chapter 7. AI+political economics in healthcare: globalized, digitalized, divided
Chapter 8. AI+health ethics: moral interoperability and pluralism
Chapter 9. The future’s (AI) thinking healthcare system: blueprint, roadmap, and DNA
Index






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