Healthcare That Learns: Value-Based Care and the AI-Enabled Learning Health System

Healthcare has more evidence, data, and artificial intelligence (AI) than ever. A risk model can flag a patient, a dashboard can reveal a gap, and a guideline can recommend the next step. But if no one owns the response, if the workflow cannot support action, or if the patient’s reality is not part of the loop, better information does not become better care.Healthcare That Learns: Value-Based Care and the AI-Enabled Learning Health System argues that the central challenge is not discovery, technology, or payment reform. It is conversion: turning practice into data, data into knowledge, knowledge into standards, standards into routines, and routines back into better practice.Written for health system executives, clinical leaders, informaticians, quality and safety professionals, policymakers, clinicians, and trainees, this book offers a practical operating model for the AI-enabled learning health system. It shows how value-based care, AI, informatics, operations, governance, equity, implementation, and workforce readiness must work together to close the know–do gap and make better care routine.Through real-world cases and a clear learning architecture, the book explains why AI creates value only when connected to trustworthy data, legitimate standards, owned workflows, response capacity, monitoring, and patient partnership. It also shows how organizations can reduce avoidable variation, govern AI responsibly, design for bioequity, and measure return on learning rather than isolated project gains.At a moment when health systems are investing heavily in dgtl transformation while struggling to improve outcomes at sustainable cost, Healthcare That Learns offers a road map for organizations that must learn continuously, act responsibly, and improve care that matters.

About the Author

Dong-Gil Ko, PhD , is Professor of Operations, Business Analytics, & Information Systems and Founder and Director of the HealthAI Research Lab at the University of Cincinnati’s Carl H. Lindner College of Business. His work sits at the intersection of organizational learning, knowledge management, information systems, artificial intelligence, health informatics, and healthcare operations, with a particular focus on how organizations convert data, evidence, and experience into better decisions, workflows, and outcomes.

Before entering academia, Dr. Ko worked in consulting, information technology, and systems development and implementation, experiences that shaped his longstanding interest in how technology creates value only when it is successfully integrated into organizational processes and human work. His subsequent academic career has spanned information systems, operations, analytics, and healthcare, with research examining knowledge transfer, organizational learning, decision support, dgtl transformation, patient-centered care, telehealth, and AI-enabled healthcare innovation.

As Founder and Director of the HealthAI Research Lab, Dr. Ko leads convergence research initiatives that bring together business, medicine, informatics, and data science to address complex healthcare challenges. His work has involved collaborations with UC Health, the UC Gardner Neuroscience Institute, Cincinnati Children’s Hospital Medical Center, Eli Lilly, and Holmusk, among others. These efforts have focused on learning health systems, clinical decision support, population health, telehealth, risk stratification, and the responsible deployment of artificial intelligence in healthcare settings. He has served as an Epic Affiliate Consultant and contributed to the development of AI-enabled healthcare innovations through translational research, learning health system initiatives, and patent-pending technologies.

Dr. Ko’s scholarship has appeared in leading journals spanning information systems, management, operations, and health informatics, including Management Science , MIS Quarterly , Information Systems Research , Organization Science , the Journal of Operations Management , Health Care Management Review , the Journal of the American Medical Informatics Association , and the Journal of Medical Internet Research . Complementing this publication record, he has served in editorial leadership roles across both information systems and health informatics, contributing to journals such as Information Systems Research , Information & Management , and the Journal of Medical Internet Research . He has also served as Lead Guest Editor for the Journal of Medical Internet Research for a special theme issue examining the strategic implications of AI-enabled uncertainty in healthcare.

His interest in learning health systems reflects a broader belief that healthcare’s greatest challenge is no longer simply generating knowledge, but converting knowledge into routine practice. His research seeks to bridge the gap between discovery and implementation by examining how data, technology, governance, workflows, patients, clinicians, and organizational capabilities can be integrated into systems that continuously learn, adapt, and improve. Through this work, he has become increasingly focused on how artificial intelligence can accelerate organizational learning while preserving trust, accountability, equity, and patient-centered care.

Umberto Tachinardi, MD, MS, FACMI, FIAHSI, FAMIA, is Associate Dean and Chair of the Department of Biostatistics, Health Informatics and Data Sciences (BHIDS) at the University of Cincinnati College of Medicine. A physician-informatician with leadership experience spanning academic medicine, health system operations, translational research, and public-sector healthcare transformation, he has worked extensively at the intersection of clinical care, dgtl transformation, artificial intelligence, and learning health systems.

Before moving to the United States, Dr. Tachinardi worked at the Heart Institute (InCor) in São Paulo, Brazil, where he helped develop electronic health systems and early clinical information infrastructures to support complex cardiovascular care, research, and hospital operations. He also worked with the State of São Paulo Secretary of Health on initiatives designed to improve coordination, information flow, and operational effectiveness across one of the world’s largest public healthcare systems. Those early experiences shaped his long-standing interest in how information systems, governance, workflow redesign, and organizational learning can improve care at scale.

Dr. Tachinardi’s subsequent career in the United States has included leadership roles at the Regenstrief Institute, the University of Wisconsin–Madison, and the University of Chicago, where he led and supported initiatives in clinical informatics, translational research infrastructure, analytics, interoperability, and dgtl health strategy. His work today spans clinical informatics, AI-enabled operational redesign, data governance, learning health systems, and workforce development for dgtlly enabled healthcare organizations.

An elected fellow of the American College of Medical Informatics, the American Medical Informatics Association, and the International Academy of Health Sciences Informatics, Dr. Tachinardi is particularly interested in how health systems can build trustworthy socio-technical architectures that continuously learn from care, research, and operations to improve quality, equity, and value.

Brett M. Kissela, MD, MS, is a neurologist, clinician-scientist, and academic medicine leader whose career has focused on improving brain health through clinical care, population-based research, health system leadership, and the translation of evidence into practice. He serves as the Julia A. Uihlein Dean of the School of Medicine and Executive Vice President for Health Affairs at the Medical College of Wisconsin. He previously served as the Executive Vice Dean and Senior Associate Dean of Clinical Research at the University of Cincinnati College of Medicine, as well as Director of the UC Gardner Neuroscience Institute. His work spans academic strategy, clinical and translational research, faculty development, health system collaboration, and the advancement of learning health system approaches across the missions of care, research, education, and community impact.

Dr. Kissela’s scholarly work has centered on stroke epidemiology, disparities in cerebrovascular disease, recovery after stroke, and the broader determinants of brain health across populations. Through leadership roles in neurology, clinical research, and academic medicine, he has worked to connect rigorous population science with practical improvements in care delivery – particularly in areas where health systems can learn from patients, communities, clinical data, and outcomes over time.

His interest in learning health systems and artificial intelligence reflects a long-standing commitment to building healthcare organizations that can continuously improve: integrating data, clinical expertise, patient-centered outcomes, and responsible innovation to enhance quality, equity, safety, and value. He is especially interested in how AI can support clinicians, accelerate discovery, strengthen implementation, and help academic health systems become more responsive, trustworthy, and humane.

Across his career, Dr. Kissela has sought to bridge the worlds of bedside care, research, education, and institutional leadership. His work reflects the belief that the future of medicine will depend not only on better technologies but also on better systems – systems that learn, adapt, and remain anchored in the needs of patients, families, clinicians, learners, and communities.