Health care 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 digital 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.
