Issue: Despite rapid innovation, health care systems face a persistent 17-year gap between evidence discovery and implementation, undermining efforts to deliver value-based care. Bridging this “know–do gap” is essential to improving outcomes and reducing waste. Existing Learning Health System (LHS) frameworks often lack mechanisms to institutionalize learning at speed and scale. Critical Theoretical Analysis: We propose an AI-enabled LHS framework that leverages artificial intelligence (AI) to connect micro-level clinical learning with macro-level organizational decision-making. Grounded in organizational learning theory, our model illustrates how AI accelerates knowledge capture, conversion, and institutionalization via continuous, bidirectional feedback loops. AI enables real-time learning cycles, linking patient–provider data (“micro”) to system-wide insights and policy adjustments (“macro”), and back to point-of-care decision support. Insight/Advance: Our framework advances the LHS paradigm by adding speed, scale, and micro↔macro integration. Unlike earlier models, it centers AI not as an adjunct but as a foundational learning engine. Case examples from UCHealth and Mass General Brigham show how AI can drive real-time operational learning and institutional memory through structured governance and data infrastructure. Practice Implications: To implement an AI-LHS, organizations should (1) assess readiness and align on value-based goals; (2) invest in data infrastructure and interoperability; (3) cultivate a learning culture by engaging clinicians and staff; (4) embed AI into continuous improvement cycles with interdisciplinary governance; (5) adopt a sociotechnical approach integrating people, processes, and technology; and (6) ensure safeguards for equity, privacy, and security. These steps allow systems to reduce lag between insight and impact, accelerating value-based care transformation.
Ko et al. (2026) studied this question.