Leveraging AI-Enabled Learning Health Systems to Advance Value-Based Health Care: A Conceptual Framework.

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.

• Publication year

  2026

• Venue

  Health Care Management Review

• Publication date

  2026-02-09

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1097/HMR.0000000000000472PMID 41661176
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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