Learning health systems for cardiovascular health and health care

Abstract Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, yet the translation of established prevention and treatment strategies into routine clinical practice remains inconsistent. This review examines the application of Learning Health System (LHS) principles to cardiovascular care, synthesizing contemporary evidence on artificial intelligence (AI) and machine learning (ML), clinical decision support (CDS), wearable and remote patient monitoring, interoperable data infrastructures and common data models, patient-generated health data (PGHD), patient-reported outcomes (PROs), implementation science, community engagement, and the governance of AI-enabled technologies. Cardiovascular health and care represent promising but complex contexts for LHS implementation, characterized by time-sensitive acute events, longitudinal disease management, guideline-directed therapies, increasingly extensive clinical and physiological data, costly interventions, well-established registries, and persistent disparities in access, quality, and outcomes. Opportunities exist to strengthen evidence generation, clinical decision-making, and continuous quality improvement while addressing major implementation challenges, including fragmented multipayer systems, misaligned incentives, substantial upfront infrastructure requirements, heterogeneous data quality, algorithmic bias and model drift, clinician workload, regulatory and liability uncertainties, and inequitable access to digital technologies. Case studies from the Veterans Health Administration, PCORnet and PCORI initiatives, Kaiser Permanente, and the American Heart Association’s Get With The Guidelines program demonstrate the feasibility of key LHS functions while also underscoring contextual factors that may limit their generalizability and scalability. Cardiovascular LHSs have the potential to accelerate the translation of evidence into practice, but their effectiveness depends on the integration of robust technical infrastructure with effective governance, sustainable financing, systematic measurement of health equity, continuous monitoring of algorithmic performance, pragmatic implementation strategies, and rigorous evaluation of patient-centered outcomes across diverse and fragmented care environments.