Learning Health Systems Resources
Explore frameworks, publications, tools and other resources to support Learning Health System research and practice.
Frameworks and models
Reid Learning Health System Framework
The Reid Learning Health System Framework is like a map. It shows how the parts of a health system, such as data, research and people, connect and work together so care keeps getting better over time.
LHSs employ ‘big data’ from electronic medical records and qualitative sources to develop a comprehensive picture of a patient’s health needs, diagnostics, behaviors, communications, interventions, care processes, costs, and clinical outcomes. By utilizing data analytics, including artificial intelligence and machine learning, LHSs generate actionable insights to address individual and population health needs.
LHSs incorporate the latest evidence and successful strategies to implementation, evaluation, feedback and adaptation to inform rapid-cycle improvements.
LHSs directly engage those impacted by the problem at hand, including patients, caregivers, care providers, community members, healthcare professionals, managers, and health system operators. Co-design activities must consider cross-sectoral approaches and build trusting relationships, paying attention to power differences among participants, especially those from equity-deserving groups. Early engagement, expectation management, and assessing learning needs are crucial, along with removing barriers to engagement.
LHSs employ implementation science methods to systematically design interventions and improve health service effectiveness. This involves understanding current behaviors, identifying strategies for desired behavior change, and specifying implementation details.
LHS approaches rely on realist evaluation methods to measure the effectiveness of multicomponent interventions for specific patient populations under different conditions. Evaluations help identify and eliminate inefficient care processes, creating new capacity.