ArticleJournal of translational medicine2026
Clinical and Translational Science Award hubs in learning health systems: evaluation framework of the engine-drivetrain model.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Knowledge Traffic in Learning Health Systems: A Conceptual Framework for Organizational Phenotyping and Translational Governance.Learning health systems · 2026Article
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Authors and funding
1 author.
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Abstract
backgroundLearning Health Systems (LHS) aim to accelerate generation, implementation, and dissemination of knowledge to improve population health. Community engagement is widely recognized as an essential component of LHS, yet methods for evaluating community empowerment and its impact on translational performance remain limited. Most assessments focus on participation metrics rather than structural influence on governance, research, and care delivery.
resultsWe proposed a conceptual and quantitative evaluation framework in which the Clinical and Translational Science Award (CTSA) hub serves as the translational science engine of the system, while community empowerment serves as the axle and the variable transmission (drivetrain) regulating the conversion of institutional resources into translational performance. System outputs are expressed through four coupled LHS cycles (clinical care, education, research, and governance), each characterized by translational velocity, innovation throughput, and economic performance. Community empowerment is quantified using the Community Transmission Index (CTI), a structured instrument that evaluates engagement maturity across eight domains including shared governance, participatory data governance, co-production, trust capital, and equity integration. The CTI generates a standardized 0–1 score that reflects the extent to which community partners shape institutional decision-making and system operations. Translational performance across the four LHS cycles can be evaluated using measures such as velocity (inverse latency to sustainment), innovation output, return on investment, and translational efficiency indices. We hypothesized that higher CTI scores will increase translational velocity, improve balance across learning cycles, and enhance innovation uptake by reducing sociotechnical friction and strengthening the legitimacy and sustainability of change processes.
conclusionsThis framework offers a structured approach for evaluating how community empowerment influences translational performance in LHS and how it can be assessed using complementary metrics. By linking engagement measures to operational and economic outcomes, the model enables LHS and academic institutions to assess whether community partnerships function as advisory mechanisms or as integral components of the translational infrastructure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.