Clinical and translational science award hubs in learning health systems: development of the engine-drivetrain model.

backgroundOver the past two decades, biomedical research has made extraordinary progress, at amazing speeds and with astounding computational capabilities. Even in very agile organizations, these fast-paced advances represent significant barriers to nimble and successful implementation, scaling, integration, cultural shaping, and ongoing education. Learning at individual, team, organization or system levels is paramount to the good functioning of all health systems. MAIN BODY: Academic health institutions are uniquely positioned to drive innovation, improve patient outcomes, and cultivate future leaders by integrating well-managed clinical operations, breakthrough research, and outstanding education. The Learning Health System (LHS) framework provides a powerful model of integration, leveraging iterative cycles of discovery, learning and improvement by employing robust operational management, analytical capabilities, implementation and dissemination capacity and functionalities. Central to this model are three interconnected phases: Practice to Data, Data to Knowledge, and Knowledge to Practice, which form the ‘implementation arc’ or the LHS cycle. At each junction between these phases, the incorporation of external evidence and the active engagement of community members or stakeholders serve as critical pillars, ensuring that learning is both rigorous and relevant.

conclusionsFirst, we offer a modified design and conceptualization of LHS as a four-cycle, mission-based model centered by its community, and strengthened by external evidence and data as distinct inputs, at multiple points of the cycle. Second, we propose that the Clinical and Translational Science Award (CTSA) hubs’ structures and functions can represent the strong operational engine of the LHS plant, while community represents the central axle and transmission of movement in the central mechanism of the LHS. As such, the CTSA hubs can serve as a robust machinery for the clinical, research, and educational cycles in learning health systems. RANDOMIZED CONTROL TRIAL NUMBER: Not applicable.