Learning research systems: A conceptual framework and formative implementation case

Learning Health Systems (LHSs) enable data-driven improvement in clinical care. However, research processes that generate this evidence remain fragmented, limiting systematic learning across study portfolios and contributing to persistent translational inefficiencies. We introduce the Learning Research System (LRS) as a sociotechnical extension of the LHS that leverages LHS infrastructure to render research operations measurable, reusable, and governable across studies, positioning research as an object of continuous learning. Using targeted literature synthesis and supplementary searches, we traced the emergence of the LRS from foundational LHS scholarship and developed five core capabilities as proposed maturity indicators: cross-study measurement, reuse of cohorts and analytic assets, portfolio-level governance, feedback-enabled workflow optimization, and integration of research outputs into clinical and operational improvement. We illustrate the distinction between LHS and LRS roles across the NCATS Translational Science Spectrum. Finally, we applied the capabilities to a formative case at the University of Nebraska Medical Center through an institutional environmental scan, mapping them as indicators of emerging LRS maturity from an existing LHS. The LRS provides a framework for evaluating research-process learning, reducing fragmentation across translational stages, and addressing selected translational discontinuities, such as early-stage "valley of death" barriers between discovery and human studies.