ArticleMethods of information in medicine2026
The Roles of Biomedical Informatics for an Equitable Learning Health System: Lessons from a Year of Scientific Informatics Literature.
Article in Methods of information in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
8 authors.
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Abstract
Background: Biomedical informatics plays a central role in supporting learning health systems (LHSs) that aim to continuously improve care by transforming data into knowledge and knowledge into action. However, health care data reflect existing disparities in access, delivery, and outcomes, raising concerns about whether the LHS cycle can improve care equitably without intentional design. Objective: This study aims to describe the recurring theme of health equity found in recent biomedical informatics literature, using a convenience sample of articles contributed to the 2025 American Medical Informatics Association (AMIA) Year in Review (YIR). Methods: In developing the 2025 AMIA YIR presentation, we found that 240 articles submitted by AMIA experts could be organized into multiple themes to allow summarization and synthesis for efficient presentation of 140 of them. Many themes could themselves be organized around the concept of the LHS, which further streamlined the presentation. We were struck by how frequently biomedical informatics literature addressed health equity in relation to LHSs. We therefore applied our own expertise to distill relevant articles from the larger set to explore this recurrent issue. Results: Here, we present 56 papers selected from 9 of the 19 themes that illustrate how biomedical informatics can support equity at each stage of the LHS. Mature LHS implementations provided large-scale examples of how learning cycles can be designed for equity. Real-World Evidence and Inclusive Data themes showed that equitable learning depends on whether data are complete enough to represent the populations the system intends to serve. Natural language processing and artificial intelligence (AI) modeling showed how data are transformed into knowledge and translated into clinical action through decision support. Trust and justice in data use, health equity as infrastructure, and equitable access emerged as interdependent conditions that influence whether the learning cycle operates equitably and effectively. Conclusion: Equity emerged as a condition influencing every stage of the LHS cycle. The value of informatics interventions cannot be judged by technical performance alone, but by whether they build learning cycles that are inclusive, accountable, and equitable for the populations they serve.
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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.