2025/11/12 by Laura K. Wiley, Laura K Wiley, Luke V. Rasmussen +20
Computer Science · Health Professions · Medicine · #Artificial Intelligence in Healthcare and Education #Electronic Health Records Systems #Machine Learning in Healthcare
paper · pdf · doi:10.1093/jamia/ocaf195
openalex created_date 2025/11/12 · openalex publication_date 2025/11/12 · openalex updated_date 2026/08/01
BACKGROUND: Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. OBJECTIVE: To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse. FITNESS FOR PURPOSE: Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application. FITNESS FOR REUSE: Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable-that is, it identifies the same intended population with similar performance characteristics when applied to a new setting. CONCLUSIONS: The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.