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PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse

Laura K Wiley,Luke V. Rasmussen,Rebecca T Levinson,Jennnifer Malinowski,S. Manemann,Melissa P Wilson,Martin Chapman,Jennifer A Pacheco,Theresa L. Walunas,J. Starren,S. Bielinski,Rachel L. Richesson

Published 2025 in JAMIA Journal of the American Medical Informatics Association

ABSTRACT

Abstract 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.

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