{"doi":"10.1093/jamia/ocaf195","title":"PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse","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.","journal":"Journal of the American Medical Informatics Association","year":2025,"id":535366,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9599,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":394676,"name":"Luke V. Rasmussen","orcid":"0000-0002-4497-8049","position":1,"is_corresponding":false},{"id":366431,"name":"Rebecca T. Levinson","orcid":"0000-0002-2775-7543","position":2,"is_corresponding":false},{"id":595051,"name":"Jennifer Malinowski","orcid":"0000-0001-7500-2199","position":3,"is_corresponding":false},{"id":272719,"name":"Sheila M. Manemann","orcid":null,"position":4,"is_corresponding":false},{"id":18471,"name":"Melissa P. Wilson","orcid":"0000-0001-9176-3131","position":5,"is_corresponding":false},{"id":1066573,"name":"Martin Chapman","orcid":"0000-0002-5242-9701","position":6,"is_corresponding":false},{"id":251749,"name":"Jennifer A. Pacheco","orcid":"0000-0001-8021-5818","position":7,"is_corresponding":false},{"id":343982,"name":"Theresa L. Walunas","orcid":"0000-0002-7653-3650","position":8,"is_corresponding":false},{"id":488216,"name":"Justin Starren","orcid":"0000-0002-5403-1115","position":9,"is_corresponding":false},{"id":431880,"name":"Suzette J. Bielinski","orcid":"0000-0002-2905-5430","position":10,"is_corresponding":false},{"id":401936,"name":"Rachel Richesson","orcid":"0000-0003-0279-7036","position":11,"is_corresponding":false},{"id":570217,"name":"Laura K. Wiley","orcid":"0000-0001-6681-9754","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:51:56.297114Z","pmid":"41223026","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}