{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T22:09:40Z","timestamp":1781993380067,"version":"3.54.5"},"reference-count":44,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000038","name":"U.S. Food and Drug Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Information from electronic health records (EHRs) may be incorporated into computable phenotype algorithms in efforts to overcome inaccuracies of algorithms based on administrative claims data alone. However, such efforts can be resource\u2010intensive and unsuccessful. Assessing the feasibility of computable phenotyping for a health outcome of interest (HOI) before proceeding is therefore recommended.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a systematic fitness\u2010for\u2010purpose (FFP) assessment process to implement concepts outlined in a previously described general framework for computable phenotyping incorporating EHR data. Our process includes verifying the HOI is well\u2010defined, reviewing clinical information about the HOI, identifying existing algorithms and their performance, evaluating HOI clinical and data complexity, and determining an overall FFP conclusion and recommendation. We applied this process to 10 HOIs lacking high\u2010performing claims\u2010based algorithms, selecting HOIs of public health importance that varied in clinical and data complexity, including neutropenia, pericardial effusion, and drug\u2010induced liver injury.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>HOIs assessed as having moderate (vs. easy) overall difficulty had characteristics such as the need for natural language processing, integration of multiple laboratory test results, or longitudinal EHR data. HOIs assessed as having high difficulty required using data from multiple EHR sources, ruling out many other potential causes, or relying on low\u2010sensitivity diagnostic tests. Input from experts in EHR data and clinical care was crucial.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>EHR data have the potential to enhance the accuracy of defining certain HOIs for research and surveillance compared to administrative claims data. The process and tools we created will support others in assessing FFP of HOIs for computable phenotyping.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pds.70346","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T03:57:31Z","timestamp":1773374251000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Systematic Process for Assessing Fitness\u2010for\u2010Purpose of Health Outcomes for Computable Phenotyping With Electronic Health Record Data"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7873-8310","authenticated-orcid":false,"given":"Nicole M.","family":"Gatto","sequence":"first","affiliation":[{"name":"Kaiser Permanente Washington Health Research Institute  Seattle Washington USA"},{"name":"Kaiser Permanente Bernard Tyson School of Medicine  Pasadena California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J.","family":"Cronkite","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Washington Health Research Institute  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0087-3429","authenticated-orcid":false,"given":"Paige D.","family":"Wartko","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Washington Health Research Institute  Seattle Washington USA"},{"name":"Department of Epidemiology University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1609-7420","authenticated-orcid":false,"given":"Robert","family":"Ball","sequence":"additional","affiliation":[{"name":"Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research U.S. Food and Drug Administration  Silver Spring Maryland 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