{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T23:03:07Z","timestamp":1763852587177},"reference-count":27,"publisher":"Georg Thieme Verlag KG","issue":"04","funder":[{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["R01LM009886\u201311"],"award-info":[{"award-number":["R01LM009886\u201311"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Center for Advancing Clinical and Translational Science","award":["UL1TR001873"],"award-info":[{"award-number":["UL1TR001873"]}]},{"name":"National Center for Advancing Clinical and Translational Science","award":["3U24TR001579"],"award-info":[{"award-number":["3U24TR001579"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population.<\/jats:p><jats:p>\n          Objectives\u2003This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage.<\/jats:p><jats:p>\n          Methods\u2003We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial.<\/jats:p><jats:p>\n          Results\u2003We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness.<\/jats:p><jats:p>\n          Conclusion\u2003This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.<\/jats:p>","DOI":"10.1055\/s-0041-1733846","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T23:10:06Z","timestamp":1631142606000},"page":"816-825","source":"Crossref","is-referenced-by-count":6,"title":["A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data"],"prefix":"10.1055","volume":"12","author":[{"given":"Yingcheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alex","family":"Butler","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"},{"name":"Department of Medicine, Columbia University, New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibrahim","family":"Diallo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae Hyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Casey","family":"Ta","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James R.","family":"Rogers","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhua","family":"Weng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"volume-title":"Clinical Trials: A Methodologic Perspective","year":"2017","author":"S 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