{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T08:14:51Z","timestamp":1778228091157,"version":"3.51.4"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Artificial intelligence\/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can detect potential safety signals. There is a lack of consensus in establishing governance to deploy, pilot, and monitor algorithms within operational healthcare delivery workflows. Here, we describe a governance framework that combines current regulatory best practices and lifecycle management of predictive models being used for clinical care. Since January 2021, we have successfully added models to our governance portfolio and are currently managing 52 models.<\/jats:p>","DOI":"10.1093\/jamia\/ocac078","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T01:04:27Z","timestamp":1654045467000},"page":"1631-1636","source":"Crossref","is-referenced-by-count":93,"title":["A framework for the oversight and local deployment of safe and high-quality prediction models"],"prefix":"10.1093","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6496-7024","authenticated-orcid":false,"given":"Armando D","family":"Bedoya","sequence":"first","affiliation":[{"name":"Department of Medicine, Duke University , Durham, North Carolina, USA"},{"name":"Duke University Health System , Durham, North Carolina, USA"}]},{"given":"Nicoleta J","family":"Economou-Zavlanos","sequence":"additional","affiliation":[{"name":"Duke University School of Medicine , Durham, North Carolina, USA"}]},{"given":"Benjamin A","family":"Goldstein","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Duke University School of Medicine , Durham, North Carolina, USA"}]},{"given":"Allison","family":"Young","sequence":"additional","affiliation":[{"name":"Duke University School of Medicine , Durham, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7196-817X","authenticated-orcid":false,"given":"J Eric","family":"Jelovsek","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Duke University , Durham, North Carolina, USA"}]},{"given":"Cara","family":"O\u2019Brien","sequence":"additional","affiliation":[{"name":"Department of Medicine, Duke University , Durham, North Carolina, USA"},{"name":"Duke University Health System , Durham, North Carolina, USA"}]},{"given":"Amanda B","family":"Parrish","sequence":"additional","affiliation":[{"name":"Duke University School of Medicine , Durham, North Carolina, USA"}]},{"given":"Scott","family":"Elengold","sequence":"additional","affiliation":[{"name":"Office of Counsel, Duke University , Durham, North Carolina, USA"}]},{"given":"Kay","family":"Lytle","sequence":"additional","affiliation":[{"name":"Duke University Health System , Durham, North Carolina, USA"}]},{"given":"Suresh","family":"Balu","sequence":"additional","affiliation":[{"name":"Duke Institute for Health Innovation , Durham, North Carolina, USA"}]},{"given":"Erich","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Medicine, Duke University , Durham, North Carolina, USA"},{"name":"Duke University Health System , Durham, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7251-5842","authenticated-orcid":false,"given":"Eric G","family":"Poon","sequence":"additional","affiliation":[{"name":"Department of Medicine, Duke University , Durham, North Carolina, USA"},{"name":"Duke University Health System , Durham, North Carolina, USA"},{"name":"Department of Biostatistics and Bioinformatics, Duke University School of Medicine , Durham, North Carolina, USA"}]},{"given":"Michael J","family":"Pencina","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Duke University School of Medicine , Durham, North Carolina, USA"},{"name":"Duke AI Health, Duke University School of Medicine , Durham, North Carolina, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"issue":"2","key":"2022081707580471700_ocac078-B1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1093\/jamiaopen\/ooz046","article-title":"Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?","volume":"3","author":"Watson","year":"2020","journal-title":"JAMIA 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deterioration","volume":"47","author":"Bedoya","year":"2019","journal-title":"Crit Care Med"},{"issue":"10","key":"2022081707580471700_ocac078-B6","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J Am Med Inform Assoc"},{"key":"2022081707580471700_ocac078-B7","year":"2018"},{"key":"2022081707580471700_ocac078-B8"},{"key":"2022081707580471700_ocac078-B9","volume-title":"ITIL Service Strategy","author":"Cannon","year":"2011","edition":"2nd ed"},{"key":"2022081707580471700_ocac078-B10","volume-title":"The Six Sigma Handbook","author":"Pyzdek","year":"2014","edition":"3rd ed"},{"key":"2022081707580471700_ocac078-B11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41746-020-0253-3","article-title":"Presenting machine learning model information to 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