{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T20:32:14Z","timestamp":1781641934672,"version":"3.54.5"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360\u00b0 continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae036","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T19:47:42Z","timestamp":1709236062000},"page":"1195-1198","source":"Crossref","is-referenced-by-count":27,"title":["Sustainable deployment of clinical prediction tools\u2014a 360\u00b0 approach to model maintenance"],"prefix":"10.1093","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0792-8867","authenticated-orcid":false,"given":"Sharon E","family":"Davis","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J","family":"Emb\u00ed","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203, United States"},{"name":"Department of Medicine, Vanderbilt University Medical Center , Nashville, TN 37232, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael E","family":"Matheny","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203, United States"},{"name":"Department of Medicine, Vanderbilt University Medical Center , Nashville, TN 37232, United States"},{"name":"Department of Biostatistics, Vanderbilt University Medical Center , Nashville, TN 37203, United States"},{"name":"Geriatric Research, Education, and Clinical Care, Tennessee Valley Healthcare System VA Medical Center, Veterans Health Administration , Nashville, TN 37212, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"issue":"6","key":"2024041923350804100_ocae036-B1","doi-asserted-by":"crossref","first-page":"100489","DOI":"10.1016\/j.patter.2022.100489","article-title":"An artificial intelligence life cycle: from conception to production","volume":"3","author":"De Silva","year":"2022","journal-title":"Patterns (N Y)"},{"issue":"2","key":"2024041923350804100_ocae036-B2","first-page":"345","article-title":"From development to deployment: dataset shift, causality, and shift-stable models in health AI","volume":"21","author":"Subbaswamy","year":"2020","journal-title":"Biostatistics"},{"issue":"3","key":"2024041923350804100_ocae036-B3","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1056\/NEJMc2104626","article-title":"The clinician and dataset shift in artificial intelligence","volume":"385","author":"Finlayson","year":"2021","journal-title":"N Engl J Med"},{"issue":"11","key":"2024041923350804100_ocae036-B4","doi-asserted-by":"crossref","first-page":"e2135286","DOI":"10.1001\/jamanetworkopen.2021.35286","article-title":"Quantification of sepsis model alerts in 24 US hospitals before and during the COVID-19 pandemic","volume":"4","author":"Wong","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"1","key":"2024041923350804100_ocae036-B5","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s12916-023-02779-w","article-title":"There is no such thing as a validated prediction model","volume":"21","author":"Van Calster","year":"2023","journal-title":"BMC Med"},{"key":"2024041923350804100_ocae036-B6","doi-asserted-by":"crossref","DOI":"10.17226\/27111","volume-title":"Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril","author":"Matheny","year":"2019"},{"key":"2024041923350804100_ocae036-B7","doi-asserted-by":"crossref","first-page":"958284","DOI":"10.3389\/fdgth.2022.958284","article-title":"Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings","volume":"4","author":"Davis","year":"2022","journal-title":"Front Digit Health"},{"issue":"4","key":"2024041923350804100_ocae036-B8","doi-asserted-by":"crossref","first-page":"e214622","DOI":"10.1001\/jamanetworkopen.2021.4622","article-title":"Algorithmovigilance-advancing methods to analyze and monitor artificial intelligence-driven health care for effectiveness and equity","volume":"4","author":"Embi","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"1","key":"2024041923350804100_ocae036-B9","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/s41746-022-00611-y","article-title":"Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare","volume":"5","author":"Feng","year":"2022","journal-title":"NPJ Digit Med"},{"issue":"4","key":"2024041923350804100_ocae036-B10","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1055\/s-0041-1735184","article-title":"Systematic review of approaches to preserve machine learning performance in the presence of temporal dataset shift in clinical medicine","volume":"12","author":"Guo","year":"2021","journal-title":"Appl Clin Inform"},{"key":"2024041923350804100_ocae036-B11","doi-asserted-by":"crossref","first-page":"103611","DOI":"10.1016\/j.jbi.2020.103611","article-title":"Detection of calibration drift in clinical prediction models to inform model updating","volume":"112","author":"Davis","year":"2020","journal-title":"J Biomed Inform"},{"issue":"23","key":"2024041923350804100_ocae036-B12","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s41512-018-0045-2","article-title":"Dynamic models to predict health outcomes: current status and methodological challenges","volume":"2","author":"Jenkins","year":"2018","journal-title":"Diagn Progn Res"},{"issue":"1","key":"2024041923350804100_ocae036-B13","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1148\/radiol.2020200038","article-title":"Continuous learning AI in radiology: implementation principles and early applications","volume":"297","author":"Pianykh","year":"2020","journal-title":"Radiology"},{"issue":"8","key":"2024041923350804100_ocae036-B14","doi-asserted-by":"crossref","first-page":"e008635","DOI":"10.1161\/CIRCOUTCOMES.121.008635","article-title":"Maintaining a national acute kidney injury risk prediction model to support local quality benchmarking","volume":"15","author":"Davis","year":"2022","journal-title":"Circ Cardiovasc Qual Outcomes"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/31\/5\/1195\/57286273\/ocae036.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/31\/5\/1195\/57286273\/ocae036.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T23:35:30Z","timestamp":1713569730000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/31\/5\/1195\/7616485"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,29]]},"references-count":14,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,2,29]]},"published-print":{"date-parts":[[2024,4,19]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocae036","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,5,1]]},"published":{"date-parts":[[2024,2,29]]}}}