{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T06:03:30Z","timestamp":1790057010228,"version":"4.0.1"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005977","name":"Kaiser Permanente","doi-asserted-by":"publisher","award":["K23 GM144867"],"award-info":[{"award-number":["K23 GM144867"]}],"id":[{"id":"10.13039\/100005977","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005977","name":"Kaiser Permanente","doi-asserted-by":"publisher","award":["R01 HL175626"],"award-info":[{"award-number":["R01 HL175626"]}],"id":[{"id":"10.13039\/100005977","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To describe the implementation process for the Precision Resuscitation with Crystalloids in Sepsis (PRECISE) trial, a pragmatic, multihospital randomized controlled trial (RCT) of a machine learning (ML) algorithm integrated into the electronic health record (EHR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Implementing PRECISE involved building 4 key components into the EHR using standard tools within Epic: (1) automated inclusion criteria, (2) real-time subphenotyping. (3) randomization, and (4) medication alternative alert.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The PRECISE trial was deployed across 6 Emory Healthcare hospitals in June 2024, spanning 6 emergency departments and 17 ICUs with more than 300 ICU beds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Conclusion<\/jats:title>\n                    <jats:p>PRECISE operationalizes an ML algorithm within the Epic to identify a subgroup of sepsis patients and prompts the clinician to order the type of fluids thought to confer a mortality benefit in this subgroup. PRECISE demonstrates that precision RCTs of ML algorithms can be executed entirely within the EHR, a framework that represents a model for future learning health systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag180","type":"journal-article","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T11:45:11Z","timestamp":1787399111000},"source":"Crossref","is-referenced-by-count":0,"title":["Development to implementation to evaluation: step-by-step approach to embedding a clinical trial of a machine learning algorithm in the electronic health record"],"prefix":"10.1093","volume":"9","author":[{"given":"Erin","family":"Morrison","sequence":"first","affiliation":[{"name":"Emory Healthcare , Atlanta, GA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaes","family":"Overley","sequence":"additional","affiliation":[{"name":"Emory Healthcare , Atlanta, GA,","place":["United 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