{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T13:17:57Z","timestamp":1784899077509,"version":"3.55.0"},"reference-count":69,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003550","name":"Queensland Government","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003550","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Advanced Queensland Industry Research Fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end clinical AI implementation framework.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>Systematically review studies of clinically applied AI-based sepsis prediction algorithms in regard to methodological quality, deployment and evaluation methods, and outcomes. Identify contextual factors that influence implementation and map these factors to the SALIENT implementation framework.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The review identified 30 articles of algorithms applied in adult hospital settings, with 5 studies reporting significantly decreased mortality post-implementation. Eight groups of algorithms were identified, each sharing a common algorithm. We identified 14 barriers, 26 enablers, and 22 decision points which were able to be mapped to the 5 stages of the SALIENT implementation framework.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>Empirical studies of deployed sepsis prediction algorithms demonstrate their potential for improving care and reducing mortality but reveal persisting gaps in existing implementation guidance. In the examined publications, key decision points reflecting real-word implementation experience could be mapped to the SALIENT framework and, as these decision points appear to be AI-task agnostic, this framework may also be applicable to non-sepsis algorithms. The mapping clarified where and when barriers, enablers, and key decisions arise within the end-to-end AI implementation process.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>A systematic review of real-world implementation studies of sepsis prediction algorithms was used to validate an end-to-end staged implementation framework that has the ability to account for key factors that warrant attention in ensuring successful deployment, and which extends on previous AI implementation frameworks.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocad075","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T19:58:10Z","timestamp":1683921490000},"page":"1349-1361","source":"Crossref","is-referenced-by-count":57,"title":["Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework"],"prefix":"10.1093","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5642-5188","authenticated-orcid":false,"given":"Anton H","family":"van der Vegt","sequence":"first","affiliation":[{"name":"Queensland Digital Health Centre, The University of Queensland , Brisbane, Queensland, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian A","family":"Scott","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital , Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna","family":"Dermawan","sequence":"additional","affiliation":[{"name":"Centre for Information Resilience, The University of Queensland , St Lucia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rudolf J","family":"Schnetler","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering, The University of Queensland , St Lucia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vikrant R","family":"Kalke","sequence":"additional","affiliation":[{"name":"Patient Safety and Quality, Clinical Excellence Queensland, Queensland Health , Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul J","family":"Lane","sequence":"additional","affiliation":[{"name":"Safety Quality & Innovation, The Prince Charles Hospital, Queensland Health , Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"issue":"10219","key":"2023062008373561300_ocad075-B1","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/S0140-6736(19)32989-7","article-title":"Global, regional, and national sepsis incidence and mortality, 1990\u20132017: analysis for the Global Burden of Disease Study","volume":"395","author":"Rudd","year":"2020","journal-title":"Lancet"},{"issue":"36","key":"2023062008373561300_ocad075-B2","doi-asserted-by":"crossref","first-page":"E1058","DOI":"10.1503\/cmaj.170149","article-title":"Clinical implications of the third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"190","author":"Fernando","year":"2018","journal-title":"CMAJ"},{"issue":"6","key":"2023062008373561300_ocad075-B3","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1378\/chest.101.6.1644","article-title":"Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis","volume":"101","author":"Bone","year":"1992","journal-title":"Chest"},{"issue":"1","key":"2023062008373561300_ocad075-B4","doi-asserted-by":"crossref","first-page":"E2","DOI":"10.1503\/cmaj.160798","article-title":"Reducing the global burden of sepsis","volume":"189","author":"Dugani","year":"2017","journal-title":"CMAJ"},{"issue":"23","key":"2023062008373561300_ocad075-B5","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1056\/NEJMoa1703058","article-title":"Time to treatment and mortality during mandated emergency care for sepsis","volume":"376","author":"Seymour","year":"2017","journal-title":"N Engl J Med"},{"key":"2023062008373561300_ocad075-B6","doi-asserted-by":"crossref","first-page":"104457","DOI":"10.1016\/j.ijmedinf.2021.104457","article-title":"Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? 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