{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T07:18:22Z","timestamp":1787815102361,"version":"build-2784847793"},"reference-count":33,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003550","name":"Queensland Government","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003550","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Evaluation Clinical Practice"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Artificial intelligence (AI)\u2010enabled risk prediction models are being increasingly used in hospital practice to predict patient risk of adverse events, aimed at giving early warning of impending events and facilitating preventive intervention. However, evidence of beneficial impact varies which may be due to clinicians not finding them useful because of suboptimal predictive accuracy (effectiveness), burden of false alerts (efficiency) or narrow prediction windows (utility). Current model performance measures do not capture the interdependency of these three dimensions. In this commentary, using sepsis risk prediction as an example, we present methods that assist clinicians to: assess the suitability of particular models and decide ideal decision thresholds; optimise model performance; and configure and deliver alerts to frontline clinicians.<\/jats:p>","DOI":"10.1111\/jep.70557","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T18:43:28Z","timestamp":1786387408000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["How Can Clinicians Decide if AI\u2010Enabled Risk Prediction Models Are Fit for Purpose for Real World Use?"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7596-0837","authenticated-orcid":false,"given":"Ian A.","family":"Scott","sequence":"first","affiliation":[{"name":"Digital Health and Informatics Directorate Metro South Hospital and Health Service Brisbane Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anton H.","family":"van der Vegt","sequence":"additional","affiliation":[{"name":"Centre for Health Services Research University of Queensland Brisbane Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victoria","family":"Campbell","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Sunshine Coast University Hospital Burtinya Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul J.","family":"Lane","sequence":"additional","affiliation":[{"name":"Safety Quality and Innovation, The Prince Charles Hospital Brisbane Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Rice","sequence":"additional","affiliation":[{"name":"Patient Safety and Quality, Clinical Excellence Queensland Brisbane Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balasubramanian","family":"Venkatesh","sequence":"additional","affiliation":[{"name":"Patient Safety and Quality, Clinical Excellence Queensland Brisbane Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocad075"},{"issue":"1","key":"e_1_2_12_3_1","first-page":"S9","article-title":"Sepsis Epidemiology in Australian Public Hospitals, a Nationwide Longitudinal Study (2013\u20132018)","volume":"26","author":"Li L.","year":"2019","journal-title":"Infection, Disease and Health"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.13836"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000000330"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-019-05872-y"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00650-5"},{"key":"e_1_2_12_8_1","first-page":"1","article-title":"Rethinking Human\u2010AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis","author":"Zhang S.","year":"2024","journal-title":"Proceedings of the CHI Conference on Human Factors in Computing Systems"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000003803"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.landig.2025.100916"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtcvs.2021.07.041"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-015-0999-1"},{"key":"e_1_2_12_13_1","doi-asserted-by":"crossref","DOI":"10.1177\/20552076251404509","article-title":"Using Workload Capacity Indicators to Evaluate Rule\u2010Based Early Warning Tools and Their Relationship to Escalation Events","volume":"12","author":"Vegt A. 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