{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T14:32:11Z","timestamp":1793197931230,"version":"4.5.0"},"reference-count":20,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Emerg Medicine Australasia"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To investigate whether the Sydney Triage to Admission Risk Tool with Artificial Intelligence (START\u2010AI) model could be used to predict specific inpatient admitting teams based on clinical notes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This was a pre\u2010trained language model analysis using electronic medical record data from an inner\u2010city tertiary referral hospital emergency department (ED) in Sydney, Australia. All adult patients who were admitted to an inpatient ward between January 2023 and June 2025 were included. A fine\u2010tuned Bidirectional Encoder Representation of Transformer (Bio\u2010ClinicalBERT) used in the original START\u2010AI analysis was trained on ED medical case history notes to predict one of twenty specialist inpatient admitting teams.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 40,054 cases were analysed. The overall weighted average area under receiver operating characteristic curve (AUROC) was 0.97 (95% CI: 0.96, 0.98). For individual classes of inpatient admitting teams, AUROC ranged from 0.91 for infectious diseases to 0.99 for psychiatry and gynaecology. The overall weighted average precision (positive predictive value) was moderate at 0.71 (95% CI: 0.71, 0.72). The model correctly classified around three quarters of the dataset (accuracy 0.75 (95% CI: 0.74, 0.076)). Accuracy in correctly assigning one of the two most probable admitting teams by the model was 0.88 (95% CI: 0.88, 0.89).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A pre\u2010trained language model used in START\u2010AI was able to accurately predict the inpatient admitting team for ED patients requiring admission based on ED medical case history notes in a single centre study. Further exploration and validation are warranted to assess clinical utility and broader feasibility of the modelling approach.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1742-6723.70341","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T23:40:00Z","timestamp":1788478800000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using Sydney Triage to Admission Risk Tool With Artificial Intelligence (\n                    <scp>START<\/scp>\n                    \u2010\n                    <scp>AI<\/scp>\n                    ) to Predict Inpatient Admitting Specialty for Emergency Department Patients: Single Centre Deep Learning Analysis"],"prefix":"10.1111","volume":"38","author":[{"given":"Michael","family":"Dinh","sequence":"first","affiliation":[{"name":"RPA Green Light Institute Sydney Local Health District  Sydney New South Wales Australia"},{"name":"Emergency Department Royal Prince Alfred Hospital, Sydney Local Health District  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4168-1428","authenticated-orcid":false,"given":"Elizabeth","family":"Corbett","sequence":"additional","affiliation":[{"name":"RPA Green Light Institute Sydney Local Health District  Sydney New South Wales Australia"},{"name":"Emergency Department Royal Prince Alfred Hospital, Sydney Local Health District  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2017-9999","authenticated-orcid":false,"given":"Thuy Truc","family":"Ngo","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering The University of Sydney  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6212-3898","authenticated-orcid":false,"given":"Eliot","family":"Salmon","sequence":"additional","affiliation":[{"name":"RPA Green Light Institute Sydney Local Health District  Sydney New South Wales Australia"},{"name":"Emergency Department Royal Prince Alfred Hospital, Sydney Local Health District  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5862-577X","authenticated-orcid":false,"given":"Saleem Ahmed","family":"Khan","sequence":"additional","affiliation":[{"name":"Digital Health and Innovation Sydney Local Health District  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2745-8535","authenticated-orcid":false,"given":"Farhana","family":"Pethani","sequence":"additional","affiliation":[{"name":"Digital Health and 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