{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T01:46:43Z","timestamp":1792460803269,"version":"4.5.0"},"reference-count":56,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"8","funder":[{"DOI":"10.13039\/100006691","name":"Harvard Medical School","doi-asserted-by":"crossref","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}],"id":[{"id":"10.13039\/100006691","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"crossref","award":["T32GM144273"],"award-info":[{"award-number":["T32GM144273"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVES:<\/jats:title>\n                    <jats:p>To evaluate whether large language models can reduce consults for proximal humerus fractures that do not meet institutional consult criteria.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design:<\/jats:title>\n                    <jats:p>Retrospective review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting:<\/jats:title>\n                    <jats:p>Single-center Level 1 trauma center.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patient Selection Criteria:<\/jats:title>\n                    <jats:p>Adults presenting to the emergency department (ED) with isolated proximal humerus fractures over a 2-year period were included. Exclusion criteria were polytrauma, concomitant orthopaedic injuries, pathologic fractures, lack of in-house ED imaging, and fractures missed in the ED.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Outcome Measures and Comparisons:<\/jats:title>\n                    <jats:p>Generative Pretrained Transformer-4o (GPT-4o) and o4-mini were provided history of present illnesses, physical examinations, and X-ray reports and asked whether orthopaedics consultation was indicated based on institutional criteria (open fracture, tenting skin, neurovascular compromise, or humeral head dislocation). A gold standard was determined by 2 independent authors who retrospectively reviewed each case and reached consensus on consult necessity based on these criteria. Large language models alignment with this standard was compared with performance of real-world providers using generalized linear models. Consult wait time and work relative value unit (wRVU) savings were estimated using the cohort's average wait time and Current Procedural Terminology\u2013based wRVUs for a 30-minute low-to-moderate complexity outpatient consult.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>Three-hundred fifteen patients (99 males and 216 females) were included (average age: 65.1 years, range: 20\u2013100 years). Alignment with consult criteria was 92.4% [95% confidence interval (CI) (88.9%, 94.8%)] for GPT-4o, 94.9% [95% CI (91.9%, 96.9%)] for o4-mini and 32.7% [95% CI (27.7%, 38.1%)] for ED providers. From a baseline of 240 consults, 327.3 wait hours, and 432 wRVUs, GPT-4o could have saved 179 consults, 295.3 wait hours, and 322.2 wRVUs over 2 years. o4-mini could have saved 183 consults, 302.0 wait hours, and 329.4 wRVUs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>Large language models accurately identified uncomplicated proximal humerus fractures, potentially conserving unnecessary ED orthopaedic consults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>LEVEL OF EVIDENCE:<\/jats:title>\n                    <jats:p>Prognostic, Level III. See Instructions for Authors for a complete description of levels of evidence.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1097\/bot.0000000000003175","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T13:00:23Z","timestamp":1775566823000},"page":"e174-e180","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":2,"title":["Improving Emergency Department Efficiency With Large Language Model\u2013Guided Orthopaedic Triage for Proximal Humerus Fractures"],"prefix":"10.1097","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9198-062X","authenticated-orcid":false,"given":"Lucy","family":"Zhao","sequence":"first","affiliation":[{"name":"Harvard Medical School, Boston, MA;"},{"name":"Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Mass General Brigham, Boston, MA; and"},{"name":"Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Bott","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA;"},{"name":"Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Mass General Brigham, Boston, MA; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arya S.","family":"Rao","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA;"},{"name":"Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Mass General Brigham, Boston, MA; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob S.","family":"Borgida","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samantha","family":"Brown","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert K.","family":"Wagner","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitchel B.","family":"Harris","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA;"},{"name":"Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thuan V.","family":"Ly","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA;"},{"name":"Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1518-3984","authenticated-orcid":false,"given":"Marc D.","family":"Succi","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA;"},{"name":"Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Mass General Brigham, Boston, MA; and"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"R1-20260809","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.3390\/healthcare10091625","article-title":"Overcrowding in emergency department: causes, consequences, and solutions\u2014A narrative review","volume":"10","author":"Sartini","year":"2022","journal-title":"Healthcare"},{"key":"R2-20260809","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1007\/s11739-023-03239-2","article-title":"Emergency department crowding: an overview of reviews describing measures causes, and harms","volume":"18","author":"Pearce","year":"2023","journal-title":"Intern Emerg Med"},{"key":"R3-20260809","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1097\/EC9.0000000000000078","article-title":"Emergency department overcrowding: causes and solutions","volume":"3","author":"Butun","year":"2023","journal-title":"Emerg Crit Care Med"},{"key":"R4-20260809","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1212\/CPJ.0000000000000295","article-title":"Can improved specialty access moderate emergency department overuse? 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