Journal of orthopaedic trauma · 2026
Improving Emergency Department Efficiency With Large Language Model-Guided Orthopaedic Triage for Proximal Humerus Fractures.
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Zhao Lucy, Bott Ethan, Rao Arya S, Borgida Jacob S, Brown Samantha, Wagner Robert K, Harris Mitchel B, Ly Thuan V, Succi Marc D
Аннотация
OBJECTIVES: To evaluate whether large language models can reduce consults for proximal humerus fractures that do not meet institutional consult criteria.
DESIGN: Retrospective review.
SETTING: Single-center Level 1 trauma center.
PATIENT SELECTION CRITERIA: 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.
OUTCOME MEASURES AND COMPARISONS: 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-based wRVUs for a 30-minute low-to-moderate complexity outpatient consult.
RESULTS: Three-hundred fifteen patients (99 males and 216 females) were included (average age: 65.1 years, range: 20-100 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.
CONCLUSIONS: Large language models accurately identified uncomplicated proximal humerus fractures, potentially conserving unnecessary ED orthopaedic consults.
LEVEL OF EVIDENCE: Prognostic, Level III. See Instructions for Authors for a complete description of levels of evidence.
DESIGN: Retrospective review.
SETTING: Single-center Level 1 trauma center.
PATIENT SELECTION CRITERIA: 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.
OUTCOME MEASURES AND COMPARISONS: 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-based wRVUs for a 30-minute low-to-moderate complexity outpatient consult.
RESULTS: Three-hundred fifteen patients (99 males and 216 females) were included (average age: 65.1 years, range: 20-100 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.
CONCLUSIONS: Large language models accurately identified uncomplicated proximal humerus fractures, potentially conserving unnecessary ED orthopaedic consults.
LEVEL OF EVIDENCE: Prognostic, Level III. See Instructions for Authors for a complete description of levels of evidence.