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04/01/26

Large language model-based identification of venous thromboembolism diagnostic delays.

Schaye, Verity; Sartori, Daniel J; Signoriello, Lexi; Malhotra, Kiran; Guzman, Benedict; Rajput, Bijal; Reinstein, Ilan; Burk-Rafel, Jesse. J Hosp Med.

Collection Topics
AI and Diagnosis Equity, Access, and Timeliness Health IT
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Summary

Delayed venous thromboembolism (VTE) diagnosis is an impactful harm in hospitals, but identifying cases is difficult and feedback is limited. This retrospective real-world study shows that a LLM-based electronic trigger outperforms traditional electronic triggers in detecting VTE delays; although positive predictive value was only 48% and observed incidence of VTE Dx delay was quite low.
Read the paper
Schaye V, Sartori DJ, Signoriello L, et al. Large language model-based identification of venous thromboembolism diagnostic delays. J Hosp Med. 2026;21(4):391-401.