aceDiagnostic accuracy of electronic medical record retrieval methods and a large language model for identifying cardiovascular events: a multisite retrospective validation study in a medical system in the United States
To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large language model (LLM)-assisted workflow, against manual chart adjudication for identifying cardiovascular events. Retrospective diagnostic accuracy study. Three sites within a single US tertiary health system. Two adult cohorts with previously adjudicated cardiovascular outcomes were included. Cohort 1 included 2258 patients treated with immune checkpoint inhibitors, and Cohort 2 inc…
A zero-shot LLM-assisted workflow improved automated identification of cardiovascular events from EMRs, achieving the highest AUCs for stroke, MI and composite MACE in two cohorts compared to ICD codes, primary diagnosis and problem list methods.
The LLM-assisted workflow did not consistently outperform ICD-based retrieval, with no statistically significant AUC difference in Cohort 1 and lower AUC than ICD codes for heart failure in that cohort.
Retrospective validation limited to three sites within a single US tertiary health system with two specific cohorts, and performance was context-dependent with ICD-based retrieval remaining competitive for some outcomes.
Evidence
- Peer-reviewedBMJ Open2026-08-13
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Truvace Impact Record TRV-2026-0790, v1: “Diagnostic accuracy of electronic medical record retrieval methods and a large language model for identifying cardiovascular events: a multisite retrospective validation study in a medical system in the United States.” Truvace, 2026-08-16. /record/TRV-2026-0790 (accessed at citation time). sha256 dabd8d5645fff82d…
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