aceLarge Language Model Data Abstraction Demonstrates Accuracy and Reliability for NSQIP
Background National Surgical Quality Improvement Program (NSQIP) data collection depends on labor-intensive manual chart abstraction, limiting efficiency, increasing cost, and necessitating patient sampling. This study evaluated whether a large language model (LLM) could accurately abstract unstructured NSQIP breast reconstruction variables compared with conventional human abstraction. Study design Clinical notes from patients enrolled in the NSQIP Breast Reconstruction pilot program (July 1, 2024-February 28, 2…
A customized ChatGPT 4.1 workflow abstracted NSQIP surgical records with 99.33% accuracy versus 98.19% for human abstraction, beating the human baseline.
Evidence
- Peer-reviewedJournal of the American College of Surgeons2026-09-18
How should this claim be treated?
Truvace Impact Record TRV-2026-1141, v2: “Large Language Model Data Abstraction Demonstrates Accuracy and Reliability for NSQIP.” Truvace, 2026-10-26. /record/TRV-2026-1141 (accessed at citation time). sha256 e53b58eb80944900…
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