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TRV-2026-1141Version 1 · Certified

Written 2026-09-19 06:53:33 UTC · current record

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TRUVACE RECORD VERSION
record: TRV-2026-1141
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-19T06:53:33.534187Z
status: published
lens: p_space
sector: health
headline: Large Language Model Data Abstraction Demonstrates Accuracy and Reliability for NSQIP
dek: 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…
gain_title: (none)
problem_title: Overall abstraction accuracy was 99.33% (61 errors) for the LLM versus 98.19% (164 errors) for human abstraction (McNemar p Conclusions In this proof-of-concept validation study, a customized LLM achieved significantly higher abstraction accuracy than conventional human review for general and breast reconstruction NSQIP variables.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Overall abstraction accuracy was 99.33% (61 errors) for the LLM versus 98.19% (164 errors) for human abstraction (McNemar p Conclusions In this proof-of-concept validation study, a customized LLM achieved significantly higher abstraction accuracy than conventional human review for general and breast reconstruction NSQIP variables.
problem_evidence: (none)
quick_read: 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, 2025) were manually de-identified and processed using a customized ChatGPT 4.1 workflow targeting individual variables. Overall accuracy of LLM and human abstraction was compared using McNemar's and Chi-square tests.
limitation: 
tag: Evidence-backed problem
key_points: 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, 2025) were manually de-identified and processed using a customized ChatGPT 4.1 workflow targeting individual variables.
rundown: 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, 2025) were manually de-identified and processed using a customized ChatGPT 4.1 workflow targeting individual variables. A faculty plastic surgeon established the reference standard.
sources:
- peer_reviewed | Journal of the American College of Surgeons | https://doi.org/10.1097/xcs.0000000000002211 | 2026-09-18
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