Large language models can accurately populate Vascular Quality Initiative procedural databases using narrative operative reports
LLMs can accurately populate VQI procedural databases with both structured and unstructured data, while incurring only minor processing costs. Increased workflow efficiency may improve center ability to successfully participate in the VQI. Further work examining other VQI databases and methods to increase accuracy is needed.
Authors
- Michael S. Conte (ORCID: https://orcid.org/0000-0001-9119-192X)
- Peter A. Schneider (ORCID: https://orcid.org/0000-0002-4498-5150)
- Colleen P. Flanagan (ORCID: https://orcid.org/0000-0003-2611-0956)
- Warren J. Gasper (ORCID: https://orcid.org/0000-0002-7773-5916)
- Karen Trang (ORCID: https://orcid.org/0000-0003-4445-9334)
- Elizabeth Collens Wick (ORCID: https://orcid.org/0000-0003-0365-2816)
- Joyce Nacario
- Allan M. Conway (ORCID: https://orcid.org/0000-0003-2137-5250)
Institutions
- University of California, San Francisco (US)
- University of San Francisco (US)
Publication Details
- Journal
- Journal of Vascular Surgery
- Published
- 2024-12-16
- DOI
- https://doi.org/10.1016/j.jvs.2024.12.002
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.60