Large language models can accurately populate Vascular Quality Initiative procedural databases using narrative operative reports
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) can accurately populate Vascular Quality Initiative (VQI) databases from operative reports, improving surgical data entry efficiency and potentially increasing VQI participation.
Area Of Science
- Vascular surgery data management
- Artificial intelligence in healthcare
- Health informatics
Background
- Vascular Quality Initiative (VQI) participation offers valuable resources but is often hindered by time and personnel constraints for data entry.
- Large language models (LLMs) demonstrate potential in natural language processing and text generation, offering a solution to data entry challenges.
Purpose Of The Study
- To evaluate the accuracy of LLMs in populating VQI procedural databases using operative reports.
- To assess the feasibility of using generative AI to streamline data entry for vascular surgery quality initiatives.
Main Methods
- A retrospective study analyzed 150 operative reports for carotid endarterectomy (CEA), endovascular aneurysm repair (EVAR), and infrainguinal lower extremity bypass (LEB) procedures.
- A HIPAA-compliant LLM (Versa, based on ChatGPT) was used to automatically extract and populate VQI data from reports.
- The accuracy of two models, gpt-35-turbo and gpt-4, was compared against existing VQI data, with a metric defined as 'unavailable' if discussed in less than 20% of reports.
Main Results
- The gpt-35-turbo model achieved median accuracy rates of 84.0% for CEA, 92.2% for EVAR, and 84.3% for LEB.
- Excluding routinely unavailable metrics, accuracy increased to 95.5% for CEA, 94.8% for EVAR, and 93.2% for LEB.
- Gpt-4 did not significantly improve performance over gpt-35-turbo, and processing costs were minimal ($0.12 for gpt-35-turbo vs. $3.39 for gpt-4 for 150 reports).
Conclusions
- LLMs can accurately populate VQI databases with structured and unstructured data at a low cost.
- Increased workflow efficiency through LLMs may enhance a center's ability to participate in the VQI.
- Further research is warranted to explore other VQI databases and improve LLM accuracy for surgical data management.
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