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Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review. | LitMetric

AI Article Synopsis

  • - The study focuses on improving the extraction of surgical data from electronic health records (EHRs) using a new Natural Language Processing (NLP) algorithm that integrates a Large Language Model (LLM), specifically GPT4-Turbo, to automate the process.
  • - The algorithm operates in two stages: first, a rule-based NLP framework identifies and classifies relevant text segments, which are then verified by the LLM for accuracy on surgical details like type of surgery and complications.
  • - Results showed that the NLP+LLM approach significantly improved accuracy, efficiency, and cost-effectiveness compared to traditional manual methods, indicating its potential for broader use in healthcare settings.

Article Abstract

The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538412PMC
http://dx.doi.org/10.1038/s41598-024-77535-yDOI Listing

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