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Development and Validation of a Generative Artificial Intelligence-Based Pipeline for Automated Clinical Data

Marvin N Carlisle1, William A Pace1, Andrew W Liu1,2

  • 1Department of Urology, University of California, San Francisco, 550 16th Street, Box 1695, San Francisco, CA, 94158, United States, 1 5109126645.

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Summary
This summary is machine-generated.

This study introduces UODBLLM, an automated system using large language models (LLMs) for efficient clinical data extraction from electronic health records. The system achieves rapid, cost-effective data retrieval, accelerating clinical research.

Keywords:
GPT-4artificial intelligence large language modelchatbotgenerative artificial intelligencekidney cancerpattern analysisprostate cancer

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Data Management

Background:

  • Manual abstraction of unstructured clinical data is labor-intensive and prone to quality variability.
  • Integrating large language models (LLMs) into research workflows for medical data extraction presents challenges.

Purpose of the Study:

  • To develop and integrate an LLM-based system for automated data extraction from electronic health record (EHR) text reports.
  • To incorporate the system into an established clinical outcomes database for research.

Main Methods:

  • Implemented a generative artificial intelligence pipeline (UODBLLM) with a flexible LLM interface.
  • Utilized extensible markup language (XML)-structured prompts and an open database connectivity interface for data structuring.
  • Evaluated performance based on completion rate, processing time, and extraction accuracy using magnetic resonance imaging (MRI) reports.

Main Results:

  • UODBLLM processed 1800 MRI reports with a 100% completion rate and an average processing time of 8.90 seconds per report.
  • Extraction cost was minimal at $0.009 per report, with consistent performance across multiple batches.
  • Successfully extracted 16 structured clinical elements, including quantitative measurements and categorical assessments, storing data in JSON format.

Conclusions:

  • Demonstrated successful integration of an LLM-based system for rapid, cost-effective clinical data extraction.
  • UODBLLM offers a scalable and secure solution for automating data extraction, enhancing protected health information security.
  • This approach can significantly accelerate clinical research and enable larger-scale database projects.