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Optimizing Chart Review Efficiency in Pressure Injury Evaluation Using ChatGPT.

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Summary
This summary is machine-generated.

Large language models like ChatGPT can significantly speed up and improve the accuracy of wound care chart reviews. This AI tool enhances efficiency for both clinical practice and research in plastic surgery.

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Area of Science:

  • Plastic Surgery
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Chronic wounds, such as pressure injuries, are increasing, complicating manual chart reviews in plastic surgery.
  • Large volumes of patient data and numerous wound outcome variables present challenges for traditional chart review methods.
  • Natural Language Processing (NLP) and large language models (LLMs) offer potential for automating data extraction in wound care.

Purpose of the Study:

  • To evaluate the efficiency and accuracy of using ChatGPT for automated data extraction from patient charts in wound care.
  • To assess the potential of AI, specifically ChatGPT, to streamline chart review processes in clinical settings and research.
  • To determine if ChatGPT can improve data retrieval accuracy and efficiency in sacral wound care.

Main Methods:

  • Utilized a secure, private Azure OpenAI service instance of ChatGPT for chart review.
  • Integrated ChatGPT and a Python script into the existing chart review workflow for patients with sacral wounds.
  • Collected metrics on review time, data extraction accuracy, and quality of ChatGPT-generated insights.

Main Results:

  • ChatGPT reduced average chart review time from 7.56 minutes to 1.03 minutes per chart.
  • Achieved an overall accuracy rate of 0.957, with element-specific accuracy ranging from 0.747 to 0.986.
  • Demonstrated ChatGPT's capability to generate accurate narrative wound descriptions.

Conclusions:

  • ChatGPT significantly enhances the speed and precision of wound care chart reviews.
  • AI integration, exemplified by ChatGPT, holds valuable implications for improving healthcare workflows in clinical care and research.
  • The study highlights the potential of LLMs to revolutionize data extraction and analysis in plastic surgery and wound management.