Optimizing Chart Review Efficiency in Pressure Injury Evaluation Using ChatGPT.

Journal: Annals of plastic surgery
PMID: 40167094

Abstract

INTRODUCTION: Wound care is an essential discipline in plastic surgery, especially as the prevalence of chronic wounds, such as pressure injuries, is increasing. The escalating volume of patient data and the numerous variables influencing wound outcomes are making traditional manual chart reviews in wound care and research increasingly complex and burdensome. The emergence of Natural Language Processing (NLP) software based on large language models (LLMs) such as ChatGPT presents an opportunity to automate the data extraction process. This study harnesses the capabilities of ChatGPT, hosted by our medical center's secure, private Azure OpenAI service, to automatically extract and process variables from patient charts following sacral wound visits. We assess ChatGPT's potential to revolutionize chart review through improved data retrieval accuracy and efficiency.

Authors

  • Rebecca Friedman
    From the Hansjörg Wyss Department of Plastic Surgery, New York University Langone Health, New York, New York.
  • Rebecca Lisk
    From the Hansjörg Wyss Department of Plastic Surgery, New York University Langone Health, New York, New York.
  • Katherine Cordero-Bermudez
    Department of Surgery NYU Langone Hospital - Long Island, Mineola, New York.
  • Soniya Singh
    Department of Surgery NYU Langone Hospital - Long Island, Mineola, New York.
  • Sofia Ghani
    From the Hansjörg Wyss Department of Plastic Surgery, New York University Langone Health, New York, New York.
  • Brian M Gillette
    Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York.
  • Scott A Gorenstein
    Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York.
  • Ernest S Chiu
    NYU Kimmel Hyperbaric and Advanced Wound Healing Center, New York, New York.

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