Patient Triage and Guidance in Emergency Departments Using Large Language Models: Multimetric Study.

Journal: Journal of medical Internet research
Published Date: May 15, 2025

Abstract

BACKGROUND: Emergency departments (EDs) face significant challenges due to overcrowding, prolonged waiting times, and staff shortages, leading to increased strain on health care systems. Efficient triage systems and accurate departmental guidance are critical for alleviating these pressures. Recent advancements in large language models (LLMs), such as ChatGPT, offer potential solutions for improving patient triage and outpatient department selection in emergency settings.

Authors

  • Chenxu Wang
    West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Shuhan Li
  • Qing-Wen Ren
    Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, China (Hong Kong).
  • Xiaomei Tan
    Department of Industrial Engineering, Sichuan University, Chengdu, China.
  • Yaoyu Fu
    West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
  • Di Liu
    Laboratory of Nutrition and Functional Food, College of Food Science and Engineering, Jilin University, Changchun, China.
  • Guangwu Qian
    West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
  • Yu Cao
    Department of Neurosurgery, FuShun County Zigong City People's Hospital, Fushun, China.
  • Rong Yin
    West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China. rong.yin@scupi.cn.
  • Kang Li
    Department of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, China.

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