医学
质量保证
胸痛
医疗急救
紧急医疗服务
急诊医学
医学物理学
内科学
病理
外部质量评估
作者
NULL AUTHOR_ID,Brent Klapthor,NULL AUTHOR_ID,NULL AUTHOR_ID,NULL AUTHOR_ID,NULL AUTHOR_ID,Scott T. Youngquist
标识
DOI:10.1080/10903127.2024.2376757
摘要
OBJECTIVES: This study assesses the feasibility, inter-rater reliability, and accuracy of using OpenAI's ChatGPT-4 and Google's Gemini Ultra large language models (LLMs), for Emergency Medical Services (EMS) quality assurance. The implementation of these LLMs for EMS quality assurance has the potential to significantly reduce the workload on medical directors and quality assurance staff by automating aspects of the processing and review of patient care reports. This offers the potential for more efficient and accurate and identification of areas requiring improvement, thereby potentially enhancing patient care outcomes
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