使用大型语言模型自动识别工作场所暴力和沟通失败事件报告
Mike Becker1, Sy Hwang2, Emily Schriver1
1University of Pennsylvania Health System, Philadelphia, PA.
概括
大型语言模型可以自动分类安全事件报告,提高工作场所的安全性. 这项技术有助于更有效地识别工作场所暴力和沟通失败的趋势.
科学领域:
- 医疗保健安全 医疗保健安全
- 自然语言处理自然语言处理.
- 人工智能在医学中的应用
背景情况:
- 安全事件报告对于患者和工作人员的福祉至关重要.
- 目前的报告系统面临着变化和资源限制的挑战,阻碍及时分析和趋势识别.
- 这些局限性推迟了医疗保健工作场所安全方面的改进.
研究的目的:
- 探索大型语言模型 (LLM) 在自动化安全事件报告叙述的分类中的实用性.
- 评估LLM在识别特定安全事件类别,如工作场所暴力和沟通失败等方面的有效性.
- 为提高工作场所安全的自动标签系统奠定基础.
主要方法:
- 利用大型语言模型来分析安全事件报告的文本.
- 培训和评估LLM以他们将叙事分类到预定义类别的能力.
- 使用特定事件类型的F1分数来测量分类性能.
主要成果:
- 在分类安全事件叙述方面,LLM表现出高准确度.
- 获得了F1的0.80的身体暴力和0.94的口头虐待.
- 在通信失败方面获得了F1的0.94分.
结论:
- 大型语言模型显示了自动安全事件报告分类的重大前景.
- 这项技术可以加快识别关键的安全趋势,包括工作场所暴力和通信问题.
- 通过LLM进行自动标签,最终可以为更安全的医疗环境做出贡献.
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