从使用自然语言处理的临床笔记中识别II类工作场所暴力
Ha Do Byon1, Catherine Harris2, Mary Crandall1,2
1University of Virginia School of Nursing.
Workplace health & safety
|June 30, 2023
概括
家庭医疗护士在工作场所经历了严重的暴力,往往没有报告. 自然语言处理 (NLP) 在每1万次访问中发现了4起事件,远远超过官方报告,突出NLP.
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.
- 工作场所暴力工作场所暴力
背景情况:
- 患者对家庭医疗保健护士的II型工作场所暴力是一个关键的健康和安全问题.
- 许多暴力事件没有通过官方道报告.
- 自然语言处理 (NLP) 提供了一种方法来从临床笔记中识别未被报告的暴力行为.
研究的目的:
- 计算家庭医疗护士经历的II型工作场所暴力的12个月流行率.
- 开发和实施NLP系统,用于在临床笔记中检测未报告的工作场所暴力.
主要方法:
- 分析了来自美国两家家庭医疗机构的近60万份临床访问笔记 (2019年1月1日 - 2019年12月31日).
- 应用基于规则和机器学习的NLP算法来识别工作场所暴力的描述.
- 将NLP发现与官方事件报告数据进行比较.
主要成果:
- NLP确定了236份临床笔记,详细说明了针对家庭医疗护士的II型工作场所暴力.
- 每10,000次家庭访问的流行率:身体暴力 (0.067),非身体暴力 (3.76),任何暴力 (4.0).
- 在研究期间,官方事件报告记录了零次II型工作场所暴力事件.
结论:
- 通过分析临床记录,NLP有效地揭示了工作场所暴力的"隐藏病例".
- NLP可以补充正式报告系统,提供更准确的暴力风险图片.
- 使用NLP可以帮助医疗管理人员和临床医生提高实践环境中的安全性.
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