使用自然语言处理监测与医疗保健相关的暴力行为
Mark Waltzman1,2, Al Ozonoff1,2, Kerri Ann Fournier1
1Boston Children's Hospital, Boston, Massachusetts.
自然语言处理 (NLP) 可以通过分析护理笔记来识别报告不足的医疗保健相关暴力 (HAV). 这种方法提高了报告的完整性,即使在医院的IT资源有限.
科学领域:
- 医疗保健管理的管理
- 临床信息学是一种临床信息学.
- 患者安全 患者安全
背景情况:
- 医疗保健相关暴力 (HAV) 正在增加,由患者和家庭爆发驱动.
- HAV发作往往没有得到报道,这阻碍了有效的干预.
- 行为健康危机加剧了这些报告挑战.
研究的目的:
- 评估使用护理笔记来识别报告不足的HAV病例的可行性.
- 评估自然语言处理 (NLP) 在检测HAV.在检测的有效性.
- 提高医疗保健机构HAV报告的完整性.
主要方法:
- 来自两个医院的护理笔记被提取用于分析.
- 采用了自然语言处理 (NLP) 辅助的手动审查过程.
- 通过从儿科三级护理中心的数据进行NLP模型的训练,并通过社区医院的数据进行验证.
主要成果:
- 在社区医院中,NLP模型在识别HAV发作时实现了96.8%的灵敏度和47.1%的特异性.
- 确定了31个HAV事件,其中26个被认为是可报告的.
- 在26个报告案件中,只有7个最初是自我报告的,这凸显了严重的报告不足.
结论:
- NLP辅助审查是一种可行和有效的方法,用于监测报告不足的HAV.
- 这种方法可以提高HAV报告的完整性.
- 这种方法甚至可以在资源有限的医院环境中实现.
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