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Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
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An Incident or Occurrence Report in a healthcare setting is a crucial document used to record any unexpected occurrence that may or may not have affected a patient, employee, or visitor. Such reports are critical to improving patient safety and include all details leading up to and including the event.
Purposes:
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使用自然语言处理监测与医疗保健相关的暴力行为

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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.