预测还是预防? 护士与电子早期预警系统的互动,以防跌倒风险
Joint Commission journal on quality and patient safety
|September 13, 2025
概括
机器学习在医院中的跌倒警报是常见的,但护士经常忽视. 大多数患者跌倒发生在初级护士没有看到警报的情况下,这表明需要优化工作流程.
科学领域:
- 护理信息学 在护理信息学.
- 临床决策支持系统临床决策支持系统
- 机器学习在医疗保健中的应用
背景情况:
- 机器学习 (ML) 模型越来越多地被整合到电子健康记录中,用于风险评估和不良结果预测.
- 关于ML技术如何影响护理工作流程,护士行为和患者结果的研究有限.
研究的目的:
- 探索护士与ML生成的跌倒警报的互动.
- 检查警报和患者在护理工作流程中的关系.
主要方法:
- 追溯分析了四个医疗/外科单位的数据.
- 从预测性跌倒模型中检查护士与中断警报的互动.
- 警报与实际患者跌倒的时间相关性.
主要成果:
- 87.0%的入院产生了至少一个下降警报;1.5%导致了下降.
- 大多数警报 (57.3%) 都是被偷偷看的;22.0%的警报是由非初级护士看到的.
- 89.3%的跌倒是任何工作人员看到警报之前,但只有38.7%的初级护士.
结论:
- 主要护士往往没有接触到患者跌倒之前的警报.
- 通过"Snooze to Review"来驳回警报是常见的.
- 需要进一步的研究来优化用于护理工作流程和患者安全的ML技术.
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