从模拟过程中识别的潜在安全威胁中学习,以提高患者安全
Joint Commission journal on quality and patient safety
|September 25, 2023
概括
这项研究开发了一种系统方法来识别和分类在医疗保健模拟中发现的潜在安全威胁 (LST). 新方法通过分析LST趋势,可以进行有针对性的改进,以提高患者的安全性.
科学领域:
- 医疗保健安全 医疗保健安全
- 患者安全研究的研究.
- 基于模拟的培训.
背景情况:
- 隐性安全威胁 (LST) 是设计,工艺或环境因素,危及患者安全,通常在模拟过程中确定.
- 目前收集,分析和分类LST的方法尚未标准化或得到充分发展.
- 在医疗保健机构中需要系统的方法来识别和分类LST.
研究的目的:
- 系统地收集在基于模拟的培训中发现的潜在安全威胁 (LST).
- 开发一种标准化的分类法来对LST进行分类.
- 建立一个分析LST数据和推动有针对性的改进的框架.
主要方法:
- 修改了Press Ganey医疗保健绩效改进失败模式分类学 (HPI-FMT) 与系统,个人和药物类别.
- 修订了子类别,以与模拟LST内容保持一致,以获得一致的数据收集.
- 利用数据可视化软件进行分析,生成仪表板和报告,用于全系统共享和趋势识别.
主要成果:
- 在一年的232次模拟中确定了1318个LST (每次模拟5.7个LST).
- 最重要的LST子类别包括环境/供应/设备,过程/结构和知识/技能/习惯.
- 在LST中观察到的模式导致了有针对性的流程改进或培训干预.
结论:
- 成功开发和实施了一种收集,分析和显示LST的系统方法.
- 这种精细的方法有助于识别LST趋势.
- 根据观察到的LST模式,建议针对性地改进过程和培训,以提高患者的安全性.
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