在多个地点中心的辐射事件学习.
Xing Li1,2, Fan-Chi Su3, Qiongge Li2
1Department of Radiation Oncology, Loyola University Chicago Stritch School of Medicine, Maywood, Illinois, USA.
Journal of applied clinical medical physics
|November 18, 2025
概括
本研究引入了一种混合方法,用于多个地点的医院的事件学习,通过高效的跨站点分析和积极的风险管理来提高患者的安全.
科学领域:
- 医疗保健管理的管理
- 患者安全 患者安全
- 临床信息学 临床信息学
背景情况:
- 多站点医院系统在事件学习方面面临挑战,原因是资源有限和不同的站点文化.
- 整个系统的政策遵守在不同的医院位置往往是不一致的.
- 有效的跨站点学习和积极的风险管理对于提高患者安全至关重要.
研究的目的:
- 引入一种新的,资源高效的混合方法,用于大型,多站点医院系统的事件学习.
- 促进跨站点学习和积极的风险管理,以提高患者安全.
- 在事件报告中应对因资源限制和多样化的现场文化所带来的挑战.
主要方法:
- 将统计和根本原因分析结合在一起的混合方法应用于2023-2024年五个医院地点的安全事件.
- 使用关键词识别故障模式 (FMs),通过词云可视化频率和跨站点相关性.
- 一个卫星诊所使用了一个主动模型来预测和减轻潜在的患者伤害.
主要成果:
- 对228个事件的分析确定了"护理路径"和"轮"作为治疗计划中最常见的失败模式.
- "轮"和"文档"的关键词出现在四个网站上,表明了常见的问题.
- 在一个卫星诊所,主动模型将平均风险优先次数从106降低到77,估计受影响的患者从27降低到20
结论:
- 开发了一种全面的,全系统的混合方法,提高跨多个地点诊所的事件学习效率.
- 该方法促进了对特定站点的安全文化进行基准测试,并使可测量的跨站点学习成为可能.
- 新的词云分析和主动模型改善了根源原因的可视化和全系统对患者安全的准备.
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