基于系统理论的事故分析技术应用于COVID-19大流行期间的外科安全报告
Aubrey L Samost-Williams1, Robert D Sinyard2, Leo L Tabayoyong3
1Department of Anesthesiology, Critical Care, and Pain Medicine, McGovern Medical School, University of Texas Health Science Center in Houston, Houston, TX.
Journal of patient safety
|June 5, 2025
概括
这项研究使用基于系统理论 (CAST) 的因果分析和专题分析来识别导致外科手术期间安全事件的系统级风险,为未来的危机提供缓解策略.
科学领域:
- 医疗保健安全科学 医疗保健安全科学
- 医学中的系统工程.
- 定性研究方法 定性研究方法
背景情况:
- 非线性回顾分析技术提供了对事故原因的深入洞察,但在医疗保健中未得到充分利用.
- 了解上下文因素对于预防外科手术期间的安全事件至关重要.
研究的目的:
- 展示基于系统理论 (CAST) 的因果分析与感应主题分析相结合的应用.
- 识别导致医院环境中术后安全事件的情境因素.
主要方法:
- 开发了周围操作系统的层次控制结构.
- 使用CAST分析了COVID激增期间自我报告的安全事件.
- 应用了感应性定性主题编码来识别系统级安全风险,并将其映射到建议中.
主要成果:
- 分析了122份报告中的19个安全事件,揭示了245个促成因素.
- 确定了三个主要的贡献因素主题:脆弱的流程,人员挑战和设备问题.
- 每个子主题都与特定的预防策略相关,例如建立中央协议枢纽.
结论:
- 非线性事故分析和专题分析有效地确定了导致安全事件的全系统因素.
- 调查结果提供了在未来的流行病或危机期间减轻风险的建议,这些危机与资源稀缺和快速环境变化有关.
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