人类错误分析和台湾药物相关不良事件的建模使用人类因素分析和分类系统和后勤回归
Shu-Huan Ko1, Min-Chih Hsieh2, Run-Feng Huang2
1Department of Marketing and Logistics Management, Vanung University, Taoyuan 320313, Taiwan.
Healthcare (Basel, Switzerland)
|July 29, 2023
概括
这项研究确定了导致药物错误的关键因素,如决策错误和组织问题. 开发的模型有助于预测和预防不良药物事件,提高患者的安全性.
科学领域:
- 医疗保健的质量和安全
- 人类因素工程 人类因素工程
- 药物监督 药物监督 药物监督
背景情况:
- 医疗不良事件,特别是与药物相关的不良事件,在医疗保健中构成重大风险.
- 对药物错误因素的全面分析仍然有限,阻碍了有效的预防策略.
研究的目的:
- 使用人类因素分析和分类系统 (HFACS) 识别与药物相关的不良事件中的关键错误因素.
- 开发物流回归模型来计算与医疗保健系统缺陷相关的不良事件的概率.
主要方法:
- 由七名经验丰富的医疗保健专业人员 (护士和药剂师) 分析了37种与药物相关的不良事件.
- 应用人类因素分析和分类系统 (HFACS) 来分类错误因素.
- 开发七种使用逻辑回归来预测事件概率的错误模型.
主要成果:
- 决策错误,身体/心理限制,未能纠正问题和组织流程被确定为HFACS各级的主要贡献因素.
- 建立了七种不同的错误模型 (发生和分析途径).
- 计算出故障因子发生的相对概率.
结论:
- 开发的错误模型为医疗保健专业人员提供了一种新的分析方法.
- 这些模型可以帮助改善和预防不良药物事件,最终提高患者的安全性.
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