Inoue Kayoko, Koizumi Akio
Department Health and Environmental Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Risk Anal. 2004 Dec;24(6):1459-73. doi: 10.1111/j.0272-4332.2004.00542.x.
Adverse events in hospitals, such as in surgery, anesthesia, radiology, intensive care, internal medicine, and pharmacy, are of worldwide concern and it is important, therefore, to learn from such incidents. There are currently no appropriate tools based on state-of-the art models available for the analysis of large bodies of medical incident reports. In this study, a new model was developed to facilitate medical error analysis in combination with quantitative risk assessment. This model enables detection of the organizational factors that underlie medical errors, and the expedition of decision making in terms of necessary action. Furthermore, it determines medical tasks as module practices and uses a unique coding system to describe incidents. This coding system has seven vectors for error classification: patient category, working shift, module practice, linkage chain (error type, direct threat, and indirect threat), medication, severity, and potential hazard. Such mathematical formulation permitted us to derive two parameters: error rates for module practices and weights for the aforementioned seven elements. The error rate of each module practice was calculated by dividing the annual number of incident reports of each module practice by the annual number of the corresponding module practice. The weight of a given element was calculated by the summation of incident report error rates for an element of interest. This model was applied specifically to nursing practices in six hospitals over a year; 5,339 incident reports with a total of 63,294,144 module practices conducted were analyzed. Quality assurance (QA) of our model was introduced by checking the records of quantities of practices and reproducibility of analysis of medical incident reports. For both items, QA guaranteed legitimacy of our model. Error rates for all module practices were approximately of the order 10(-4) in all hospitals. Three major organizational factors were found to underlie medical errors: "violation of rules" with a weight of 826 x 10(-4), "failure of labor management" with a weight of 661 x 10(-4), and "defects in the standardization of nursing practices" with a weight of 495 x 10(-4).
医院中的不良事件，如手术、麻醉、放射、重症监护、内科及药学领域的不良事件，受到全球关注，因此，从这些事件中吸取教训很重要。目前，尚无基于先进模型的合适工具可用于分析大量医疗事故报告。在本研究中，开发了一种新模型，以结合定量风险评估促进医疗差错分析。该模型能够检测医疗差错背后的组织因素，并加快就必要行动做出决策的过程。此外，它将医疗任务确定为模块实践，并使用独特的编码系统来描述事件。该编码系统有七个用于差错分类的向量：患者类别、工作班次、模块实践、关联链（差错类型、直接威胁和间接威胁）、用药、严重程度和潜在危害。这种数学公式使我们能够得出两个参数：模块实践的差错率和上述七个要素的权重。每个模块实践的差错率通过将每个模块实践的年度事故报告数量除以相应模块实践的年度数量来计算。给定要素的权重通过对感兴趣要素的事故报告差错率求和来计算。该模型在一年时间里专门应用于六家医院的护理实践；分析了5339份事故报告，共涉及63294144次模块实践。通过检查实践数量记录和医疗事故报告分析的可重复性，引入了我们模型的质量保证（QA）。对于这两项内容，质量保证确保了我们模型的合理性。所有医院中所有模块实践的差错率约为10的-4次方量级。发现有三个主要组织因素构成医疗差错的基础：“违反规则”，权重为826×10的-4次方；“劳动管理失败”，权重为661×10的-4次方；“护理实践标准化缺陷”,权重为495×10的-4次方。
