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一级创伤中心过度分诊和分诊不足的贝叶斯评估。

Bayesian assessment of overtriage and undertriage at a level I trauma centre.

作者信息

DiDomenico Paul B, Pietzsch Jan B, Paté-Cornell M Elisabeth

机构信息

Department of Management Science and Engineering, Terman Engineering Center, Stanford University, 380 Panama Way, Stanford, CA 94305-4026, USA.

出版信息

Philos Trans A Math Phys Eng Sci. 2008 Jul 13;366(1874):2265-77. doi: 10.1098/rsta.2008.0036.

Abstract

We analysed the trauma triage system at a specific level I trauma centre to assess rates of over- and undertriage and to support recommendations for system improvements. The triage process is designed to estimate the severity of patient injury and allocate resources accordingly, with potential errors of overestimation (overtriage) consuming excess resources and underestimation (undertriage) potentially leading to medical errors.We first modelled the overall trauma system using risk analysis methods to understand interdependencies among the actions of the participants. We interviewed six experienced trauma surgeons to obtain their expert opinion of the over- and undertriage rates occurring in the trauma centre. We then assessed actual over- and undertriage rates in a random sample of 86 trauma cases collected over a six-week period at the same centre. We employed Bayesian analysis to quantitatively combine the data with the prior probabilities derived from expert opinion in order to obtain posterior distributions. The results were estimates of overtriage and undertriage in 16.1 and 4.9% of patients, respectively. This Bayesian approach, which provides a quantitative assessment of the error rates using both case data and expert opinion, provides a rational means of obtaining a best estimate of the system's performance. The overall approach that we describe in this paper can be employed more widely to analyse complex health care delivery systems, with the objective of reduced errors, patient risk and excess costs.

摘要

我们分析了某一级创伤中心的创伤分诊系统,以评估过度分诊和分诊不足的发生率,并为系统改进提供建议。分诊流程旨在评估患者损伤的严重程度,并据此分配资源,高估(过度分诊)可能会消耗过多资源,而低估(分诊不足)则可能导致医疗失误。我们首先使用风险分析方法对整个创伤系统进行建模,以了解参与者行为之间的相互依存关系。我们采访了六位经验丰富的创伤外科医生,以获取他们对该创伤中心过度分诊和分诊不足发生率的专业意见。然后,我们评估了在同一中心为期六周收集的86例创伤病例随机样本中的实际过度分诊和分诊不足发生率。我们采用贝叶斯分析将数据与从专家意见得出的先验概率进行定量结合,以获得后验分布。结果显示,分别有16.1%和4.9%的患者被过度分诊和分诊不足。这种贝叶斯方法利用病例数据和专家意见对错误率进行定量评估,为获得系统性能的最佳估计提供了一种合理手段。我们在本文中描述的整体方法可更广泛地用于分析复杂的医疗保健提供系统,目标是减少失误、患者风险和额外成本。

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