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.

译文

我们在特定的I级创伤中心分析了创伤分类系统,以评估过度和不足分类的发生率,并支持有关系统改进的建议。分类过程旨在估计患者伤害的严重程度并相应地分配资源,其中潜在的高估错误 (overtriage) 消耗了多余的资源,而低估 (undertriage) 则可能导致医疗错误。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赖性。我们采访了六位经验丰富的创伤外科医生,以获取他们对创伤中心发生的过度和漏诊率的专家意见。然后,我们评估了在同一中心六周内收集的86例创伤病例的随机样本中的实际分流率和分流不足率。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定量结合,以获得后验分布。结果分别是16.1和4.9% 患者的超分流和不足分流的估计值。这种贝叶斯方法可以使用案例数据和专家意见对错误率进行定量评估,从而提供了一种合理的方法来获得系统性能的最佳估计。我们在本文中描述的总体方法可以更广泛地用于分析复杂的医疗保健提供系统,目的是减少错误,患者风险和超额成本。

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