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采用数据驱动的贝叶斯信念网络调查影响患者安全的组织因素。

Adoption of a Data-Driven Bayesian Belief Network Investigating Organizational Factors that Influence Patient Safety.

机构信息

Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.

School of Management, University College London, London, E14 5AA, UK.

出版信息

Risk Anal. 2022 Jun;42(6):1277-1293. doi: 10.1111/risa.13610. Epub 2020 Oct 18.

Abstract

Medical errors pose high risks to patients. Several organizational factors may impact the high rate of medical errors in complex and dynamic healthcare systems. However, limited research is available regarding probabilistic interdependencies between the organizational factors and patient safety errors. To explore this, we adopt a data-driven Bayesian Belief Network (BBN) model to represent a class of probabilistic models, using the hospital-level aggregate survey data from U.K. hospitals. Leveraging the use of probabilistic dependence models and visual features in the BBN model, the results shed new light on relationships existing among eight organizational factors and patient safety errors. With the high prediction capability, the data-driven approach results suggest that "health and well-being" and "bullying and harassment in the work environment" are the two leading factors influencing the number of reported errors and near misses affecting patient safety. This study provides significant insights to understand organizational factors' role and their relative importance in supporting decision-making and safety improvements.

摘要

医疗差错会对患者构成高风险。在复杂和动态的医疗保健系统中,有几个组织因素可能会影响医疗差错的高发生率。然而,关于组织因素与患者安全错误之间的概率相互依存关系的研究有限。为了探讨这一点,我们采用数据驱动的贝叶斯信念网络(BBN)模型来表示一类概率模型,使用来自英国医院的医院级综合调查数据。利用 BBN 模型中的概率依赖模型和可视化特征,结果揭示了八个组织因素与患者安全错误之间存在的关系。具有高预测能力的数据驱动方法结果表明,“健康与福祉”和“工作环境中的欺凌和骚扰”是影响报告错误数量和影响患者安全的近因数量的两个主要因素。这项研究提供了重要的见解,以了解组织因素的作用及其在支持决策和安全改进方面的相对重要性。

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