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Improving Patient Safety through Medical Alert Management: An Automated Decision Tool to Reduce Alert Fatigue

机译:通过医疗警报管理提高患者安全性:减少警报疲劳的自动化决策工具

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摘要

Drug safety alerts, a feature of electronic medical records (EMRs), are increasingly recognized as valuable tools for reducing adverse drug events and improving patient safety. However, there has also been increased understanding that alert fatigue, a state in which users become overwhelmed and unresponsive to alerts in general, is a threat to patient safety. In this paper, we seek to mitigate alert fatigue by filtering superfluous alerts. We design a method of predicting alert overrides based on past alert override rate, range in override rate, and sample size. Using a dataset from a large pediatric network, we retroactively test and validate our method. For the test implementation, alerts are filtered with 91–96% accuracy, depending on the parameter values selected. By filtering these alerts, we reduce alert fatigue and allow users to refocus resources to potentially vital alerts, reducing the occurrence of adverse drug events.
机译:药物安全警报是电子病历(EMR)的功能,逐渐被认为是减少不良药物事件和提高患者安全性的有价值的工具。但是,人们也越来越认识到,警报疲劳是一种对患者安全的威胁,警报疲劳通常是用户不知所措且对警报没有反应的状态。在本文中,我们试图通过过滤多余的警报来减轻警报疲劳。我们根据过去的警报覆盖率,覆盖率范围和样本大小设计了一种预测警报覆盖的方法。使用来自大型儿科网络的数据集,我们可以追溯测试和验证我们的方法。对于测试实施,警报的过滤精度为91–96%,具体取决于所选的参数值。通过过滤这些警报,我们减少了警报疲劳,并允许用户将资源重新聚焦于潜在的重要警报,从而减少了药物不良事件的发生。

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