Improving clinical decision support reliability using anomaly detection methods

描述（由申请人提供）：临床决策支持（CDS）工具旨在帮助医疗保健提供者做出更好的决策。越来越多的证据表明，CDS，有效使用时，可以提高医疗质量，安全性和有效性（2-7）。事实上，电子健康记录（EHR）相对于基于纸张的医疗生态系统改善患者护理的承诺的很大一部分取决于CDS。当CDS工作良好时，医疗服务提供者可以依赖它。然而，我们已经确定了CDS干预发生故障的一些情况，并且停止提供正确的警报或开始提供不正确的警报。在许多情况下，这些故障持续很长一段时间，在某些情况下，它们导致患者伤害。 我们提出了一种新的异常检测为基础的方法来识别CDS系统中的故障，使他们可以被纠正。这种方法已经在其他行业中用于识别与预期行为的偏差，例如信用卡欺诈或计算机网络入侵检测，但以前从未应用于CDS功能和故障的问题。 在初步工作中，我们已经表明，即使是简单的异常检测为基础的方法可以识别许多CDS故障。在拟议的项目中，我们将扩展这些方法，以提高其灵敏度和特异性，并验证他们在三个网站：布里格姆妇女医院，俄亥俄州州立大学医学中心和得克萨斯大学。我们还将对CDS故障进行定性评估和根本原因分析，并开发一个开源模块化仪表板和警报系统来跟踪它们。 我们的项目有三个目标：1）清点三个医疗中心发生的CDS故障和问题，并进行根本原因分析，以识别原因、指标和潜在解决方案，2）开发和验证可推广的异常检测方法，以识别CDS故障，3）创建、实施和测试有用且可推广的CDS仪表板和警报系统，以实时监控CDS异常。

DESCRIPTION (provided by applicant): Clinical decision support (CDS) tools are designed to help healthcare providers make better decisions. Significant and mounting evidence suggests that CDS, when used effectively, can improve health care quality, safety, and effectiveness (2-7). Indeed, a good part of the promise of the electronic health record (EHR) to improve patient care relative to a paper-based healthcare ecosystem rests upon CDS. When CDS works well, healthcare providers can come to depend on it. However, we have identified a number of instances where CDS interventions malfunctioned and either stopped providing correct alerts or began providing incorrect alerts. In many cases, these malfunctions persisted for a long period of time and in some cases, they led to patient harm. We propose a novel anomaly-detection-based method for identifying malfunctions in CDS systems so that they can be corrected. Such approaches have been used in other industries to identify deviations from expected behavior, such as credit card fraud or computer network intrusion detection, but never previously applied to the problem of CDS function and failure. In preliminary work, we have shown that even simple anomaly-detection-based approaches can identify many CDS malfunctions. In the proposed project, we will extend these methods to improve their sensitivity and specificity and validate them at three sites: Brigham and Women's Hospital, The Ohio State University Medical Center and the University of Texas. We will also conduct a qualitative assessment and root cause analyses of CDS malfunctions and develop an open-source modular dashboard and alerting system for tracking them. Our project has three aims: 1) to inventory CDS failures and issues that have occurred in three medical centers and conduct root cause analyses to identify causes, indicators, and potential solutions, 2) to develop and validate generalizable anomaly detection approaches to identifying CDS failures and 3) to create, implement, and test a useful and generalizable CDS dashboard and alert system for real-time monitoring for CDS anomalies.