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。越来越多的重要证据表明，如果有效使用CDS，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保健生态系统改善患者护理的承诺，很大一部分依赖于CDS。当CDS运作良好时，医疗保健提供者可以依赖它。但是，我们已经确定了许多CDS干预发生故障的实例，这些实例要么停止提供正确的警报，要么开始提供错误的警报。在许多情况下，这些故障持续了很长一段时间，在某些情况下，它们导致了病人的伤害。

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.