We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) system. Our hypothesis is that patient-management actions that are unusual with respect to past patients may be due to a potential error and that it is worthwhile to raise an alert if such a condition is encountered. We evaluate this hypothesis using data obtained from the electronic health records of 4,486 post-cardiac surgical patients. We base the evaluation on the opinions of a panel of experts. The results support that anomaly-based alerting can have reasonably low false alert rates and that stronger anomalies are correlated with higher alert rates.

翻译：我们开发并评估了一种数据驱动方法，用于利用电子健康记录（EHR）系统中存储的既往病例，检测异常（反常）的患者管理行为。我们的假设是：与既往病例相比异常的患者管理行为可能源于潜在错误，且当遇到此类情况时发出告警具有临床价值。我们基于4,486例心脏术后患者的电子健康记录数据对该假设进行验证，并以专家小组意见作为评估基准。结果表明，基于异常检测的告警机制可保持合理较低的错误告警率，且异常程度越强与告警准确率的关联性越高。