Applying Bayesian Changepoint Model and Hierarchical Divisive Model for Detecting Anomalies in Clinical Decision Support Alert Firing

Abstract

Clinical Decision Support (CDS) Systems are widely used to support efficient evidence-based care and have become an important aspect of healthcare. CDS systems are complex, and sometimes malfunction or exhibit anomalous behavior. We have previously shown how anomaly detection models can be used to successfully identify malfunctions in CDS systems. We have extended this work and applied two new anomaly detection models on CDS alert firing data from a large health system.