Conditional outlier detection for clinical alerting

ArXi:2605.05124v1 Announce Type: new We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases d 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.