Outlier detection for patient monitoring and alerting

ArXi:2605.08955v1 Announce Type: new We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases d in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data.