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arXiv · 2605.08955

Outlier detection for patient monitoring and alerting

Miloš Hauskrecht·Iyad Batal·Michal Valko·Shyam Visweswaran·Gregory F. Cooper·Gilles Clermont
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Abstract

We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored 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. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25\% to 66\% for a variety of patient-management actions, with 66\% corresponding to the strongest outliers.

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Miloš Hauskrecht, Iyad Batal, Michal Valko, Shyam Visweswaran, Gregory F. Cooper, Gilles Clermont. 2026-05-09. Outlier detection for patient monitoring and alerting. https://doi.org/10.1016/j.jbi.2012.08.004

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