Authors:
Milos Hauskrecht, PhD, Michal Valko, MSc, Iyad Batal, MSc, Gilles Clermont,
MD, MS, Shyam Visweswaran MD, PhD, Gregory F. Cooper, MD, PhD Computer Science Department, Department of Critical Care Medicine, Department of
Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, US
Summary:
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.
Source:
Proceedings of the Fall Symposium of the American Medical Informatics Association; Nov 2010