Conditional outlier detection for clinical alerting

Abstract

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.

AMIA 2010 Annual Symposium proceedings, pp. 286-290. Homer R. Warner Best Paper Award

References in corpus (2)

• Evidence-based anomaly detection in clinical domains
• Conditional anomaly detection methods for patient-management alert systems

Cited by in corpus (5)

• Outlier detection for patient monitoring and alerting
• Conditional anomaly detection with soft harmonic functions
• Robust Contextual Outlier Detection: Where Context Meets Sparsity
• Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
• Conditional anomaly detection using soft harmonic functions: An application to clinical alerting