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J Biomed Inform. 2013 Feb;46(1):47-55. doi: 10.1016/j.jbi.2012.08.004. Epub 2012 Aug 27.

Outlier detection for patient monitoring and alerting.

Author information

1
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA. milos@pitt.edu

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.

PMID:
22944172
PMCID:
PMC3567774
DOI:
10.1016/j.jbi.2012.08.004
[Indexed for MEDLINE]
Free PMC Article
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