Outlier detection for patient monitoring and alerting
For clinicians and patient safety, this work proposes a data-driven alerting system to catch potential errors in post-cardiac surgical care, though it is an incremental application of existing outlier detection methods.
The paper develops an outlier detection method to identify unusual patient-management decisions in EHRs, aiming to flag potential errors. Evaluation on 4486 post-cardiac surgical patients showed true alert rates of 25-66%, with 66% for the strongest outliers.
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.