Conditional outlier detection for clinical alerting
Milos Hauskrecht, Michal Valko, Shyam Visweswaran, Iyad Batal, Gilles Clermont + 1 more
TLDR
This paper presents a data-driven method for detecting anomalous patient-management actions in EHRs to alert for potential errors.
Key contributions
- Develops a data-driven method to detect anomalous patient-management actions in EHRs.
- Hypothesizes unusual actions indicate potential errors, triggering clinical alerts.
- Evaluated using EHR data from 4,486 post-cardiac surgical patients.
- Expert evaluation confirms low false alert rates and anomaly-alert correlation.
Why it matters
This paper addresses a critical need for error detection in clinical settings. It offers a proactive approach to improve patient safety by identifying unusual management actions. The findings suggest a viable path for integrating anomaly detection into EHR systems for real-time clinical alerting.
Original 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.
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