A Potential Answer to the Alert Override Riddle: Using Patient Attributes to Predict False Positive Alerts

Electronic health records (EHRs) use alerts to help prevent medical errors, yet clinicians override many of these alerts due to desensitization from constant exposure (alert fatigue). We hypothesize that a clinician might override an alert warning about the dangers of a treatment if the patient's health is so poor that the treatment is worth the risk or if a patient's health suggests the treatment is not needed. We used logistic regression with general estimating equations to determine if the Early Warning Score (EWS), a measurement used to predict critical care need, could be used to predict alert overrides. EWS was a significant predictor of overrides for three alerts. Although EWS could not predict overrides for all alert rules, these results suggest that EWS may be helpful for some alerts, but that additional EHR data will be needed for predicting override behavior to a useful degree.