Feedback at the Point of Care to Decrease Medication Alert Rates in an Electronic Health Record. Issue 7 (July 2020)

• Record Type: Journal Article
• Authors: van Camp, P. J.; Kirkendall, Eric S.; Hagedorn, Philip A.; Minich, Thomas; Kouril, Michal; Spooner, S. Andrew; Gecili, Emrah; Dexheimer, Judith W.
• Other Names: Harper Marvin B. section editor.; Hoffman Jeffrey M. section editor.; Zorc Joseph J. section editor.
• Is Part Of: Pediatric emergency care. Volume 36:Issue 7(2020)
• Publication Date: 2020-07
• Subjects: alert fatigue -- clinical decision support systems -- drug prescriptions -- electronic health records -- health personnel; Pediatric emergencies -- Periodicals; 618.92002505
• DOI: 10.1097/PEC.0000000000001847
• Languages: English
• ISSNs: 0749-5161

Abstract

Objective: Frequently overridden alerts in the electronic health record can highlight alerts that may need revision. This method is a way of fine-tuning clinical decision support. We evaluated the feasibility of a complementary, yet different method that directly involved pediatric emergency department (PED) providers in identifying additional medication alerts that were potentially incorrect or intrusive. We then evaluated the effect subsequent resulting modifications had on alert salience. Methods: We performed a prospective, interventional study over 34 months (March 6, 2014, to December 31, 2016) in the PED. We implemented a passive alert feedback mechanism by enhancing the native electronic health record functionality on alert reviews. End-users flagged potentially incorrect/bothersome alerts for review by the study's team. The alerts were updated when clinically appropriate and trends of the impact were evaluated. Results: More than 200 alerts were reported from both inside and outside the PED, suggesting an intuitive approach. On average, we processed 4 reviews per week from the PED, with attending physicians as major contributors. The general trend of the impact of these changes seems favorable. Discussion: The implementation of the review mechanism for user-selected alerts was intuitive and sustainable and seems to be able to detect alerts that are bothersome to the end-users. The method should be run in parallel with the traditional data-driven approach to support capturing of inaccurate alerts. Conclusions: User-centered, context-specific alert feedback can be used for selecting suboptimal, interruptive medication alerts.