Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics. (19th October 2021)

• Record Type: Journal Article
• Authors: Orenstein, Evan W; Kandaswamy, Swaminathan; Muthu, Naveen; Chaparro, Juan D; Hagedorn, Philip A; Dziorny, Adam C; Moses, Adam; Hernandez, Sean; Khan, Amina; Huth, Hannah B; Beus, Jonathan M; Kirkendall, Eric S
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 12(2021)
• Page Start: 2654
• Page End: 2660
• Publication Date: 2021-10-19
• Subjects: electronic health records -- decision support systems -- clinical burnout -- professional -- benchmarking -- alert fatigue -- health personnel; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab179
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Background: Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden. Objective: (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics. Materials and Methods: We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016–2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric. Results: Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden. Conclusion: Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.