Better Decisions. Better Health for Kids.

Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics

Evan W Orenstein 1 2, Swaminathan Kandaswamy, Naveen Muthu 3 4, Juan D Chaparro 5 6, Philip A Hagedorn 7 8, Adam C Dziorny 9 10, Adam Moses 11, Sean Hernandez 11 12, Amina Khan 4, Hannah B Huth 11, Jonathan M Beus 3 4, Eric S Kirkendall 11 13


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.

Keywords: alert fatigue; benchmarking; clinical burnout; decision support systems; electronic health records; health personnel; professional.



Full Text


  1. Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA.
  2. Division of Hospital Medicine, Children’s Healthcare of Atlanta, Atlanta, Georgia, USA.
  3. Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
  4. Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  5. Division of Clinical Informatics, Nationwide Children’s Hospital, Columbus, Ohio, USA.
  6. Department of Pediatrics, The Ohio State University, Columbus, Ohio, USA.
  7. Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, USA.
  8. Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA.
  9. Department of Pediatrics, University of Rochester School of Medicine, Rochester, New York, USA.
  10. Division of Critical Care Medicine, Golisano Children’s Hospital at Strong, Rochester, New York, USA.
  11. Center for Healthcare Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
  12. Department of General Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
  13. Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.