Reducing Interruptive Alert Burden Using Quality Improvement Methodology

Appl Clin Inform. 2020 Jan;11(1):46-58. doi: 10.1055/s-0039-3402757. Epub 2020 Jan 15.


Background: Increased adoption of electronic health records (EHR) with integrated clinical decision support (CDS) systems has reduced some sources of error but has led to unintended consequences including alert fatigue. The "pop-up" or interruptive alert is often employed as it requires providers to acknowledge receipt of an alert by taking an action despite the potential negative effects of workflow interruption. We noted a persistent upward trend of interruptive alerts at our institution and increasing requests for new interruptive alerts.

Objectives: Using Institute for Healthcare Improvement (IHI) quality improvement (QI) methodology, the primary objective was to reduce the total volume of interruptive alerts received by providers.

Methods: We created an interactive dashboard for baseline alert data and to monitor frequency and outcomes of alerts as well as to prioritize interventions. A key driver diagram was developed with a specific aim to decrease the number of interruptive alerts from a baseline of 7,250 to 4,700 per week (35%) over 6 months. Interventions focused on the following key drivers: appropriate alert display within workflow, clear alert content, alert governance and standardization, user feedback regarding overrides, and respect for user knowledge.

Results: A total of 25 unique alerts accounted for 90% of the total interruptive alert volume. By focusing on these 25 alerts, we reduced interruptive alerts from 7,250 to 4,400 per week.

Conclusion: Systematic and structured improvements to interruptive alerts can lead to overall reduced interruptive alert burden. Using QI methods to prioritize our interventions allowed us to maximize our impact. Further evaluation should be done on the effects of reduced interruptive alerts on patient care outcomes, usability heuristics on cognitive burden, and direct feedback mechanisms on alert utility.

MeSH terms

  • Clinical Governance
  • Feedback
  • Heuristics
  • Humans
  • Internet
  • Medical Order Entry Systems / standards*
  • Nurse Practitioners
  • Physicians
  • Quality Improvement*
  • Tobacco Products