Abstract
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.
Keywords
clinical information systems - decision support systems - clinical alert fatigue -
health personnel - quality improvement - electronic health records - human factors
