Reducing Interruptive Alert Burden Using Quality Improvement Methodology

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.

Fig. 1

Requests for interruptive Best Practice Advisories per year.

Fig. 2

Weekly volume of interruptive Best Practice Advisories shown to providers (defined as physicians, residents, fellows, nurse practitioners, and physician assistants) and total inpatient days as a marker of overall hospital volume with linear trendlines in the 16 months preceding our quality improvement project. This volume is not controlled per provider, but a global indicator of alert volume.

Fig. 3

Key driver diagram. We established a focused goal for our QI project of reducing the number of interruptive alerts seen by providers by 35% within 6 months and sustaining this reduction for at least 3 months. Using the information from the Ishikawa exercise, we identified these five key drivers and designed several interventions to effect change in those areas. QI, quality improvement.

Fig. 4

Adaptation of Nielsen's web usability heuristics to alert design.

Fig. 5

Pareto Chart. This chart, created using cumulative alert information from the 16 months preceding our QI project as seen in Fig. 2 , shows the individual number of firings for each interruptive alert as well as the cumulative total sorted from highest volume to lowest volume alerts. QI, quality improvement.

Fig. 6

Stylized depictions of initial version of alert before (above) and after (below) usability changes.

Fig. 7

Control chart of total interruptive BPAs seen by providers per week. BPA, best practice advisory.

Fig. 8

Control chart of interruptive BPAs seen by providers per 1,000 hours logged in as measured using log-in and log-out times. BPA, best practice advisory.

Fig. 9

Admission order balancing metric: Arrow A denotes the initial revision of the visual appearance of the alert. Arrow B denotes changes in alert restrictions.

Fig. 10

Tobacco history alert balancing metric. This alert was turned completely off at week 18 of 2017 (arrow). There was no significant change in the percentage of patients 13 years or older with tobacco history reviewed.