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Reduced Effectiveness of Interruptive Drug-Drug Interaction Alerts after Conversion to a Commercial Electronic Health Record
Authors:Adam Wright  Skye Aaron  Diane L Seger  Lipika Samal  Gordon D Schiff  David W Bates
Institution:1.Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital and Harvard Medical School,Boston,USA;2.Department of Biomedical Informatics,Harvard Medical School,Boston,USA;3.Information Systems Department,Partners HealthCare,Boston,USA
Abstract:

Background

Drug-drug interaction (DDI) alerts in electronic health records (EHRs) can help prevent adverse drug events, but such alerts are frequently overridden, raising concerns about their clinical usefulness and contribution to alert fatigue.

Objective

To study the effect of conversion to a commercial EHR on DDI alert and acceptance rates.

Design

Two before-and-after studies.

Participants

3277 clinicians who received a DDI alert in the outpatient setting.

Intervention

Introduction of a new, commercial EHR and subsequent adjustment of DDI alerting criteria.

Main Measures

Alert burden and proportion of alerts accepted.

Key Results

Overall interruptive DDI alert burden increased by a factor of 6 from the legacy EHR to the commercial EHR. The acceptance rate for the most severe alerts fell from 100 to 8.4%, and from 29.3 to 7.5% for medium severity alerts (P?<?0.001). After disabling the least severe alerts, total DDI alert burden fell by 50.5%, and acceptance of Tier 1 alerts rose from 9.1 to 12.7% (P?<?0.01).

Conclusions

Changing from a highly tailored DDI alerting system to a more general one as part of an EHR conversion decreased acceptance of DDI alerts and increased alert burden on users. The decrease in acceptance rates cannot be fully explained by differences in the clinical knowledge base, nor can it be fully explained by alert fatigue associated with increased alert burden. Instead, workflow factors probably predominate, including timing of alerts in the prescribing process, lack of differentiation of more and less severe alerts, and features of how users interact with alerts.
Keywords:
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