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DOI: 10.1055/a-2068-6940
Pseudorandomized Testing of a Discharge Medication Alert to Reduce Free-Text Prescribing
Funding None.![](https://www.thieme-connect.de/media/10.1055-s-00035026/202303/lookinside/thumbnails/10-1055-a-2068-6940_202211ra0300-1.jpg)
Abstract
Background Pseudorandomized testing can be applied to perform rigorous yet practical evaluations of clinical decision support tools. We apply this methodology to an interruptive alert aimed at reducing free-text prescriptions. Using free-text instead of structured computerized provider order entry elements can cause medication errors and inequity in care by bypassing medication-based clinical decision support tools and hindering automated translation of prescription instructions.
Objective The objective of this study is to evaluate the effectiveness of an interruptive alert at reducing free-text prescriptions via pseudorandomized testing using native electronic health records (EHR) functionality.
Methods Two versions of an EHR alert triggered when a provider attempted to sign a discharge free-text prescription. The visible version displayed an interruptive alert to the user, and a silent version triggered in the background, serving as a control. Providers were assigned to the visible and silent arms based on even/odd EHR provider IDs. The proportion of encounters with a free-text prescription was calculated across the groups. Alert trigger rates were compared in process control charts. Free-text prescriptions were analyzed to identify prescribing patterns.
Results Over the 28-week study period, 143 providers triggered 695 alerts (345 visible and 350 silent). The proportions of encounters with free-text prescriptions were 83% (266/320) and 90% (273/303) in the intervention and control groups, respectively (p = 0.01). For the active alert, median time to action was 31 seconds. Alert trigger rates between groups were similar over time. Ibuprofen, oxycodone, steroid tapers, and oncology-related prescriptions accounted for most free-text prescriptions. A majority of these prescriptions originated from user preference lists.
Conclusion An interruptive alert was associated with a modest reduction in free-text prescriptions. Furthermore, the majority of these prescriptions could have been reproduced using structured order entry fields. Targeting user preference lists shows promise for future intervention.
Keywords
clinical decision support - medication safety - randomized controlled trials - pediatrics - quality improvementProtection of Human and Animal Subjects
The preceding work was performed as part of a quality improvement effort at our institution and does not qualify as human subjects research.
Publication History
Received: 11 November 2022
Accepted: 03 April 2023
Accepted Manuscript online:
04 April 2023
Article published online:
14 June 2023
© 2023. Thieme. All rights reserved.
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