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Appl Clin Inform 2023; 14(03): 470-477
DOI: 10.1055/a-2068-6940
Research Article

Pseudorandomized Testing of a Discharge Medication Alert to Reduce Free-Text Prescribing

Naveed Rabbani
1   Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
,
Milan Ho
2   Department of Pediatrics, University of Texas Southwestern Medical School, Dallas, Texas, United States
,
Debadutta Dash
3   Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, United States
,
Tyler Calway
1   Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
,
Keith Morse
1   Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
4   Division of Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
,
Whitney Chadwick
1   Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
4   Division of Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
› Author Affiliations Funding None.
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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 improvement

Protection 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.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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