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Appl Clin Inform 2025; 16(01): 215-222
DOI: 10.1055/a-2447-8463
Research Article

Iterative Development of a Clinical Decision Support Tool to Enhance Naloxone Coprescribing

Richard Wu
1   Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, United States
,
Emily Foster
2   Department of IT and Analytics, Boston Medical Center, Boston, Massachusetts, United States
,
Qiyao Zhang
2   Department of IT and Analytics, Boston Medical Center, Boston, Massachusetts, United States
,
Tim Eynatian
2   Department of IT and Analytics, Boston Medical Center, Boston, Massachusetts, United States
,
Rebecca Mishuris
3   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
4   Digital, Mass General Brigham, Somerville, Massachusetts, United States
,
Nicholas Cordella
5   Department of Medicine, Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, United States
6   Department of Quality and Patient Safety, Boston Medical Center, Boston, Massachusetts, United States
› Author Affiliations Funding None.
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Abstract

Background Opioid overdoses have contributed significantly to mortality in the United States. Despite long-standing recommendations from the Centers for Disease Control and Prevention to coprescribe naloxone for patients receiving opioids who are at high risk of overdose, compliance with these guidelines has remained low.

Objectives The objective of this study was to develop and evaluate a hospital-wide electronic health record (EHR)-based clinical decision support (CDS) tool designed to promote naloxone coprescription for high-risk opioids.

Methods We employed an iterative approach to develop a point-of-order, interruptive EHR alert as the primary intervention and assessed naloxone prescription rates, EHR efficiency metrics, and barriers to adoption. Data were obtained from our EHR's clinical data warehouse and analyzed using statistical process control with odds ratios calculated to quantify statistically significant differences in prescribing rates during the intervention periods.

Results The initial implementation phase of the intervention, spanning from April 2019 to May 2022, yielded a nearly 3-fold increase in the proportion of high-risk patients receiving naloxone, rising from 13.4% (95% confidence interval [CI], 12.9–13.8%) to 36.4% (95% CI, 35.2–37.5%; p = 10−38). Enhancements to the CDS design and logic during the subsequent iteration's study period, June 2022 and December 2023, reduced the number of CDS triggers by more than 30-fold while simultaneously driving an additional increase in naloxone receipt to 42.7% (95% CI, 40.6–44.8%; p = 2 × 10−5). The efficiency of the CDS demonstrated marked improvement, with prescribers accepting the naloxone coprescription recommendation provided by the CDS in 41.1% of the encounters in version 2, compared with 6.2% in version 1 (p = 6 × 10−9).

Conclusion This study offers a sustainable and scalable model to address low rates of naloxone coprescription and may also be used to target other opportunities for improving guideline-concordant prescribing practices.

Keywords

continuous quality improvement - clinical decision support - PDSA - patient safety

Protection of Human and Animal Subjects

Human and animal subjects were not utilized in this study. This study was determined to be quality improvement and was exempt from Institutional Review Board review.




Publication History

Received: 21 June 2024

Accepted: 22 October 2024

Accepted Manuscript online:
25 October 2024

Article published online:
05 March 2025

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