Wu Richard, Foster Emily, Zhang Qiyao, Eynatian Tim, Mishuris Rebecca, Cordella Nicholas
Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, United States.
Department of IT and Analytics, Boston Medical Center, Boston, Massachusetts, United States.
Appl Clin Inform. 2025 Jan;16(1):215-222. doi: 10.1055/a-2447-8463. Epub 2024 Oct 25.
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.
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.
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.
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%; = 10). 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%; = 2 × 10). 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 ( = 6 × 10).
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.
阿片类药物过量在美国的死亡率中占很大比例。尽管美国疾病控制与预防中心长期建议为有阿片类药物过量高风险的患者同时开具纳洛酮，但这些指南的依从性一直很低。
本研究的目的是开发并评估一种基于全院电子健康记录（EHR）的临床决策支持（CDS）工具，旨在促进为高风险阿片类药物患者同时开具纳洛酮。
我们采用迭代方法开发了一种医嘱下达时的、具有打断功能的EHR警报作为主要干预措施，并评估了纳洛酮处方率、EHR效率指标以及采用过程中的障碍。数据从我们EHR的临床数据仓库中获取，并使用统计过程控制进行分析，计算比值比以量化干预期间处方率的统计学显著差异。
干预的初始实施阶段从2019年4月持续到2022年5月，接受纳洛酮的高风险患者比例增加了近3倍，从13.4%（95%置信区间[CI]，12.9 - 13.8%）升至36.4%（95%CI，35.2 - 37.5%；= 10）。在后续迭代研究期间（2022年6月至2023年12月）对CDS设计和逻辑进行改进后，CDS触发次数减少了30多倍，同时纳洛酮的接受率进一步提高至42.7%（95%CI，40.6 - 44.8%；= 2×10）。CDS的效率有显著提高，在第2版中，41.1%的会诊中开处方者接受了CDS提供的纳洛酮联合处方建议，而在第1版中这一比例为6.2%（= 6×10）。
本研究提供了一个可持续且可扩展的模型来解决纳洛酮联合处方率低的问题，也可用于针对其他改善符合指南的处方实践的机会。
