Lauffenburger Julie C, Isaac Thomas, Trippa Lorenzo, Glynn Robert J, Russo Massimiliano, Keller Punam, Robertson Ted, Kim Dae H, Bhatkhande Gauri, Hanken Kaitlin E, Haff Nancy, Jungo Katharina Tabea, Crum Katherine L, Blair Molly S, Brill Gregory, Choudhry Niteesh K
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Center for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
J Am Geriatr Soc. 2025 Jul 4. doi: 10.1111/jgs.19609.
Interventions to reduce prescribing of high-risk medications like benzodiazepines and sedative hypnotics to older adults have had modest success. Electronic health record (EHR)-based alerts, especially those incorporating behavioral science, can improve prescribing but are under-evaluated for deprescribing.
In Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR), we randomized primary care providers (PCPs) in a large healthcare system to usual care (no tool), 1 of 14 EHR tools (alerts with or without in-basket messages) designed using behavioral science factors for patients ≥ 65 years using long-term benzodiazepines and sedative hypnotics, or standard EHR alert without factors. The alerts either triggered when PCPs initiated a patient encounter ("open encounter") or placed medication orders ("order entry"); most contained one additional behavioral factor. Stage 1 tested all tools; the most promising were evaluated in Stage 2. The primary outcome was deprescribing (composite of PCP-directed discontinuation or tapering) over follow-up, measured using EHR data. We used generalized linear mixed models combining both stages and then among open encounter tools.
Across 216 randomized PCPs, 3063 patients were eligible (mean: 74.5 [SD: 7.2] years of age; 66.5% female). Stage 1 results selected open encounter timing, boostering (e.g., reinforcement), simplification, additional timing, and pre-commitment behavioral science factors. Across both stages, 33.6% of usual care patients experienced deprescribing; unadjusted intervention group rates ranged from 25.9% to 45.1%. In primary models, none of the factors significantly increased the odds of deprescribing compared to arms not containing that factor. In secondary comparisons, open encounter timing was more effective than order entry (OR: 1.25, 95% CI: 1.01-1.56), and among open encounter tools, pre-commitment significantly increased deprescribing (OR: 1.67, 95% CI: 1.00-2.87).
Incorporating most behavioral science principles into EHR alerts did not significantly improve deprescribing in older adults. However, alerts at encounter opening and patient-focused pre-commitment approaches may be more effective solutions.
ClinicalTrials.gov identifier: NCT04284553.
减少向老年人开具苯二氮䓬类和镇静催眠药等高风险药物的干预措施取得的成效有限。基于电子健康记录(EHR)的警报,尤其是那些融入行为科学的警报,可以改善处方行为,但在减药方面的评估不足。
在“适应性设计在引导医疗服务提供者参与电子健康记录中的新应用(NUDGE-EHR)”研究中,我们将一个大型医疗系统中的初级保健提供者(PCP)随机分为常规护理组(无工具)、14种EHR工具中的一种(带有或不带有收件篮消息的警报),这些工具是利用行为科学因素为使用长效苯二氮䓬类和镇静催眠药的65岁及以上患者设计的,或无相关因素的标准EHR警报。警报在PCP开始患者诊疗时(“开始诊疗”)或下达用药医嘱时(“医嘱录入”)触发;大多数包含一个额外的行为因素。第一阶段测试了所有工具;最有前景的工具在第二阶段进行评估。主要结局是随访期间的减药情况(PCP指导下停药或逐渐减量的综合情况),使用EHR数据进行测量。我们使用广义线性混合模型合并两个阶段的数据,然后在开始诊疗工具中进行分析。
在216名随机分组的PCP中,3063名患者符合条件(平均年龄:74.5[标准差:7.2]岁;66.5%为女性)。第一阶段的结果选择了开始诊疗时间、强化(如加强)、简化、额外时间以及预先承诺等行为科学因素。在两个阶段中,33.6%的常规护理患者实现了减药;未调整的干预组比例在25.9%至45.1%之间。在主要模型中,与不包含该因素的组相比,没有一个因素能显著增加减药的几率。在次要比较中,开始诊疗时间比医嘱录入更有效(比值比:1.25,95%置信区间:1.01-1.56),在开始诊疗工具中,预先承诺显著增加了减药情况(比值比:1.67,95%置信区间:1.00-2.87)。
将大多数行为科学原则纳入EHR警报并不能显著改善老年人的减药情况。然而,在诊疗开始时的警报和以患者为中心的预先承诺方法可能是更有效的解决方案。
ClinicalTrials.gov标识符:NCT04284553。