Journal of the American Geriatrics Society · 2025
Comparing Fourteen Behavioral Science Electronic Health Record Deprescribing Tools in Older Adults: NUDGE-EHR Adaptive Trial.
СтатьяРандомизированное исследованиеСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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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
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04284553.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04284553.