ArticleImplementation science : IS2021
Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol.
Article in Implementation science : IS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04284553 (Optimizing Electronic Health Record Prompts With Behavioral Economics to Improve Prescribing for Older Adults), which is not on this map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Optimizing Electronic Health Record Prompts With Behavioral Economics to Improve Prescribing for Older Adults
Who cites it
14 citing papers in PubMed, 19 citations in OpenAlex.
- Trial
- Comparing Fourteen Behavioral Science Electronic Health Record Deprescribing Tools in Older Adults: NUDGE-EHR Adaptive Trial.Journal of the American Geriatrics Society · 2025Trial
- Behavioral Phenotypes in Electronic Health Record Use by Primary Care Providers: a Cluster Analysis.Journal of general internal medicine · 2025Article
- Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system.Learning health systems · 2024Article
- Uncertainty directed factorial clinical trials.Biostatistics (Oxford, England) · 2024Article
- Bridging Treatment Implementation Gaps in Patients With Heart Failure: JACC Focus Seminar 2/3.Journal of the American College of Cardiology · 2023Review
- Reducing ethnic and racial disparities by improving undertreatment, control, and engagement in blood pressure management with health information technology (REDUCE-BP) hybrid effectiveness-implementation pragmatic trial: Rationale and design.American heart journal · 2023Article
- Connecting the science and practice of implementation - applying the lens of context to inform study design in implementation research.Frontiers in health services · 2023Article
- An empirical investigation into the preferences of the elderly for user interface design in personal electronic health record systems.Frontiers in digital health · 2023Article
- Evaluating fitness-for-use of electronic health records in pragmatic clinical trials: reported practices and recommendations.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Nudging within learning health systems: next generation decision support to improve cardiovascular care.European heart journal · 2022Article
- Designing and conducting adaptive trials to evaluate interventions in health services and implementation research: practical considerations.BMJ medicine · 2022Article
- Use of the consolidated framework for implementation research in a mixed methods evaluation of the EQUIPPED medication safety program in four academic health system emergency departments.Frontiers in health services · 2022Article
- Left-digit bias and deceased donor kidney utilization.Clinical transplantation · 2021Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundThe prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actionable information. Electronic health record (EHR)-based tools are commonly used but have had limited application in facilitating deprescribing in older adults. The objective is to determine whether designing EHR tools using behavioral science principles reduces inappropriate prescribing and clinical outcomes in older adults.
methodsThe Novel Uses of Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) project uses a two-stage, 16-arm adaptive randomized pragmatic trial with a "pick-the-winner" design to identify the most effective of many potential EHR tools among primary care providers and their patients ≥ 65 years chronically using benzodiazepines, sedative hypnotic ("Z-drugs"), or anticholinergics in a large integrated delivery system. In stage 1, we randomized providers and their patients to usual care (n = 81 providers) or one of 15 EHR tools (n = 8 providers per arm) designed using behavioral principles including salience, choice architecture, or defaulting. After 6 months of follow-up, we will rank order the arms based upon their impact on the trial's primary outcome (for both stages): reduction in inappropriate prescribing (via discontinuation or tapering). In stage 2, we will randomize (a) stage 1 usual care providers in a 1:1 ratio to one of the up to 5 most promising stage 1 interventions or continue usual care and (b) stage 1 providers in the unselected arms in a 1:1 ratio to one of the 5 most promising interventions or usual care. Secondary and tertiary outcomes include quantities of medication prescribed and utilized and clinically significant adverse outcomes. DISCUSSION: Stage 1 launched in October 2020. We plan to complete stage 2 follow-up in December 2021. These results will advance understanding about how behavioral science can optimize EHR decision support to improve prescribing and health outcomes. Adaptive trials have rarely been used in implementation science, so these findings also provide insight into how trials in this field could be more efficiently conducted.
trial registrationClinicaltrials.gov ( NCT04284553 , registered: February 26, 2020).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.