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ArticleResearch square2025

When Nudges Don't Budge: A Mixed Methods Study of Why EHR-Based Deprescribing Nudges Failed to Change Provider Behavior.

Ratnalekha V N Viswanadham, Hayley M Belli, Tiffany Rose Martinez, Christina Wong, Saul Blecker, Andrea B Troxel, Devin M Mann

Registry-linked trialAbstract readPreprint
In one paragraph

Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04181307 (A Randomized Trial of a BE-EHR Module to Guide the Care of Older Adults With Diabetes), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

NCT04181307 nacompletednot on this map

A Randomized Trial of a BE-EHR Module to Guide the Care of Older Adults With Diabetes

TypeinterventionalSponsorNYU Langone HealthRan2020 to 2022Enrolled7,630ConditionsDiabetesArmsBE-EHR module
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Ratnalekha V N ViswanadhamNYU Grossman School of Medicine: New York University School of Medicine.ORCID 0000-0002-9786-2090
Hayley M BelliNYU Grossman School of Medicine: New York University School of Medicine.
Tiffany Rose MartinezNYU Grossman School of Medicine: New York University School of Medicine.
Christina WongNYU Langone Medical Center: NYU Langone Health.
Saul BleckerNYU Grossman School of Medicine: New York University School of Medicine.
Andrea B TroxelNYU Grossman School of Medicine: New York University School of Medicine.
Devin M MannNYU Grossman School of Medicine: New York University School of Medicine.

Funding

NRSA Training CoreTL1TR001447 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI MICOLI, KEITH J. · 2015 to 2025
$5.9M
EHR nudges: Development and testing of a behavioral economics electronic health record moduleR33AG057382 · NIA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI MANN, DEVIN M, TROXEL, ANDREA B · 2019 to 2021
$2.2M
NCATS NIH HHS TL1 TR001447NIA NIH HHS R33 AG057382
6 · The paper itself

Abstract

Background: De-implementation-reducing low-value or harmful care-is critical but often difficult in practice. Nudges via clinical decision support (CDS) tools in electronic health records aim to promote guideline-concordant care, but their effectiveness is mixed. In a randomized trial, we tested CDS nudges to support deprescribing glycemic medications in older adults, aligned with Choosing Wisely guidelines. Despite prior success elsewhere, the intervention had limited impact. The current study evaluated potential reasons why the EHR-based nudges to encourage guideline-based, relaxed glycemic control for older adults with Type 2 Diabetes were not effective in influencing clinician behavior. Methods: We conducted a retrospective cohort analysis of EHR data from 67,412 alerts issued to clinicians, promoting different types of glycemic control, including reducing metformin, switching from non-metformin medications to metformin, and discontinuing medication. Comments left by providers on 779 of those firings were coded and thematically analyzed by two authors. Logistic and multinomial logistic regressions were performed to understand the contexts behind the lack of nudge effectiveness at the alert, encounter, patient, and physician levels. Results: Out of 67,412 alerts, providers commented in only 1.15% of cases. When they did, they were about 10.7% more likely to act on the alert, but comments were mostly negative (3.28 times more likely). Feedback highlighted three themes: disagreement with guidelines (most common), poor alert fit in workflow, and patient reluctance to change medications. Logistic regressions showed providers were less likely to act on alerts with multiple triggers and more likely to leave negative comments. Multinomial models linked rejection themes to patient and medication traits, noting less rejection related to workflow in patients with limited life expectancy. Disparities in engagement were found, with female providers, patients, and socially vulnerable individuals less likely to comment. Conclusion: These findings highlight barriers to de-implementation via CDS. Provider disagreement, misaligned alerts, and patient resistance hinder effectiveness. Low engagement and negative feedback suggest nudges alone may not change behavior without integration into routines. Engagement variation stresses the need for tailored strategies. Future work should refine nudge design to address complexity, align with provider roles, and include patient-centered approaches. Trial registration: The NYU School of Medicine Institutional Review Board (i17-01308) approved the trial, which has the clinicaltrials.gov ID NCT04181307 (https://clinicaltrials.gov/study/NCT04181307), with date of first record on November 26, 2019.

Indexed as

behavioral economicschoosing wiselyclinical decision supportdiabeteselectronic health recordsimplementation sciencemixed methods researchnudge

Identifiers

PMID41041570
PMCPMC12486085

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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.