Evidence map›Paper›PMID 41419994›Full record

ReviewImplementation science communications2025

The role of electronic health records systems in de-implementing low-value care in primary care: a scoping review.

Oliver T Nguyen, Steven D Vo, Dang Nguyen, Sri Varsha Katoju, Avaneesh R Kunta, James H Ford, Young-Rock Hong, Randa Perkins, Amir Alishahi Tabriz, Kea Turner

Abstract readReview
In one paragraph

Review in Implementation science communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Oliver T NguyenDepartment of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA. fnguyen2@wisc.edu.ORCID http://orcid.org/0000-0003-2852-7106
Steven D VoDepartment of Epidemiology & Biostatistics, University of South Florida, Tampa, FL, USA.
Dang NguyenCorrigan Minehan Heart Center, Massachusetts General Hospital, Boston, MA, USA.
Sri Varsha KatojuDepartment of Community Health and Family Medicine, University of Florida, Gainesville, FL, USA.
Avaneesh R KuntaCollege of Medicine, University of Central Florida, Orlando, FL, USA.
James H FordDivision of Social and Administrative Sciences, School of Pharmacy, University of Wisconsin at Madison, Madison, WI, USA.
Young-Rock HongDepartment of Health Services Research, Management, and Policy, University of Florida, Gainesville, FL, USA.
Randa PerkinsDepartment of Internal Medicine, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
Amir Alishahi TabrizDepartment of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
Kea TurnerSchool of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundElectronic health record (EHR) systems have been used to support the implementation of evidence-based care. Growing evidence suggests that EHR systems can also support de-implementation of low-value care. However, a review of this literature has not been conducted. This scoping review will: 1) summarize how EHR-based interventions themselves have been used in primary care settings to de-implement low-value care, 2) summarize the effectiveness of these EHR interventions, 3) describe de-implementation strategies and outcome measures that have been used, and 4) describe facilitators and barriers that influence EHR-based de-implementation interventions.

methodsWe conducted a search using MEDLINE, CINAHL, Embase, and Web of Science on January 19, 2024 for peer-reviewed papers on EHRs and de-implementation in primary care. We inductively developed themes of how the EHR was used to support de-implementation. We mapped de-implementation strategies to a previously published taxonomy on implementation strategies, de-implementation outcomes to a previously published taxonomy on these outcomes, and facilitators and barriers to the Consolidated Framework for Implementation Research. We stratified study findings by EHR intervention type.

resultsWe included 50 studies. EHRs supported de-implementation using four intervention types: 1) EHR alerts, 2) order sets and preference lists, 3) documentation templates, and 4) communication tools among the care team. The proportion of studies that showed favorable effectiveness in reducing low-value care ranged from 16.7% (communication tools) to 50.0% (documentation templates). Common strategies to support EHR-based de-implementation interventions included auditing and providing feedback, conducting educational meetings, and distributing educational materials. Twenty-two studies reported some assessment of de-implementation outcomes. Most EHR intervention types had numerous multi-level facilitators and barriers identified.

conclusionsThis scoping review identified multiple EHR-based interventions that health systems use to support de-implementation and their effectiveness. Although promising, the evidence base is limited by the general lack of frameworks used for intervention development and de-implementation, unclear theoretical rationale to support the use of selected de-implementation strategies, and the unclear validity of de-implementation outcomes used. Additional research is needed to develop and validate frameworks and outcomes for de-implementation to strengthen the evidence base.

trial registrationNone.

Indexed as

Electronic health recordsImplementation scienceLow-value carePrimary care

Identifiers

PMID41419994
PMCPMC12717702

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.