Evidence map›Paper›PMID 41350102›Full record

ReviewJACC. Advances2025

Nudges in A Learning Health System: Applications to Cardiovascular-Kidney-Metabolic Care.

Diana De Oliveira-Gomes, Muthiah Vaduganathan, Tor Biering-Sørensen, Larry A Allen, Clara K Chow, Nihar R Desai, Adam J Nelson, Cass R Sunstein, Ankeet S Bhatt

Abstract readReview
In one paragraph

Review in JACC. Advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Bridging the screening-treatment gap in hypertension.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
    Article
  2. Article
  3. Article
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

9 authors.

Diana De Oliveira-GomesDepartment of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA.
Muthiah VaduganathanBrigham and Women's Hospital, Harvard Medical School, Cardiology, Boston, Massachusetts, USA.
Tor Biering-SørensenDepartment of Cardiology, Copenhagen University Hospital, Herlev, Denmark; Herlev & Gentofte Hospital, Hellerup, Denmark.
Larry A AllenDivision of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.
Clara K ChowWestmead Applied Research Centre, University of Sydney and Department of Cardiology, Westmead Hospital, Sydney, Australia.
Nihar R DesaiSection of Cardiovascular Medicine, Yale School of Medicine, Yale New Haven Hospital, New Haven, Connecticut, USA.
Adam J NelsonVictorian Heart Institute, Monash University, Melbourne, Australia.
Cass R SunsteinProgram on Behavioral Economics and Public Policy, Harvard Law School, Cambridge, Massachusetts, USA.
Ankeet S BhattDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, California, USA; Division of Research, Kaiser Permanente Northern California, San Francisco, California, USA; Division of Cardiovascular Medicine, Stanford School of Medicine, Palo Alto, California, USA. Electronic address: Ankeet.s.bhatt@kp.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nudges are behavioral interventions that influence decision-making by subtly altering the choice environment without restricting freedom. Rooted in behavioral economics, they have shown promise in health care by improving adherence to guidelines and promoting preventive behaviors. Learning Health Systems offer infrastructure for implementing nudges at scale through tools like electronic health records and decision support systems. In cardiovascular-kidney-metabolic care, nudges targeting both patients and providers, ranging from defaults and reminders to gamification, have improved prescribing, vaccination rates, and physical activity. Frameworks such as EAST and MINDSPACE guide effective design, emphasizing timing, audience, and framing. Looking ahead, artificial intelligence-powered nudges promise personalized, adaptive interventions that respond to real-time behavior and performance, enhancing scalability and sustainability. By aligning behavioral science with health technology, nudges can help optimize care delivery, reduce variation, and improve outcomes across complex health systems.

Indexed as

behavioral designcardiovascular diseasesclinical decision support systemsLearning Health Systemnudging

Identifiers

PMID41350102
PMCPMC12717558

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.