Evidence map›Paper›PMID 34396212›Full record

ReviewJACC. CardioOncology2020

Using Behavioral Economics and Technology to Improve Outcomes in Cardio-Oncology.

Kimberly J Waddell, Payal D Shah, Srinath Adusumalli, Mitesh S Patel

Abstract readReview
In one paragraph

Review in JACC. CardioOncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Pilot Trial of a Behavioral Economics-Informed Clinical Decision Support Alert to Improve Inpatient Tobacco Use Treatment Rates.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025
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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

4 authors.

Kimberly J WaddellCrescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA.
Payal D ShahPerelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Srinath AdusumalliPenn Medicine Nudge Unit, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Mitesh S PatelCrescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with cancer are often at elevated risk for cardiovascular disease due to overlapping risk factors and cardiotoxic anticancer treatments. Their cancer diagnoses may be the predominant focus of clinical care, with less of an emphasis on concurrent cardiovascular risk management. Widely adopted technology platforms, including electronic health records and mobile devices, can be leveraged to improve the cardiovascular outcomes of these patients. These technologies alone may be insufficient to change behavior and may have greater impact if combined with behavior change strategies. Behavioral economics is a scientific field that uses insights from economics and psychology to help explain why individuals are often predictably irrational. Combining insights from behavioral economics with these scalable technology platforms can positively impact medical decision-making and sustained healthy behaviors. This review focuses on the principles of behavioral economics and how "nudges" and scalable technology can be used to positively impact clinician and patient behaviors.

Indexed as

ASCVD, atherosclerotic cardiovascular diseasebehavioral economicsbehavior changecardiotoxicityCV, cardiovascularCVD, cardiovascular diseaseDOAC, direct oral anticoagulantEHR, electronic health recordelectronic health recordsLVEF, left ventricular ejection fractionnudgeswearables

Identifiers

PMID34396212
PMCPMC8352113

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.