CoChat
Sign Up
Cited by 150Sex and Gender in HealthcareDiversity and Career in MedicineClinical Reasoning and Diagnostic Skills

The DOI matches its Crossref record; 64 of the 66 references it cites were found in a registry or an index.

Implicit Gender Bias and the Use of Cardiovascular Tests Among Cardiologists

Open access
Daugherty, Stacie L., Blair, Irene V., Havranek, Edward P., Furniss, Anna, Dickinson, L. Miriam, Karimkhani, Elhum, Main, Deborah S., Masoudi, Frederick A.·2017·Journal of the American Heart Association
DOI 10.1161/jaha.117.006872PMC PMC5779009
Open this paper in CoChat — free accountOpen PDF

Abstract

Background: Physicians' gender bias may contribute to gender disparities in cardiovascular testing. We used the Implicit Association Test to examine the association of implicit gender biases with decisions to use cardiovascular tests. Methods and Results: In 2014, cardiologists completed Implicit Association Tests and a clinical vignette with patient gender randomly assigned. The Implicit Association Tests measured implicit gender bias for the characteristics of strength and risk taking. The vignette represented an intermediate likelihood of coronary artery disease regardless of patient gender: chest pain (part 1) followed by an abnormal exercise treadmill test (part 2). Cardiologists rated the likelihood of coronary artery disease and the usefulness of stress testing and angiography for the assigned patient. Of the 503 respondents (9.3% of eligible; 87% male, median age of 45 years, 58% in private practice), the majority associated strength or risk taking implicitly with male more than female patients. The estimated likelihood of coronary artery disease for both parts of the vignette was similar by patient gender. The utility of secondary stress testing after an abnormal exercise treadmill test was rated as “high” more often for female than male patients (32.8% versus 24.3%, P =0.04); this difference did not vary with implicit bias. Angiography was more consistently rated as having “high” utility for male versus female patients (part 1: 19.7% versus 9.8%; part 2: 73.7% versus 64.3%; P <0.05 for both); this difference was larger for cardiologists with higher implicit gender bias on risk taking ( P =0.01). Conclusions: Cardiologists have varying degrees of implicit gender bias. This bias explained some, but not all, of the gender variability in simulated clinical decision‐making for suspected coronary artery disease.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1161/jaha.117.006872

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Flowchart of the study population. IAT indicates Implicit Association Test.
Figure 1 Flowchart of the study population. IAT indicates Implicit Association Test.CC BY-NC · PubMed Central
Figure 2 Cardiology physicians' gender bias regarding risk taking and strength. IAT indicates Implicit Association Test.
Figure 2 Cardiology physicians' gender bias regarding risk taking and strength. IAT indicates Implicit Association Test.CC BY-NC · PubMed Central
Figure 3 The strength of angiography rating varied according to case patient gender and physician implicit gender bias. The x‐axis represents physician gender bias based on Implicit Association Test scores, and the y‐axis represents the extent to which angiography was rated as useful for the case vignette by the physician. The relationship between gender bias and angiography rating is indicated when the case patient was male (blue line) and female (red line). In unadjusted analysis, significant interactions were seen between gender bias and case gender; those with higher implicit gender bias on risk taking (A) or strength (B) rated angiography as less useful in women than men (unadjusted P<0.05 for interaction for both). After adjustment for perceived likelihood of coronary artery disease and physician specialty, the interaction between risk‐taking bias and patient gender on angiography usefulness remained significant (adjusted P=0.01 for interaction in panel A); however, the interaction with strength bias was no longer significant (adjusted P=0.12 in panel B).
Figure 3 The strength of angiography rating varied according to case patient gender and physician implicit gender bias. The x‐axis represents physician gender bias based on Implicit Association Test scores, and the y‐axis represents the extent to which angiography was rated as useful for the case vignette by the physician. The relationship between gender bias and angiography rating is indicated when the case patient was male (blue line) and female (red line). In unadjusted analysis, significant interactions were seen between gender bias and case gender; those with higher implicit gender bias on risk taking (A) or strength (B) rated angiography as less useful in women than men (unadjusted P<0.05 for interaction for both). After adjustment for perceived likelihood of coronary artery disease and physician specialty, the interaction between risk‐taking bias and patient gender on angiography usefulness remained significant (adjusted P=0.01 for interaction in panel A); however, the interaction with strength bias was no longer significant (adjusted P=0.12 in panel B).CC BY-NC · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 66 references

Research works printed but not linked: 1

  1. Smedley BD, Stith AY, Nelson AR. Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care, Board on Health Sciences Policy, and Institute of Medicine, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: The National Academies Press; 2003.

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Daugherty, S. L., Blair, I. V., Havranek, E. P., Furniss, A., Dickinson, L. M., Karimkhani, E., Main, D. S., & Masoudi, F. A.
Affiliations
  • University of Colorado Anschutz (US)
  • University of Colorado Boulder (US)
  • Denver Health Medical Center (US)
  • University of Colorado Denver (US)
Corresponding author
Daugherty, Stacie L.
ORCID
Venue
Journal of the American Heart Association
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2017-12-02
Volume
6
Issue
12
ISSN
2047-9980 (ISSN-L)
DOI
10.1161/jaha.117.006872
PMC
PMC5779009
OpenAlex
W2769597399
Cited by
150
References
66
Citations by year
2026: 10 · 2025: 17 · 2024: 18 · 2023: 21 · 2022: 27 · 2021: 30 · 2020: 18 · 2019: 7 · 2018: 2
FWCI
6.93
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Journal of the American Heart Association (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • CU Scholar (University of Colorado Boulder) (submittedVersion, cc-by-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
National Heart, Lung, and Blood Institute (R01HL133343, K08HL103776)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Sex and Gender in Healthcare
  • Social Sciences › Gender Studies › Diversity and Career in Medicine
  • Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
Keywords
implicit gender bias, gender disparities, cardiovascular testing, cardiologists, coronary artery disease, stress testing, angiography, treadmill exercise testing, clinical decision-making, Implicit Association Test, patient gender
MeSH terms
Clinical Decision-Making, Cardiologists, Exercise Test, Humans, Sex Factors, United States, Sex Distribution, Healthcare Disparities

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review