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      Implicit Gender Bias and the Use of Cardiovascular Tests Among Cardiologists

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          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.

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          Most cited references51

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          Measuring individual differences in implicit cognition: the implicit association test.

          An implicit association test (IAT) measures differential association of 2 target concepts with an attribute. The 2 concepts appear in a 2-choice task (2-choice task (e.g., flower vs. insect names), and the attribute in a 2nd task (e.g., pleasant vs. unpleasant words for an evaluation attribute). When instructions oblige highly associated categories (e.g., flower + pleasant) to share a response key, performance is faster than when less associated categories (e.g., insect & pleasant) share a key. This performance difference implicitly measures differential association of the 2 concepts with the attribute. In 3 experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black + pleasant vs. White + pleasant for self-described unprejudiced White subjects).
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            Implicit bias in healthcare professionals: a systematic review

            Background Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. Methods PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies. Results Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. Discussion The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Conclusions Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.
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              Understanding and using the implicit association test: I. An improved scoring algorithm.

              In reporting Implicit Association Test (IAT) results, researchers have most often used scoring conventions described in the first publication of the IAT (A.G. Greenwald, D.E. McGhee, & J.L.K. Schwartz, 1998). Demonstration IATs available on the Internet have produced large data sets that were used in the current article to evaluate alternative scoring procedures. Candidate new algorithms were examined in terms of their (a) correlations with parallel self-report measures, (b) resistance to an artifact associated with speed of responding, (c) internal consistency, (d) sensitivity to known influences on IAT measures, and (e) resistance to known procedural influences. The best-performing measure incorporates data from the IAT's practice trials, uses a metric that is calibrated by each respondent's latency variability, and includes a latency penalty for errors. This new algorithm strongly outperforms the earlier (conventional) procedure.
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                Author and article information

                Contributors
                stacie.daugherty@ucdenver.edu
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                29 November 2017
                December 2017
                : 6
                : 12 ( doiID: 10.1002/jah3.2017.6.issue-12 )
                : e006872
                Affiliations
                [ 1 ] Division of Cardiology Department of Medicine University of Colorado School of Medicine Aurora CO
                [ 2 ] Adult and Children Center for Outcomes Research and Delivery Sciences (ACCORDS) University of Colorado Aurora CO
                [ 3 ] Colorado Cardiovascular Outcomes Research Group Denver CO
                [ 4 ] Department of Psychology and Neuroscience University of Colorado Boulder Boulder CO
                [ 5 ] Division of Cardiology Denver Health and Hospital Authority Denver CO
                [ 6 ] Department of Health and Behavioral Sciences University of Colorado Denver Denver CO
                Author notes
                [*] [* ] Correspondence to: Stacie L. Daugherty, MD MSPH, Division of Cardiology, University of Colorado School of Medicine, 12605 E. 16 th Ave, Mailstop B130, PO Box 6511, Aurora, CO 80045. E‐mail: stacie.daugherty@ 123456ucdenver.edu
                Article
                JAH32764
                10.1161/JAHA.117.006872
                5779009
                29187391
                91363ac8-dc91-4e58-a209-457b926fdffe
                © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 06 June 2017
                : 20 October 2017
                Page count
                Figures: 3, Tables: 3, Pages: 11, Words: 8244
                Funding
                Funded by: National Heart, Lung, and Blood Institute
                Award ID: K08 HL103776
                Award ID: R01 HL133343
                Funded by: American Heart Association
                Award ID: 15SFDRN24470027
                Categories
                Original Research
                Original Research
                Health Services and Outcomes Research
                Custom metadata
                2.0
                jah32764
                December 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.8 mode:remove_FC converted:27.12.2017

                Cardiovascular Medicine
                angiography,gender disparities,implicit bias,stress testing,cardiovascular disease,women,diagnostic testing,quality and outcomes

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