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      The Impact of Unconscious Bias in Healthcare: How to Recognize and Mitigate It

      1 , 2 , 3 , 3 , 3
      The Journal of Infectious Diseases
      Oxford University Press (OUP)

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          Abstract

          The increasing diversity in the US population is reflected in the patients who healthcare professionals treat. Unfortunately, this diversity is not always represented by the demographic characteristics of healthcare professionals themselves. Patients from underrepresented groups in the United States can experience the effects of unintentional cognitive (unconscious) biases that derive from cultural stereotypes in ways that perpetuate health inequities. Unconscious bias can also affect healthcare professionals in many ways, including patient-clinician interactions, hiring and promotion, and their own interprofessional interactions. The strategies described in this article can help us recognize and mitigate unconscious bias and can help create an equitable environment in healthcare, including the field of infectious diseases.

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          Most cited references45

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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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            Long-term reduction in implicit race bias: A prejudice habit-breaking intervention.

            We developed a multi-faceted prejudice habit-breaking intervention to produce long-term reductions in implicit race bias. The intervention is based on the premise that implicit bias is like a habit that can be reduced through a combination of awareness of implicit bias, concern about the effects of that bias, and the application of strategies to reduce bias. In a 12-week longitudinal study, people who received the intervention showed dramatic reductions in implicit race bias. People who were concerned about discrimination or who reported using the strategies showed the greatest reductions. The intervention also led to increases in concern about discrimination and personal awareness of bias over the duration of the study. People in the control group showed none of the above effects. Our results raise the hope of reducing persistent and unintentional forms of discrimination that arise from implicit bias.
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              Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies.

              This article reports a meta-analysis of studies examining the predictive validity of the Implicit Association Test (IAT) and explicit measures of bias for a wide range of criterion measures of discrimination. The meta-analysis estimates the heterogeneity of effects within and across 2 domains of intergroup bias (interracial and interethnic), 6 criterion categories (interpersonal behavior, person perception, policy preference, microbehavior, response time, and brain activity), 2 versions of the IAT (stereotype and attitude IATs), 3 strategies for measuring explicit bias (feeling thermometers, multi-item explicit measures such as the Modern Racism Scale, and ad hoc measures of intergroup attitudes and stereotypes), and 4 criterion-scoring methods (computed majority-minority difference scores, relative majority-minority ratings, minority-only ratings, and majority-only ratings). IATs were poor predictors of every criterion category other than brain activity, and the IATs performed no better than simple explicit measures. These results have important implications for the construct validity of IATs, for competing theories of prejudice and attitude-behavior relations, and for measuring and modeling prejudice and discrimination.
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                Author and article information

                Journal
                The Journal of Infectious Diseases
                Oxford University Press (OUP)
                0022-1899
                1537-6613
                September 15 2019
                August 20 2019
                August 20 2019
                September 15 2019
                August 20 2019
                August 20 2019
                : 220
                : Supplement_2
                : S62-S73
                Affiliations
                [1 ]University of Nebraska Medical Center, Omaha
                [2 ]University of Wisconsin, Madison
                [3 ]Stanford University School of Medicine, California
                Article
                10.1093/infdis/jiz214
                31430386
                6e1344e0-dd80-4970-adff-69848f919329
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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