A systematic review of implicit bias in health care: A call for intersectionality

• Main Authors: Oluwabunmi Ogungbe, Amal K. Mitra, Joni K. Roberts
• Format: Article
• Language: English
• Published: Ibrahim Medical College 2019-06-01
• Series: IMC Journal of Medical Science
• Online Access: http://www.imcjms.com/registration/journal_full_text/315

Background and objectives: Health disparities are a growing concern in health care. Research provides ample evidence of bias in patient care and mistrust between patient and providers in ways that could perpetuate health care disparities. This study aimed to review existing literature on implicit bias (or unconscious bias) in healthcare settings and determine studies that have considered adverse effects of bias of more than one domain of social identity (e.g., race and gender bias) in health care. Methods: This is a systematic review of articles using databases such as EBSCO, Embase, CINAHL, COCHRANE, Google Scholar, PsychINFO, Pub Med, and Web of Science. Search terms included implicit bias, unconscious bias, healthcare, and public health. The inclusion criteria included studies that assessed implicit bias in a healthcare setting, written in English, and published from 1997-2018. Results: Thirty-five articles met the selection criteria – 15 of which examined race implicit bias, ten examined weight bias, four assessed race and social class, two examined sexual orientation, two focused on mental illness, one measured race and sexual orientation, and another investigated age bias. Conclusions: Studies that measured more than one domain of social identity of an individual did so separately without investigating how the domains overlapped. Implicit Association Test (IAT) is a widely used psychological test which is used to determine existence of an implicit bias in an individual. However, this study did not find any use of an instrument that could assess implicit bias toward multiple domains of social identities. Because of possible multiplicative effects of several biases affecting a single entity, this study suggests the importance of developing a tool in measuring intersectionality of biases. IMC J Med Sci 2019; 13(1): 005. EPub date: 13 March 2019 Address for Correspondence: Amal K. Mitra, Professor of Epidemiology, School of Public Health, Jackson State University, 350 West Woodrow Wilson Drive, Room 216,Jackson, MS 39213; e-mail: [e-mail]