A decade of studying implicit racial/ethnic bias in healthcare providers using the implicit association test
Introduction
The landmark report Unequal Treatment brought increased attention to inequities that exist in healthcare, including racial/ethnic disparities in the incidence, prevalence and complications from hypertension, heart disease and diabetes (Nelson et al., 2003). Despite efforts to reduce such disparities, racial/ethnic minorities (Black, Hispanic, Asian, Pacific Islander and American Indian/Alaska Native) continue to experience poorer healthcare and outcomes. In an annual disparities report each year since 2003, the Agency for Healthcare Research and Quality has documented that widespread disparities persist in the United States. Through 2013 Blacks, Hispanics, and American Indians/Alaska Natives have continued to receive worse care for 40% of the quality measures assessed and Asians receive worse care for 20% of measures (Agency for Healthcare Research and Quality, 2016). Minorities also have higher incidence, mortality and advanced staging at diagnosis for several cancer types including cervical, kidney, breast, colorectal, lung, and prostate (National Cancer Institute, 2016, Jemal et al., 2017)). For children, disparities in infant mortality rates, chronic disease, quality of care, organ transplantation and leukemia related deaths have also been noted (Flores, 2010).
Although a large body of literature documenting disparities in health and healthcare exists, little is known about the sources of such disparities. Unequal Treatment concluded that “bias, stereotyping, prejudice and clinical uncertainty on the part of health care providers may contribute to racial/ethnic disparities in health care” (Nelson et al., 2003, p. 12). Other research also suggests that health care providers' (HCPs) perceptions of patients vary depending on patient race/ethnicity, suggesting the existence of bias. One study found cardiologists perceived Black patients as less intelligent, likeable, friendly; and more prone to risky behavior and non-compliance compared to White patients (van Ryn and Burke, 2000). Patients’ perceptions of discrimination in medical interactions also suggest the existence of provider bias. When compared to White patients, minorities are more likely to believe they would receive better care and more respect from medical staff if they belonged to another racial group (Johnson et al., 2004).
Despite evidence suggesting the presence of provider bias, measuring bias poses methodological challenges. Bias can exist on both explicit and implicit levels, representing two related but independent constructs (Nosek et al., 2007a). Explicit bias encompasses our conscious attitudes which can be measured by self-report, but pose the potential of individuals falsely endorsing more socially desirable attitudes. Implicit biases are unconscious and involuntary attitudes which lie below the surface of consciousness, but can influence affect, behavior, and cognitive processes. The Implicit Association Test (IAT) is one validated tool used to measure implicit bias (Greenwald et al., 1998). The IAT requires participants to rapidly pair two social groups with either positive or negative attributes. For example, in the race IAT, participants pair photos of Black and White faces with good or bad words like pleasure or agony. Depending on the latency in response time and frequency of errors, the IAT measures the strength of association of each pairing such that more strongly associated categories are easier to pair, reflected by faster responses and fewer errors. Participants who categorize White faces with positive words more quickly and with fewer errors than when categorizing Black faces have an implicit pro-White bias. Scored using the D algorithm, the average difference in response time across trials yields a continuous measure ranging from −2 to +2, which represents an estimate of effect size (Greenwald et al., 2003). Results are categorized into groups with scores from 0 to 0.14 indicating no racial bias; 0.15–0.34, slight pro-White bias; 0.35–0.64, moderate pro-White bias; and >0.65, strong pro-White bias. Negative scores of the same degree indicate similar categories of pro-Black bias.
Though introduced into psychological literature in 1998, research using the IAT to examine HCP bias was first published in 2007 (Green et al., 2007). The objective of this review was to critically assess and synthesize the current knowledge on the role of implicit bias in healthcare disparities. Specifically, we sought to determine whether implicit bias towards racial/ethnic minorities is present among HCPs; and if so, determine if implicit bias is associated with healthcare outcomes, and if effective interventions exist to reduce implicit bias and its impact on healthcare.
Section snippets
Methods
A literature review was performed using a protocol created according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (Moher et al., 2015). In consultation with a biomedical sciences librarian, we searched PubMed, PsycINFO, SCOPUS and CINAHL for articles published from 1997 through May 30th 2015. Our search strategy is detailed in Table 1. Search terms were saved in PubMed, with weekly-automated updates for additional articles published through September 30th
Results
Details about objectives, location and setting, provider characteristics, patient characteristics, provider IAT scores, and associations between implicit bias with outcomes or effects of intervention for all 37 studies are detailed in the supplemental online Appendix [INSERT LINK TO ONLINE FILE A].
Discussion
Our review of the literature reveals four important findings. First, a growing body of research suggests that similar to the general US population, most HCPs across multiple levels of training and disciplines have implicit biases against Black, Hispanic, American-Indian and dark-skinned individuals. Second, we found interesting trends when looking at provider characteristics associated with bias. Most studies suggest Blacks are more likely to demonstrate no implicit bias compared to Whites and
Conclusion
Overall, this review summarizes the best available evidence on the role of implicit provider bias in healthcare disparities. Future studies have the opportunity to build on this current body of research, and in doing so will enable us to achieve equity in healthcare and outcomes for all.
Acknowledgements
This research was supported by The Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program grant 72430 to Dr. Johnson and by the Perelman School of Medicine at the University of Pennsylvania.
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