Publication | Open Access
Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort
102
Citations
31
References
2022
Year
<b><i>Background:</i></b> Despite documented benefits of diabetes technology in managing type 1 diabetes, inequities persist in the use of these devices. Provider bias may be a driver of inequities, but the evidence is limited. Therefore, we aimed to examine the role of race/ethnicity and insurance-mediated provider implicit bias in recommending diabetes technology. <b><i>Method:</i></b> We recruited 109 adult and pediatric diabetes providers across 7 U.S. endocrinology centers to complete an implicit bias assessment composed of a clinical vignette and ranking exercise. Providers were randomized to receive clinical vignettes with differing insurance and patient names as proxy for Racial-Ethnic identity. Bias was identified if providers: (1) recommended more technology for patients with an English name (Racial-Ethnic bias) or private insurance (insurance bias), or (2) Race/Ethnicity or insurance was ranked high (Racial-Ethnic and insurance bias, respectively) in recommending diabetes technology. Provider characteristics were analyzed using descriptive statistics and multivariate logistic regression. <b><i>Result:</i></b> Insurance-mediated implicit bias was common in our cohort (<i>n</i> = 66, 61%). Providers who were identified to have insurance-mediated bias had greater years in practice (5.3 ± 5.3 years vs. 9.3 ± 9 years, <i>P</i> = 0.006). Racial-Ethnic-mediated implicit bias was also observed in our study (<i>n</i> = 37, 34%). Compared with those without Racial-Ethnic bias, providers with Racial-Ethnic bias were more likely to state that they could recognize their own implicit bias (89% vs. 61%, <i>P</i> = 0.001). <b><i>Conclusion:</i></b> Provider implicit bias to recommend diabetes technology was observed based on insurance and Race/Ethnicity in our pediatric and adult diabetes provider cohort. These data raise the need to address provider implicit bias in diabetes care.
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