3 ways racial bias and stereotypes, algorithms affect clinical decision-making: KFF

Disproven conceptions of race may affect clinical decision-making and treatment in several ways and have implications on patient safety and outcomes, according to a Dec. 9 Kaiser Family Foundation report.

Though race is a "socio-political system of categorization without a biologic basis," it still plays a role in clinical teaching and decision-making, according to KFF. The use of race to inform clinical diagnoses and decisions may encourage the circulation of false notions of race and influence ongoing racial disparities in healthcare.  

Three ways race can affect clinical decision-making and treatment: 

1. Providers' attitudes, implicit biases and discrimination

Research suggests that provider and institutional bias and discrimination influence racial differences in diagnosis, prognosis and treatment decisions. A 2015 study suggests that providers were more likely to point to individual patient factors — rather than provider or health system influences — as causes for health disparities. A separate 2015 study found that providers were more likely to view Black patients as less cooperative with treatment and were more likely to associate noncompliance and risky behavior with Hispanic patients. A review of multiple studies found most healthcare providers appeared to have implicit bias in terms of positive attitudes toward white people and negative attitudes toward people of color.   

A 2020 survey by KFF and ESPN's The Undefeated found that Black adults were more likely than white adults to report negative experiences with providers, including feeling that a provider didn't believe they were telling the truth, being refused a test or treatment they thought was necessary, and being denied pain medication. Additionally, Black and Hispanic adults were more likely than white adults to say it was challenging to find a physician with a shared background and experiences and who treated them with respect.

2. Disease stereotyping and clinical nomenclature

Some medical training techniques and materials use labels that combine race and ancestry and depict disease through racial stereotypes and mental associations. Preclinical lectures and clinical vignettes use nonspecific labels, such as Black instead of Nigerian or Haitian, and may misuse "race" in place of "genetic ancestry." In some cases, race is used incorrectly as a proxy for differing socioeconomic statuses, health behaviors or other factors that may influence care access or risk of disease. Lecture materials often present racial differences in disease burden without historical or social context, which may lead students to associate diseases with certain racial groups and assign differences to genetic predisposition. For example, most medical learning resources depict Lyme disease predominantly on white skin. In turn, the disease is often diagnosed much later among Black patients. In contrast, Black skin is more commonly used to depict sexually transmitted diseases.   

Some disease names use racial or geographic references that tie diseases to specific communities. Though clinical nomenclature has moved toward more descriptive language, disease naming is still sometimes linked to place of discovery. In 2015, the World Health Organization said linking disease names to geography may result in backlash toward certain ethnic communities. This has occurred recently, when the COVID-19 virus was labeled the "China virus," which has been tied to increased public anti-Asian sentiment and Asian hate crimes.

3. Clinical algorithms, tools and guidelines

Currently, clinical calculators across multiple specialties assign different risk levels for certain conditions based on race. For example, a common calculator used to predict success of vaginal birth after Cesarean section had a correction factor for both Black and Hispanic race that decreases the success of VBAC. The tool may urge providers into disproportionately recommending these patients undergo a Cesarean section. Since race is an extremely inconsistent proxy for genetic ancestry, using race in clinical calculators may lead to both under- and over-treatment and delays in diagnosis and care.

Research has also found algorithms may have racial bias if the underlying data on which they are built are biased and/or not reflective of a diverse population. However, research also suggests that carefully designed algorithms can mitigate bias and help decrease disparities in care.

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