Articles

Discrimination in Healthcare

As we age, hearing about the passing of friends, family, and even colleagues becomes a normal part of life. However, learning that black individuals have a higher rate of mortality than White Americans at nearly every age is anything but normal.

Clyde Murphy is a successful black civil rights lawyer who graduated from Yale in 1970, and has a unique story to tell. Being one of America’s top civil rights lawyers for 30 years, his success story was an inspiration for people of color across the world. In 2010 his life took an unfortunate turn when he died from a pulmonary embolism at 62. Clyde’s experience was not an unusual occurrence. It was just one example of countless situations which occur every day for individuals belonging to racialized and marginalized groups. An article in the Yale Alumni Magazine titled “Before Their Time”, reported that 9 of the 32 African American men who entered the Yale Class of 1970 died at a rate more than three times faster than that of their white classmates.

The question arises, why do such disparities exist in healthcare that causes a disproportionate impact on people of color and other marginalized groups with regard to health outcomes and the quality of healthcare that they receive? There is no straightforward answer to such a complex issue, but it is known that many factors contribute to the increased morbidity and mortality among these populations, including education, income level, geography, occupation, physical environment, and more. It’s important to note, that this inequality is not limited to black individuals. Hispanic, Asian, immigrant, refugee, and LGBTQ+ communities also face significantly higher rates of infection, hospitalization, shorter life expectancies, and death compared to their white populations. The National Academy of Medicine (NAM) found that “racial and ethnic minorities receive lower quality healthcare than white people-even in circumstances where income, age, and severity of conditions were parallel.” They identified various reasons for this gap, namely, unequal treatment stemming from bias, prejudice, and stereotyping on the part of physicians, which creates differences in the care provided. Additionally, a report by the Wellesley Institute shows that black Canadians have an increased prevalence of chronic diseases, including, diabetes, asthma, heart failure, and stroke.

Health inequities are complex, multi-faceted, and constantly evolving. Understanding the root causes and factors that lead to health inequality is the only way to tackle this issue. These inequalities stem from biases, racism, and income disparities. To tackle this ongoing issue, a collective effort must be made to improve access to healthcare services and to educate the public on the effects that racism has on the overall quality of life for racialized groups. By acknowledging the issue, work towards a more equitable and fair healthcare system for all can be done.

Systemic issues such as lack of access to quality healthcare can significantly contribute to racial disparities in healthcare outcomes. Black communities are more likely in areas with limited access to healthcare facilities that may also not have health insurance, which creates barriers to accessing healthcare and can lead to poorer health outcomes. Historical and ongoing discrimination in housing policies has had a significant impact on the lack of access to quality healthcare in black communities. One of the most significant policies that have contributed to this issue is redlining, a discriminatory practice that emerged in the 1930s and lasted until the 1960s. Redlining refers to a practice in which banks and other lending institutions would refuse to invest in predominantly black neighborhoods, based solely on their racial composition. As a result of redlining, black communities were denied access to mortgage loans and other forms of financing, which made it difficult for them to purchase homes and build equity and wealth. This practice also prevented black neighborhoods from attracting businesses, including healthcare facilities, which could have provided residents with greater access to healthcare resources. As a result, many black neighborhoods lacked adequate healthcare facilities and resources, leaving residents with limited access to preventive care and medical treatment. This lack of access to healthcare can have significant consequences for the health outcomes of black individuals, who are more likely to suffer from chronic illnesses such as diabetes, hypertension, and heart disease. Although redlining is no longer legal, the effects of this practice continue to impact black communities today. Many predominantly black neighborhoods still lack adequate healthcare facilities, which can make it difficult for residents to access the care they need. Moreover, the lack of healthcare resources in these areas can make it difficult for black individuals to receive preventive care, which can lead to the exacerbation of chronic illnesses and other health problems.

In addition, the high rates of poverty in black communities contribute to the lack of access to quality healthcare. Poverty is associated with a higher risk of chronic illnesses such as diabetes and hypertension, and it can make it more difficult for individuals to afford health insurance and pay for medical expenses. This, in turn, can lead to delayed or inadequate medical treatment, worsening health outcomes.

The lack of diversity among healthcare professionals is another factor that contributes to the lack of access to quality healthcare in black communities. Black patients are more likely to feel comfortable with and trust healthcare providers who share their cultural and racial backgrounds. This is because these healthcare providers may be better able to understand the experiences and perspectives of their patients and provide care that is culturally sensitive and appropriate. However, black individuals are significantly underrepresented in the healthcare workforce, particularly in high-paying, leadership positions. According to the Association of American Medical Colleges, in 2020, black people accounted for only 5% of practicing physicians and 4% of medical school faculty. This lack of diversity can result in a lack of cultural competence and bias in healthcare delivery, which can lead to poorer health outcomes for black patients. For example, healthcare providers who are not culturally competent may not understand the unique health risks and health-seeking behaviors of black patients. They may also be less likely to provide appropriate preventive care, such as cancer screenings or vaccinations. Moreover, bias in healthcare delivery can manifest in several ways, such as in the form of stereotyping, discrimination, or the lack of attention paid to a patient's concerns. This can result in delayed or inadequate medical treatment, leading to worsened health outcomes for black patients. Addressing the lack of diversity in the healthcare workforce requires a multifaceted approach. It involves increasing the recruitment and retention of black individuals in healthcare professions, particularly in leadership positions. It also requires the development of cultural competency training programs for healthcare providers to ensure that they are equipped to provide high-quality care to diverse patient populations. Furthermore, black patients are more likely to experience discrimination and bias from healthcare providers, leading to a lower quality of care and poorer health outcomes. Studies have shown that black patients receive less pain medication and fewer diagnostic tests than white patients, even when they have similar symptoms. This discrimination can lead to delayed or inadequate medical treatment, which can worsen health outcomes.

Systemic racism has plagued healthcare systems across the globe for decades, posing significant consequences for public health. One of the critical manifestations of this is the presence of racial and implicit biases in medical decision-making. These biases can impact how medical professionals perceive and treat patients, often resulting in poorer health outcomes for people of color. The root of these biases is multifaceted and can stem from a variety of sources, including historical and societal factors that have perpetuated stereotypes and implicit biases. Moreover, healthcare providers are also receptive to the larger cultural biases and prejudices that prevail in society. This makes it crucial to identify and address these biases, both at the individual and systemic levels. The real-life effects of racial biases in medical decision-making can be devastating and can lead to potentially fatal consequences such as misdiagnoses, delayed treatment, and higher mortality rates for patients of color.

In recent times, significant efforts have been made by public health organizations and institutions to identify and expose the biases present in the world of healthcare. A recent study conducted by public health researcher Ziad Obermeyer and his colleagues at the University of Berkeley revealed that a clinical algorithm utilized by several hospitals to manage care priorities generated racially biased decision-making. The researchers used a machine learning model to analyze healthcare data from over 6,000 patients in a single healthcare network. The results revealed that the algorithm was much more likely to predict that white patients had higher medical needs than patients of color, even when both patients had the same health conditions or symptoms. Consequently, black individuals were less likely to be recommended for programs that offer more personalized medical care. Ironically, upon further analysis of the data, Obermeyer and his team discovered that on average, black individuals had a significantly higher prevalence of health conditions, including diabetes, anemia, kidney failure, and high blood pressure, in comparison to white individuals. This finding is alarming, especially given that hospitals and insurers across the United States use this algorithm and similar tools to manage care for approximately 200 million people each year. Interestingly, the data also collectively revealed that healthcare services offered to black individuals, who had the same number of chronic health issues as their white counterparts, were on average $1,800 less per year in cost. In essence, the algorithm's biases resulted in black individuals being recommended for less personalized and intensive care programs, which then led to reduced frequency and lower-cost medical interventions which may not have necessarily resulted in the optimal care. This systemic bias and neglect of black individuals' health evidently lead to worse health outcomes, creating a vicious cycle of underfunded and inadequate healthcare for this minority.

According to sociologist Ruha Benjamin from Princeton University, biases present in algorithms stem from a lack of diversity among their designers, coupled with inadequate training in the social and historical context of their work. She emphasized that healthcare systems should not solely depend on the current algorithm designers to anticipate or address all the potential harms linked with automation. The presence of bias in algorithms poses a significant and complex problem that stems from the deeply ingrained systemic racism present in our country. In order to effectively tackle the issue of racism perpetuated by machines, it is essential that we first address the underlying issue in human society. Addressing racism in healthcare is an urgent and complex problem that requires a holistic approach. Fortunately, there are promising solutions to this. One potential solution is to increase diversity and representation in the healthcare industry, including in leadership positions, to ensure that marginalized communities have a voice in decision-making processes. Additionally, implementing anti-bias training for healthcare professionals can help to mitigate implicit bias in patient care. One example of this would be incorporating cultural competence into medical education and practice which would promote understanding and respect for diverse cultural backgrounds and practices. It is also important for healthcare systems to acknowledge the profound impact of systemic racism on the actual treatment of ethnic minorities, which frequently translates to suboptimal care outcomes. One strategy to combat this is to actively collect and analyze data on health disparities among different racial and ethnic groups, which can help identify gaps in care and inform targeted interventions. Additionally, leveraging technology, such as machine learning algorithms and data analytics, with a conscious focus on diversity and inclusion can help identify and address healthcare disparities. Overall, it is crucial to take a comprehensive approach to combat racism in healthcare and work towards a more inclusive healthcare system. By taking these steps, we can work towards a more equitable and just healthcare system for all.

Akinyemiju, T., Deveaux, A., Wilson, L., Gupta, A., Joshi, A., Bevel, M., Omeogu, C., Ohamadike, O., Huang, B., Pisu, M., Liang, M., McFatrich, M., Daniell, E., Fish, L. J., Ward, K., Schymura, M., Berchuck, A., & Potosky, A. L. (2021). Ovarian Cancer Epidemiology, Healthcare Access and Disparities (ORCHiD): methodology for a population-based study of black, Hispanic and white patients with ovarian cancer. BMJ Open, 11(10), e052808–e052808. https://doi.org/10.1136/bmjopen-2021-052808

Canedo, J. R., Miller, S. T., Schlundt, D., Fadden, M. K., & Sanderson, M. (2018). Racial/Ethnic Disparities in Diabetes Quality of Care: the Role of Healthcare Access and Socioeconomic Status. Journal of Racial and Ethnic Health Disparities, 5(1), 7–14. https://doi.org/10.1007/s40615-016-0335-8

Hall, W. J., Chapman, M. V., Lee, K. M., Merino, Y. M., Thomas, T. W., Payne, B. K., Eng, E., Day, S. H., & Coyne-Beasley, T. (2015). Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: A systematic review. American Journal of Public Health, 105(12). https://doi.org/10.2105/ajph.2015.302903

Hostetter, M. (2021, October 18). Confronting racism in health care. Proclamations to New Practices | Commonwealth Fund. https://www.commonwealthfund.org/publications/2021/oct/confronting-racism-health-car e

Kara Manke| October 24, 2019December 2, & Manke, K. (2019, December 2). Widely used health care prediction algorithm biased against Black people. Berkeley News. https://news.berkeley.edu/2019/10/24/widely-used-health-care-prediction-algorithm-biase d-against-black-people/

Lee, N. T., Resnick, P., & Barton, G. (2022, March 9). Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms. Brookings. https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practi ces-and-policies-to-reduce-consumer-harms/

Lightman, E., Mitchells, A., & Wilson, B. (2008). Poverty is making us sick: A comprehensive survey of income and health in Canada. In Wellesley Institute. https://www.wellesleyinstitute.com/wp-content/uploads/2011/11/povertyismakingu ssick.pdf

National Academies’ Institute of Medicine. (2002). Minorities More Likely to Receive Lower-Quality Health Care, Regardless of Income and Insurance Coverage. https://www.nationalacademies.org/news/2002/03/minorities-more-likely-to-receive-lower -quality-health-care-regardless-of-income-and-insurance-coverage

Valenti, D. (2020, May 15). Benjamin’s “race after technology” speaks to a growing concern among many of tech bias. Princeton University. https://www.princeton.edu/news/2020/05/15/benjamins-race-after-technology-speaks-gro wing-concern-among-many-tech-bias

Wilson, C., Alam, R., Latif, S., Knighting, K., Williamson, S., & Beaver, K. (2012). Patient access to healthcare services and optimisation of self-management for ethnic minority populations living with diabetes: a systematic review. Health & Social Care in the Community, 20(1), 1–19. https://doi.org/10.1111/j.1365-2524.2011.01017.x

Yale Alumni Publications, Inc. (n.d.). Yale Alumni Magazine: black men in 1960s Yale College (May/June2011).http://archives.yalealumnimagazine.com/issues/2011_05/feature_howell .html