PART TWO OF A TWO-PART SERIES

Last week, I began to explain the future of health disparities after COVID-19 in Part I of this series by highlighting what the world has witnessed during this pandemic. Feel free to read Part I here.

This week, I will lay the groundwork on reimagining a healthcare system that works to reduce healthcare disparities through health equity, and begin to describe what that world would look like. I do contend that one of the leading solutions to achieve these objectives is to truly unharness the power of ethnic concordance.

Ethnic Concordance

A 2018 study in Oakland, California found that Black/African American patients in ethnically concordant physician/patient relationships were more likely to agree to the preventive care services offered up by their physician as compared to if they were in a discordant relationship. (Alsan, Garrick, Graziani, 2018). Concordance is defined as “the degree of patient and physician similarity or agreement across a given dimension. Sharing specific social characteristics (e.g. gender, race, socioeconomic status, education), expectations, beliefs, and perceptions impact health care quality” (Thornton, Powe, Roter, Cooper, 2011).

The theory builds on work from Dr. Theodis Thompson’s Social Accessibility Hypothesis, which contends that “physicians find it very difficult to effectively communicate with their patients, especially when there are cultural differences. On that premise, the psychosocial accessibility problem of blacks obtaining healthcare would be greatly alleviated through the existence of an appropriate number of black physicians to meet the black demand for healthcare services” (Thompson, 1974).

There are many research studies that show ethnically concordant physician/patient relationships lead to greater patient satisfaction scores, longer physician-patient talk times, more physician effort, and higher adherence rates (LaVeist & Nuru-Jeter, 2002; Street, O’Malley, Cooper, Haidet 2008; Hagiwara, Penner, Gonzalez, Eggly, Dovidio, Gaetner, West, Albrecht, 2013). However, there are seemingly an equal amount of studies that also find little to no statistical significance in health outcomes between ethnically concordant physician visits too (Traylor, Subramanian, Uratsu, Mangione, Selby, Schmittdiel, 2010; Stevens, Shi, Cooper, 2003; Schoenthaler, Montague, Manwell, Brown, Schwartz, Linzer, 2014).

However, the one similarity between these many research studies that find no correlation between ethnic concordance and patient outcomes has more to do with endogeneity than simply a lack of correlation. That is, many research studies in concordance fail to include one very important variable in their regression analyses. A variable, I contend, is the most important variable for this type of research in apost-George-Floyd-America; implicit bias. 

Implicit Bias

Implicit bias is defined as “associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender” (FitzGerald & Hurst, 2017).

A 2017 study by the Children’s Hospital of Philadelphia found that 91% of their residents had pro-white/anti-black bias against black children and 85% bias against black adults. These biases led to poorer physician communication, disparities in medical treatment, and lower adherence to treatment recommendations (Johnson, Winger, Hickey, Switzer, Miller, Nguyen, Saladino, Hausmann, 2016).

Once implicit bias is included as a variable in research models focusing on ethnic concordance, statistical significance in health outcomes begin to emerge. A 2018 meta-analysis that reviewed 6,249 research studies found that implicit bias absolutely plays a role in continuing healthcare disparities (Maina, 2018). 

Since implicit bias exists in our healthcare system, it also exists in the data we use to treat our patients. This has become clear to even New York regulators who have begun to conducted investigations into algorithms that may have been foundationally built upon bias data, which led to a perpetuation and exacerbation of bias care for Black/African American and Latinx patients (Evens, Mathews, 2019; Obermeyer, Powers, Vogeili, Mullainathan, 2019). 

The Four Core Recommendations for the Future

We are at a crossroads in America. The longevity and wellbeing of millions of minority Americans hang in the balance. A near-term Presidential election looms over us as over $230 billion dollars in health care savings await to be realized. COVID-19 has galvanized and coalesced an entire industry to draw a line in the sand and commit to meaningful changes that will transform the trajectory of patient care forever. This momentum will last so long as our industry wants it to. The future is up to us.

As a result, here are my four core recommendations for the future health equity commitments post COVID-19:

1)     Invest more into Concordant Research

Our industry needs to conduct more studies focused on ethnic concordance with the inclusion of racial implicit bias as a measurable variable, so we can learn more and do more around this important topic.

a.      Some disease states, like Lupus, lack information around concordance all together (Delis, Corless, Young, Hildebrand, Bell, Tarbet, 2020)

b.      Not all ethnicities, like Asian populations or English-speaking Hispanics, response the same to ethnic concordant physician/patient relationships (Traylor et al., 2010). 

2)     Increase Physician Workforce Diversity with clear Targets and Incentives

Diversity is the great equalizer to improving disparities. Most patients are more likely to choose ethnically concordant physicians regardless of ethnicity (Traylor et al., 2010) however, Black/African American and Hispanic patients are the least likely to have ethnically concordant options (Cooper & Powe, 2004). For example, as Dr. Thomas discussed in his research, in 1942 the ratio of black physicians to the black population was one to 3,377, and by 1976 it fared even worse; one to 4,000. As of 2018, approximately 45,000 Black/African American physicians only make up 6.8% of all physicians in the United States (Association of American Medical Colleges, 2018) even though 42 million Black/African Americans living in the United States make up 13% of the population (U.S. Department of Health & Human Services, 2019). While minority physicians that are underrepresented in medicine are more likely to practice in underserved areas than their White peers, internists had much higher rates of diversity as compared to family physicians (Xierali, I., Nivet, M., 2018).

3)     Create Data & AI Ethic Committees

As the move to automation grows, more companies are relying on data science to be the sail that helps steer the strategic ship of growth. However, an ethical framework is required when making decisions using big data, artificial intelligence, or machine learning to ensure that the sins of our past, inherent in the data we are using, do not become the foundations for our future. (Sandler, Basl, Tiell, 2019).           

4)     Work with Legislatures and Policy Makers to Undo Systemic Racism & Bias

Even though healthcare is close to one-fifth of our gross domestic product (Martin, Hartman, Washington, Catlin, 2018), Health equity will never be achieved through this industry alone. As Hooper et al. (2020) noted “the underlying causes of health disparities are complex and include social and structural determinants of health, racism and discrimination, economic and educational disadvantages, health care access and quality, individual behavior, and biology”. Thus, policy makers must invest in areas that influence the social determinants of health from education, to housing, to job training, in order to help the healthcare sector achieve its goal of an equitable healthcare system that leads to long life and wellbeing, irrespective of your zip code or the color of your skin.

——–

References

Alsan, M., Garrick, O., Graziani, G., (2018). Does Diversity Matter for Health? Experimental Evidence from Oakland. National Bureau of Economic Research. Working Paper No. 24787.

Association of American Medical Colleges (AAMC), (2018). Percentage of all active physicians by race/ethnicity, 2018. Diversity in Medicine: Facts and Figures 2019. Retrieved on July 29, 2020 from https://www.aamc.org/data-reports/workforce/interactive-data/figure-18-percentage-all-active-physicians-race/ethnicity-2018

Cooper, L., Powe, N., (2004). Disparities in Patient Experiences, Health Care Process, and Outcomes: The Role of Patient-Provider Racial, Ethnic, and Language Concordance. The Commonwealth Fund. 

Delis, P., Corless, I., Young, I., Hildebrand, M., Bell, J., Tarbet, A., (2020). Does patient-provider race/ethnicity concordance impact outcomes for adults with lupus?. Journal of Health Disparities Research and Practice: Vol. 13: Issue 1 , Article 5.

Evens, M., Mathews, A., (2019). New York Regulator Probes UnitedHealth Algorithm for Racial Bias. Wall Street Journal. Retrieved on July 29, 2020 from https://www.wsj.com/articles/new-york-regulator-probes-unitedhealth-algorithm-for-racial-bias-11572087601

FitzGerald, C., Hurst, S. Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics 18, 19 (2017)

Hagiwara, N., Penner, L. A., Gonzalez, R., Eggly, S., Dovidio, J. F., Gaertner, S. L., West, T., & Albrecht, T. L. (2013). Racial attitudes, physician-patient talk time ratio, and adherence in racially discordant medical interactions. Social science & medicine (1982), 87, 123–131.

Johnson, T. J., Winger, D. G., Hickey, R. W., Switzer, G. E., Miller, E., Nguyen, M. B., Saladino, R. A., Hausmann, L. R. (2016). Comparison of Physician Implicit Racial Bias Toward Adults Versus Children. Acad Pediatr.

Laveist, T. A., & Nuru-Jeter, A. (2002). Is doctor-patient race concordance associated with greater satisfaction with care?. Journal of health and social behavior, 43(3), 296–306.

Maina, I., (2018). A Systematic Review of Implicit Racial Bias in Healthcare. Pediatrics January 2018, 141

Martin, A., Hartman, M., Washington, B., Catlin, A., (2018). National Health Care Spending In 2017: Growth Slows To Post–Great Recession Rates; Share Of GDP Stabilizes. Health Affairs. Vol. 38, No. 1.

Sandler, R., Basl, J., Tiell, S., (2019). Building Data and AI Ethics Committees: Executive Summary + Full Report. Accenture & Northeastern’s Ethics Institute.

Schoenthaler, A., Montague, E., Manwell, L., Brown, R., Schwartz, M., Linzer, M., (2014) Patient–physician racial/ethnic concordance and blood pressure control: the role of trust and medication adherence, Ethnicity & Health, 19:5, 565-578,

Stevens, G., Shi, L., Cooper, L., (2003). Patient-Provider Racial and Ethnic Concordance and Parent Reports of the Primary Care Experiences of Children. The Annals of Family Medicine July 2003, 1 (2) 105-112.

Street, R. L., Jr, O’Malley, K. J., Cooper, L. A., & Haidet, P. (2008). Understanding concordance in patient-physician relationships: personal and ethnic dimensions of shared identity. Annals of family medicine, 6(3), 198–205.

Thompson, T., (1974). Curbing the Black Physician Manpower Shortage. Journal of Medical Education. Pages 944-50.

Thornton, R. L., Powe, N. R., Roter, D., & Cooper, L. A. (2011). Patient-physician social concordance, medical visit communication and patients’ perceptions of health care quality. Patient education and counseling, 85(3), e201–e208.

Traylor, A. H., Subramanian, U., Uratsu, C., Mangione, C., Selby, J., Schmittdiel, J., (2010). Patient Race/Ethnicity and Patient-Physician Race/Ethnicity Concordance in the Management of Cardiovascular Disease Risk Factors for Patients with Diabetes. Diabetes Care 2010 Mar; 33(3): 520-525.

Traylor, A. H., Schmittdiel, J. A., Uratsu, C. S., Mangione, C. M., & Subramanian, U. (2010). The predictors of patient-physician race and ethnic concordance: a medical facility fixed-effects approach. Health services research, 45(3), 792–805.

U.S. Department of Health and Human Services. Office of Minority Health. (2019). Profile: Black/African Americans. Retrieved on August 05, 2020 from https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=61

Xierali, I. M., & Nivet, M. A. (2018). The Racial and Ethnic Composition and Distribution of Primary Care Physicians. Journal of health care for the poor and underserved, 29(1), 556–570.