Just Medicine: A Cure for Racial Inequality in American Health Care

Chapter 3 of Just Medicine discusses how unconscious racism in healthcare harms patients while increasing costs. Implicit bias leads to unequal treatment, such as withholding specialist referrals for BIPOC patients. Despite numerous laws prohibiting intentional discrimination, Matthew states that unconscious bias remains unaddressed from a legal perspective.

In the first section of the chapter, titled “The Health Impact of Physicians’ Bias,” Matthew discusses the 2003 Institute of Medicine (IOM) report, which shows that BIPOC patients in the US receive inferior healthcare compared to white patients. This report, based on over 100 studies, revealed that BIPOC patients are less likely to receive appropriate treatment for diseases such as cardiovascular disease, cancer, and diabetes, among others. Even when controlling for factors like socioeconomic status and access to care, race and ethnicity significantly influence treatment disparities.

Matthew gets specific in her discussion of racial disparities in the treatment of heart disease, renal disease, and cancer. For example, African Americans are more likely to develop cardiovascular disease but receive fewer treatments and less preventative care than whites. In cases of end-stage renal disease, BIPOC patients are less likely to be informed about or referred for kidney transplants. Furthermore, cancer treatment disparities result in worse survival rates for BIPOC patients, largely because they are less likely to receive timely diagnoses and optimal treatments.

Matthew highlights several studies that explore how race and gender influence medical treatment decisions. Firstly, she presents a 1999 study conducted by Dr. Kevin Schulman, which examines how 720 physicians responded to video vignettes of patients who differed only in race and gender. Schulman found that white male patients were more likely to receive recommendations for catheter cardiac diagnosis than Black or female patients. Matthew also references a 2007 study by Dr. Alexander Green, who expanded Schulman’s findings by using the Implicit Association Test (IAT) to measure physicians’ unconscious racial biases. Green’s study shows that physicians exhibited similar implicit bias against Black patients as the general population, and that the bias influenced clinical decisions. Notably, the physicians expressed no explicit racial bias, which proved the unconscious nature of these preferences. Green’s research also demonstrates that when physicians were made aware of their implicit biases, they corrected their behavior, which indicates that such biases can be addressed in real-world medical settings.

Matthew also discusses Dr. Janice Sabin’s study on pediatricians, which shows that, while implicit pro-white bias was lower among pediatricians in the study, it influenced pain medication prescriptions. Moreover, Dr. Gordon Moskowitz’s 2012 study reveals that physicians unconsciously associate conditions linked to behavioral choices like HIV and drug abuse with African Americans. The study shows that physicians responded to subliminal cues related to race and gender, which influenced their quick association of these conditions with African American patients.

Matthew cites an additional study carried out by Dr. Adil Haider at Johns Hopkins Medical School, who found that first-year medical students, just like senior doctors, held implicit bias favoring white patients. However, unlike their seniors, the bias did not affect the students’ clinical judgments. This study raises questions about the exact moment in a physician’s clinical practice when bias starts influencing medical decisions.

The final section of Chapter 3, titled “Hypertension and Other Counterfactuals,” presents Dr. Irene Blair’s 2012 study exploring the link between physician implicit bias and hypertension outcomes in Black, Latino, and white patients. Involving 138 physicians, the study found that while 70% of physicians showed implicit bias against Blacks or Latinos, this bias did not correlate with less care or attention to the patients. Blair suggests that strong patient-physician relationships in primary care may mitigate bias. However, the study did have significant limitations, such as the exclusion of patients who switched providers due to discrimination, and the focus on a single condition. Matthew concludes that further research is needed to fully understand bias in healthcare outcomes.

In Chapter 4 of Just Medicine, Matthew introduces the Biased Care Model as a response to questions posed by the Institute of Medicine’s 2003 report. The model identifies six mechanisms linking physician and patient bias to unequal health outcomes and aims to synthesize empirical data on disparities into a practical framework. Matthew explains how systemic pressures, uncertainty, and cognitive overload in healthcare exacerbate the effects of implicit bias. Her model offers a theoretical and practical guide to understanding and mitigating bias in medical decision-making and care delivery.

In the section “The Biased Care Model,” Matthew outlines the six mechanisms of the Biased Care Model, which affect physician-patient interactions before, during, and after clinical encounters. Mechanisms 1 and 2 describe how physicians’ biases and flawed data interpretation influence expectations before meeting patients. Mechanisms 3 and 4 address how bias affects communication during encounters, while Mechanisms 5 and 6 are concerned with impact decisions made after the visit. The model illustrates how implicit racial and ethnic bias overshadows biomedical or behavioral factors and the ways bias creates a cyclical influence throughout the care process.

The section “Bias before the Clinical Encounter” presents the first mechanism identified by the Biased Care Model, titled “Physicians’ Biased Perceptions Negatively Impact Minority Patient Health Outcomes” (79). This model outlines how physicians often enter clinical interactions with preconceived notions based on “stored social knowledge,” which are automatically triggered when they encounter patients from racial or ethnic groups different from their own (79). The bias can shape the quality of care BIPOC patients receive.

One example Matthew provides is the tendency of physicians to describe white patients in favorable terms as agreeable and easy to cooperate with, while they may unconsciously view BIPOC patients more negatively. This bias affects communication and patient interaction, leading to different care standards for different racial or ethnic groups. Matthew notes that even well-intentioned physicians who consider themselves egalitarian may exhibit implicit biases that manifest in ways they do not fully recognize. For instance, one emergency physician admitted surprise upon learning that a Black patient was the son of a prominent official—something that would not have surprised her had the patient been white.

The chapter further emphasizes that bias is not always explicitly malicious. Some caregivers believe that their implicit bias helps them better care for BIPOC patients by anticipating their needs. However, Matthew argues that such stereotypes—though seemingly benign—are harmful when they are based on race or ethnicity rather than medical relevance. For example, negative stereotypes about Black women, such as an association with promiscuous behavior, can lead to damaging medical recommendations. In one case, an African American woman seeking help with fertility was instead advised to undergo a hysterectomy, a procedure that was neither medically necessary nor desired.

Matthew also notes that implicit bias is often taught and reinforced during medical training. Physicians may develop negative perceptions about certain patient groups early in their careers based on their education and experiences. One doctor described how he was trained to view older Hispanic women as more likely to present with bodily complaints, which led to a bias in diagnosing and treating them. This pervasive stereotyping, passed down through generations of medical practitioners, can have far-reaching consequences for patient care.

Moreover, Matthew highlights that implicit biases are not limited to white physicians. Studies show that BIPOC physicians can also harbor biases against their own racial or ethnic groups. Matthew quotes a patient who describes this phenomenon through the lens of Paulo Freire: “those who have been oppressed themselves become oppressors” (90).

Matthew also discusses research that shows how younger medical professionals often develop implicit bias early in their training. She points to a study by Dr. Shelley White-Means, which followed preprofessional health students. The study reveals a significant, unconscious, pro-white bias among a majority of students. Notably, the implicit bias in the study was inversely related to the students’ self-assessed cultural competence. Medical students’ bias increased as they advanced in training, as they were influenced by their clinical environment and senior physicians.

Matthew explores the second mechanism of the Biased Care Model, titled “Physicians’ Implicit Biases Can Lead to Discriminatory Statistical Interpretation” (98). She notes that statistical discrimination occurs when physicians misinterpret accurate population data due to underlying bias, leading to poor medical decisions for individual patients. Matthew provides the example of an African American woman with fibroids who was dismissed by several gynecologists. These doctors relied on generalized statistics about the prevalence of fibroids among Black women and the supposed harmlessness of the condition, rather than addressing the woman’s severe pain and individual needs.

This mechanism illustrates how physicians often over-rely on population-based data, substituting it for personalized care. For instance, physicians may assume that African American women have a higher pain tolerance, leading them to underestimate their pain levels and misdiagnose their conditions. Similarly, a doctor may downplay a Black patient’s mental health concerns based on the lower statistical prevalence of depression in the Black population, disregarding the patient’s actual symptoms.

Matthew explains that this form of discrimination is particularly likely when doctors are pressured by time constraints or patient volume, prompting them to rely on biased shortcuts. Additionally, physicians may communicate less effectively with BIPOC patients, misinterpreting or dismissing their symptoms due to cultural or language barriers, further exacerbating the issue. Matthew underscores the importance of addressing these issues by improving doctor-patient communication and reducing reliance on population statistics that may not apply to individual cases, as even well-intentioned physicians may unintentionally perpetuate racial disparities in healthcare.