Just ten hours after giving birth to her second child via C-section, Kira Johnson died of haemorrhagic shock at Cedars-Sinai hospital, surrounded by some of the best medical care providers in the world. Why did Kira bleed to death after a routine surgery, despite constant care around the clock? After her husband, Charles Johnson IV, filed a complaint over her treatment, the Biden administration launched a civil rights investigation. On January 16th, 2025, the US Department of Health and Human Services (HHS) reached a settlement with Cedars-Sinai outlining specific steps to protect Black patients against discrimination and adhere to best practices in maternal care. 5 days later, a new administration issued an executive order banning precisely the sort of enforcement of civil rights that the HHS settlement exemplifies. But as Amani Echols writes in Ms. Magazine’s coverage of the case, “Kira’s story is not an anomaly. Black birthing people are 3.5 times more likely to die from a pregnancy-related cause than white women, regardless of education or income.” Furthermore, these deaths are avoidable. In fact, more than four out of every five maternal deaths in the United States is entirely preventable, and that number goes up when considering patients of color. For example, as a CDC report concludes, “Most pregnancy-related deaths of [American Indian/Alaska Native] AI/AN people (93%) were determined to be preventable.” The stakes in January’s executive order and the ensuing prohibition of diversity, equity, inclusion, and belonging actions could not be higher; Johnson’s case and Echols’ analysis show the bias at play is a matter of life and death.
In this blog post, I outline a system by which to understand bias in the medical field and apply it to five different forms of medical bias: medical sexism, medical classism, medical racism, medical ableism, and medical weightism within the lens of precision medicine. I argue that these forms of medical bias need to be systematized and considered as whole as well as singly and that doing so can help us oppose this discrimination.
In my last book, I argued that medical sexism is a gendered hierarchy in the medical field. This definition abstracted away from both agents and patients in the identification of bias. It abstracts away from agents, since intentions, motivations, and specific actions are not required for the identification of medical sexism. This shift allows us to identify bias in institutions, systems, policies, rules, environments, and even medical equipment. Disproportionate impact, then, becomes the key feature of bias. The definition of medical sexism also abstracts away from patients, since women are not the only ones who experience medical sexism or sexism generally. Not only may other individuals with a uterus or breasts find themselves subject to medical sexism, but also any individual exhibiting characteristics associated with femaleness or femininity, regardless of their gender identity or physical characteristics.
If medical sexism is a gendered hierarchy, then what other hierarchies can we examine in medicine? Medical classism is a class-based hierarchy in health care in which higher socioeconomic status is placed above lower socioeconomic status in the delivery of medicine. Medical racism is a race-based hierarchy in which white racial identities are placed above minoritized racial identities, leading to the sort of care that Johnson experienced and that white pregnant patients are three times less likely to experience. Medical ableism is the hierarchy of abilities over disabilities in health care. Medical weightism is a weight-based hierarchy placing low body weights over higher ones in the medical field. These forms of oppression overlap, intersect, and give insight on each other through their definitions. Though I cannot cover every form of medical bias, I hope this analysis establishes a methodology by which to define, interpret, and oppose forms of medical bias. In each case, a hierarchy is upheld in the medical field, and in each case, that hierarchy highlights a socially constructed characteristic. I aim to examine these hierarchies in more detail in my next book by considering the ethics of precision health because the sort of medicine which targets these groups can be understood through the lens of precision health.
Identifying and systematizing an understanding of these hierarchies is the first step towards eliminating them. But it is also necessary to argue against these hierarchies. If in each case, the characteristic held in a hierarchy is socially constructed, then it is also morally irrelevant to uphold a hierarchy and unjustified in application.
Of course, that does not mean that the socially constructed categories are irrelevant to health care entirely. After all, we only know about these biases through research that examines socially constructed categories to analyze differential care. We can and ought to seek to understand the hierarchies themselves and to identify the biases occurring in the delivery of medicine. And, we can seek to better understand and respond to the role that stigma, discrimination, and bias play in individuals’ health through the social determinants of health. But, when treatment and care turn on these socially constructed characteristics, we often run the risk of upholding the hierarchy rather than breaking it down.
For example, it is tempting to say that due to the higher maternal mortality rate found in minoritized populations in the United States, that we ought to treat those patients differently in the delivery room. In fact, that perspective arguably was in play when the VBAC Calculator (the algorithm developed to determine the likelihood of success of a vaginal birth after Caesarean), until 2022, incorporated race and ethnicity data into the measurement tool to achieve precision health outcomes tailored to the individual patient. The entirely preventable maternal mortality rate that exists as a symptom of bias was then used to deny patients access to VBACs that non-Hispanic white patients would have had the opportunity to try. Thus, attempts to be adequately responsive to discriminatory care can end up compounding that discrimination. Closer attention therefore ought to be paid to when socially constructed characteristics are being used for research and when they are being used as proxies for other meanings.
If we do better in this regard, not only can we prevent tragic deaths like Kira Johnson’s, but also, we can improve the research and delivery of precision medicine throughout the medical field.
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