Addressing bias in health care

COVID-19 has highlighted stark health disparities in the United States, particularly along the lines of race and ethnicity. For example, individuals who identify as American Indian, Alaska Native (AIAN), Latinx, or Black, are more than twice as likely to be hospitalized from COVID-19 compared with white people, and almost twice as likely to die.² While COVID-19 research is ongoing, initial studies show that racially and ethnically diverse individuals aren’t more susceptible to the virus and they don’t face worse health outcomes because of their race. Instead, poor outcomes can be attributed to factors associated with racism such as decreased access to care, living in multigenerational homes and crowded conditions, working in high-exposure environments, and the direct impact of discrimination, among many others.³

Although it’s often used to describe the prevalence of disease, race is not an underlying cause of health disparities. Racism is. Health disparities are evidence of systemic bias, deep inequities in the nonmedical drivers of health (DOH), and structural flaws in the health system. And these inequities affect both individual and community health and well-being and can be compounded through systemic biases in clinical algorithms and technologies. 

Research methodology: To better understand the steps stakeholders are taking to improve health equity and address bias in their data, diagnostic algorithms, and technologies, the Deloitte Center for Health Solutions (DCHS) interviewed 19 industry experts in health equity. DCHS also conducted an extensive secondary literature review to understand the strategies that health systems are deploying and the barriers they’re overcoming. 

Findings: Interviewed experts expressed that systemic racial bias in medicine stems from the historically incorrect concept of race which was originally developed as a system of hierarchical human categorization. This led to the notion that white people were superior based on race alone. Medical education and clinical guidelines have unintentionally continued to reflect the antiquated notion that race is a biologically valid distinction among individuals, rather than socially constructed. While there are many types of bias, including gender, disability, sexual orientation, and language, to name a few—and the intersections between these groups (e.g., a Black woman whose first language isn’t English) increase the risk for bias—our research focused specifically on race and ethnicity. 

Some health systems have already begun to address these systemic biases in clinical care through implicit bias training and changes in medical education. These hospitals are driven by their missions as well as the opportunity to improve outcomes and quality, increase profit margins, rebuild patient trust, respond to policymakers, and embed this work within their environmental, social, and governance (ESG) initiatives.

Understanding that race has been socially constructed and has no basis in biological differences is another way health systems can activate health equity in their communities. Interviewed experts expressed the need to address this issue holistically at all levels of care delivery. Health systems can take the following approaches to the insertion of biologic race in medicine and biases that stem from it by:

• Implementing strategies for data granularity and standardization: Developing standards for data collection can help health systems better understand their patient populations and the health challenges that need to be addressed. Health systems should consider expanding the types of data they collect to include race, ethnicity, preferred language, and DOH. In addition to claims data, health systems can use new datasets like employment data, and leverage nontraditional and community partnerships.
• Developing metrics for proper data collection and use: In addition to establishing data standards, developing measures and metrics for proper data collection and use is vital. Health systems should consider educating and training providers about why this data is important to collect, how to talk to patients about the importance of sharing this data in a culturally humble and empathetic way, and how to develop scorecards and health equity indicators (HEIs). Understanding when to use social race and ethnicity data, and when not to—even when it’s available—will likely be key to understanding the root causes of health and how to determine an appropriate care plan (this also applies to areas outside of direct care delivery).
• Reevaluating clinical algorithms: Reexamining long standing clinical algorithms, including care pathways and workflows, can help health care systems ensure all patients receive the care they need. Health systems should consider forming designated teams to evaluate algorithms and assess which clinical algorithms are currently being used in their facilities, how race is used in the algorithm or calculation, and whether race is justified. Scrutinizing existing practices in a new light can ensure that  the intention of using said racial adjustment is consistent with the desired impact (i.e. closing racial disparity gaps and improving outcome measures for all); if not, then race should be removed. This approach can help determine what underlying factors are driving differences in health outcomes and which should be included in the algorithm. 
• Conducting regular audits:As the use of artificial intelligence (AI), medical devices, and other technologies increase, continually testing for bias will be critical to ensuring health disparities aren’t unintentionally exacerbated. Health systems should consider conducting regular audits of their AI systems to check for bias and re-evaluate their current tools and devices by considering if other screening tools should be used, reviewing their vendors, and understanding the diversity of the clinical trials that devices were tested on.

Addressing systemic bias in health care delivery

As the COVID-19 pandemic and ongoing social injustices continue to spur health care organizations to address equity, there have been increased calls to address systemic discrimination and bias in health care delivery.4 While many organizations are focused on solving biases that stem from individual clinicians,⁵ there also is a need to address the systemic biases within the health care ecosystem infrastructure that stem from using race as a factor in medical decision-making. The lack of data standards and accountability around race and ethnicity, the misuse of racial and ethnic data to inform diagnosis and treatment plans, the implementation of algorithms that account for race factors based on biased data, and the use of technologies that exacerbate these issues are examples of systemic biases.

Continuing to ignore and perpetuate these biases could result in increased health care costs, poor quality of care for both individuals and communities, decreased patient trust, and growing disparities in preventable poor health outcomes. Focusing on racism, rather than race, as a determinant of illness is pivotal to activating health equity but will likely require health care systems to rethink when and how to use race appropriately in care delivery.

To learn how organizations are tackling the issue of bias in care delivery and improve health equity in their health care data, algorithms and technologies, the Deloitte Center for Health Solutions (DCHS) interviewed 19 industry experts, including chief diversity, equity, and inclusion officers at health systems, former CEOs of health systems, academics, medical directors, and chief medical information officers. These interviews helped us to better understand the insertion of race in medicine today and the steps stakeholders are taking to improve health equity in their data, diagnostic algorithms, and technologies. DCHS also conducted an extensive secondary literature review to understand the barriers and identify the strategies organizations are deploying in this space.