Systemic racism in America impacts health and wellness in many different ways, from poverty rates to insurance access to quality of care received by healthcare institutions. The COVID-19 pandemic has illustrated alarming disparities in healthcare outcomes by race/ethnicity. 80% of deaths from COVID-19 in the U.S. have been of Black individuals, while only 13.4% of the U.S. population is Black. There are other examples of disparities in mortality rates, such as the maternal birth mortality rate, which is 3x higher for Black women than White women, regardless of education and income levels. Furthermore, studies have shown that ER wait times are on average longer for Black individuals than White ones for the same severity of condition.

Beyond socioeconomic factors of health, these statistics indicate unequal quality of care based on race. This is an informatics challenge because there need to be deep assessments of all metrics of quality of care, clinical decision-making, and physician biases in order to analyze the root causes of these disparities and to eliminate them from the healthcare system. While there has been plenty of data collected over the past decades indicating the disparities in health outcomes by race/ethnicity, now with the adoption of EMRs, the amount of data that relates to this issue can vastly expand.

Since EMRs track every episode of care, appointments, and service that occurs, all of these pieces of care can be analyzed to determine the reasons why quality of care is not equal for everyone in America.  Once the causes have been identified, technology systems can facilitate educational programs to mitigate bias or to document/prevent racial bias from impacting care outcomes. Policies will also be able to evolve that penalize health systems for metrics such as disparate ER wait times. In addressing the many layers of impacts of systemic racism on U.S. healthcare, I expect that informatics can play a large role in identifying problems and ultimately, preventing them.