What is it?
Data is often considered an objective source of truth. But there are underlying issues in health care data that can lead to skewed inferences and decisions.
1. Incomplete data: Gaps in data prevent us from having a holistic view of a patient or population. Marginalized groups may experience more fractured care and less documentation of conditions and outcomes. Demographics and social determinants of health (SDOH) influence outcomes but aren’t routinely captured in systems. Only one-third of commercial plans reported having complete or partially complete data on race, a pattern that is likely reflected in electronic health records (EHRs).
2. Small sample size: Marginalized populations are not adequately represented in health care data. More data is available for those that are able to access care and treatments, and “data deserts” exist for groups that experience systemic barriers to accessing care. Underrepresentation in data can lead to less informed care decisions or flawed inferences about a population.
3. Historical inequities embedded in data: Health care outcomes are not the same across populations. For example, black women are 42% more likely to die from breast cancer. This can be at least partially attributed to factors like a higher burden of comorbidities and barriers to accessing care that stem from the enduring legacies of structural racism and intergenerational poverty. Black women are also more likely to be diagnosed at later stages of the disease and experience delays in treatment of two or more months. These types of inequitable outcomes are baked into health care data.