Synthetic Data and Health Equity: Accounting for Racism and Sexism in Health Care Delivery

Introduction Synthetic data are a promising new tool for answering health service research questions, including those relevant to health equity. However, it is unclear whether synthetic data can accurately capture inequities in health care, which may perpetuate racial and ethnic health inequities when applied to the real world. Methods In this study, we determine to what extent Synthea, a popular open-source synthetic electronic health record (EHR) data generator captures racial, ethnic and sex disparities in clinical practice, and evaluate whether the data can be augmented by other publicly available data sources. We examine rates of intervention for three common medical conditions – myocardial infarction (MI), chronic obstructive pulmonary disease (COPD), and type II diabetes mellitus (T2DM). Results For two of the three conditions, Synthea data showed higher rates of intervention for all patients, and attenuated or no disparities in intervention, versus comparator literature. After incorporating data on race, ethnicity and sex disparities from the Dartmouth Atlas, updated Synthea proportions approached their literature counterparts in both absolute and relative terms. Conclusion If using synthetic data, researchers and policymakers can work to ensure such data accurately reflects downstream effects of social forces in order to mitigate inadvertent harm to minoritized populations.