On March 10th I had the distinct privilege of presenting to the ONC Health IT Advisory Committee at their meeting titled “Health Equity by Design.” The presentation highlighted five areas of focus for Health IT in reducing health disparities: health equity data collection, actionable analysis of the data, integration of healthcare and social service providers, the special role that Health Information Exchanges (HIEs) can play, and access to healthcare and technology. Under the maxim that “you can’t manage what you can’t measure,” reducing health disparities begins with the standardized collection of health equity data. The COVID-19 pandemic has brought to light dramatic gaps in even the simplest health equity data collection. For instance, at the first peak of the pandemic only 24 states had reported the race and ethnicity of people who died from COVID-19, and during the first month of vaccine distribution, these data were missing for almost half of the doses delivered even though it was required. The Gravity Project is a good example of how we can expand standardized data sets in order to systematically capture critical information about social determinants of health (SDOH) and other demographic data needed to inform actions intended to achieve health equity. As these data are captured, implementation of a clear privacy framework will be critical to building trust with currently underserved populations. Even as we work to expand a health equity database, it will be important to evolve requirements for capture of clinical data so as to minimize the burden of data collection on practicing clinicians. A rapid movement away from specific clinical metrics to patient-reported outcomes could go a long way toward reducing administrative burden on providers even as it drives a deeper understanding of health disparities. Once adequate data is available, it must be compared to clinical and health outcomes data to identify disparities. NextGen Healthcare and other Health IT vendors offer many tools for analyzing demographic and other health equity data alongside health conditions and outcomes.Common analyses include geospatial comparisons (to “map” health disparities in a community), location vs location comparisons and benchmarking, quality improvement project before and after analyses, risk stratification, and predictive analytics.