This perspective examines what is lost when race and ethnicity data are not adequately collected or analyzed. It highlights the importance of these data for identifying disparities, informing the design of effective interventions, and ensuring that clinical trials achieve meaningful and equitable impact. It also discusses how the absence of such data can limit the broader applicability and effectiveness of research findings. The conclusion is that when high-quality data are available and analyzed thoughtfully, they enable the identification of inequities, illuminate underlying mechanisms, guide targeted interventions, support accountability, and ultimately improve the health of all Americans.

This paper introduces a special issue examining data infrastructure for patient-centered outcomes research that addresses health equity. Addressing the persistence and breadth of health inequalities in the United States requires ongoing expansion of the capacity to detect, monitor, and understand them. Data infrastructure is itself an equity policy matter: decisions about what data to collect, on whom, and in what form determine whose health inequities can be observed and, consequently, addressed. The 9 research papers in this issue are organized around 4 themes. The first examines new data sources and standards that extend what health systems can observe about the populations they serve. The second demonstrates how linking data across sectors enables analysis of social determinants that clinical records alone cannot capture. The third addresses strategies for disaggregating populations that are small, internally heterogeneous, or historically excluded from data collection. The fourth considers implementation-the organizational and institutional conditions under which new data systems are successfully adopted in practice. Together, the papers illustrate both the scientific possibilities created by deliberate investment in data capacity and the policy stakes of sustaining it.