Context: Racial and ethnic disparities in the quality of health care are well documented in the U.S. health care system. Reducing these disparities requires action by health care organizations. Collecting accurate data from patients about their race and ethnicity is an essential first step for health care organizations to take such action, but these data are not systematically collected and used for quality improvement purposes in the United States. This study explores the challenges encountered by health care organizations that attempted to collect and use these data to reduce disparities.
Methods: Purposive sampling was used to identify eight health care organizations that collected race and ethnicity data to measure and reduce disparities in the quality and outcomes of health care. Staff, including senior managers and data analysts, were interviewed at each site, using a semi-structured interview format about the following themes: the challenges of collecting and collating accurate data from patients, how organizations defined a disparity and analyzed data, and the impact and uses of their findings.
Findings: To collect accurate self-reported data on race and ethnicity from patients, most organizations had upgraded or modified their IT systems to capture data and trained staff to collect and input these data from patients. By stratifying nationally validated indicators of quality for hospitals and ambulatory care by race and ethnicity, most organizations had then used these data to identify disparities in the quality of care. In this process, organizations were taking different approaches to defining and measuring disparities. Through these various methods, all organizations had found some disparities, and some had invested in interventions designed to address them, such as extra staff, extended hours, or services in new locations.
Conclusion: If policymakers wish to hold health care organizations accountable for disparities in the quality of the care they deliver, common standards will be needed for organizations’ data measurement, analysis, and use to guide systematic analysis and robust investment in potential solutions to reduce and eliminate disparities.