How To Improve Race, Ethnicity, And Language Data And Disparities Interventions

The disproportionate impact of the COVID-19 pandemic on historically marginalized communities has elevated and motivated a focus on equity-oriented data—especially race, ethnicity, and language (REL) data—that can be used to identify and intervene on urgent population health priorities. In particular, the pandemic increased awareness of structural racism and structural inequities that have led to long-standing disparities in health outcomes by REL categories, prompting public health and social driver interventions. Health care and public health entities with more complete REL data were able to more quickly and equitably allocate pandemic resources to communities hardest hit by the pandemic. However, the quality and completeness of REL data vary across health care organizations and states, creating state-specific challenges that impede efforts to identify and intervene on observed disparities. Without a valid and reliable “source of truth” for REL data across organizations, efforts to reduce health inequities risk being scattershot or misdirected.

There is a policy window to focus on multistakeholder approaches to improve the collection and use of REL data. Accrediting agencies such as the National Committee for Quality Assurance will soon require health plans to stratify performance measures by REL and reconcile REL data inconsistencies. Nationally, only 24 percent of commercial payers have complete race and ethnicity data. The Department of Health and Human Services (in its Equity Action Plan) and the Office of Inspector General recently reported that inaccuracies in Medicare and Medicaid’s REL data hindered their ability to assess health disparities. In addition, the Centers for Medicare and Medicaid Services’ Framework for Health Equity and the Center for Medicare and Medicaid Innovation’s (the Innovation Center’s) Strategy Refresh prioritize creating more significant partnerships with states, achieving multistakeholder alignment, and embedding equity into care transformation efforts, such as in the new ACO REACH Model or the Quality Payment Program. Together, these requirements and shared visions for health equity motivate a timely and sustained approach for collecting, sharing, validating, and using REL data.

Health care organizations have similarly advocated for better REL data to identify and reduce disparities. The American Hospital Association highlighted the importance of collecting REL data in health system efforts to identify disparities, improve patient satisfaction, and ensure access to care. Blue Cross Blue Shield of Massachusetts’s member portal asks members to identify their race, ethnicity, and language to help measure inequities, design solutions to improve programs and services, and publicly share performance. However, data collection efforts or programs that use REL data can cause apprehension among patients and providers or be misused or not adequately contextualized, as in the case of some race-based algorithms. Moreover, past efforts to improve the completeness and quality of REL data have been siloed, focused on single-organization quality improvement efforts or a standardized approach for data collection in hospitals. Renewed efforts should concentrate on systemwide partnerships to improve REL data.

We present considerations for state-based, multistakeholder approaches to collect, validate, and share REL data with the ultimate goal of advancing equity through clinical quality and social driver interventions that can improve the health and well-being of historically marginalized communities. States have long pursued multistakeholder approaches to health reforms and can play a critical role in addressing past challenges with REL data collection while promoting shared learning and accountability for reducing inequities. We outline examples of successful payer, provider, and state efforts that can be used in future comprehensive, multistakeholder approaches.