Community-level interventions to collect race/ethnicity and language data to reduce disparities

The systematic collection and use of race/ethnicity and language (REL) data by healthcare organizations has long been recognized as a critical step to reducing healthcare disparities locally and nationally. We seek to identify the challenges and opportunities in implementing community-level interventions to collect REL data for detecting and reducing disparities in care in the 14 multi-stakeholder communities participating in the Aligning Forces for Quality initiative.

This was a cross-sectional descriptive qualitative study.

We conducted 1-hour, face-to-face, semi-structured interviews with identified key informants during 2-day visits to each of the 14 communities in 2010, and supplemented this information with 2 rounds of semi-structured telephone interviews. Data were analyzed using a qualitative analysis software program, which assists with organizing and analyzing large quantities of interview data through creation of analytic units. We used deductive and inductive qualitative methods to analyze the data.

Communities found it challenging to implement a community-level intervention to collect standardized REL data because addressing disparities is complex, the utility of having individual healthcare organizations collect these data is difficult to communicate, and perceptions of disparities in the community vary across stakeholders. Opportunities include working with credible "early adopters" in the community and leveraging federal or state mandates to encourage providers to collect this information.

Community-level efforts to collect REL data require securing buy-in from organizational leadership, developing a dialogue across the community, and generating awareness across key players about disparities-reduction efforts, especially REL data collection, without alienating patients, communities, and providers.