Health care organizations have widely accepted the goal of reducing health disparities in this country. But getting to equitable health care starts with collecting the right data.

Programs should consider how they collect, aggregate and analyze data. The first step is collecting “direct” sources of race, ethnicity and language (REL) data from as many sources as possible, and having a strategy for prioritizing and managing those sources. The next step is aggregating data from both internal and external sources. The third step is filling data gaps. This requires imputation of indirect sources, using models such as RAND mBISG, aiming to link 100% of covered lives to either direct or indirect REL data. The value of these data comes from the organization's analysis and ability to leverage the data at the point of care, in addition to producing outcomes and quality metrics. While the first three steps are critical and foundational, getting data to the point of care is becoming a minimum requirement in the industry. Key features include bidirectional dataflow, NLP (quality and data accuracy), built-in clinical quality metrics and exporting.

With consolidated data, users can stratify measures by social factors, understand disparity scores and track score changes based on specific social factors. This information can help practices reduce health disparities by encouraging annual wellness visits, improving risk calculations, understanding the needs of their patients and tailoring care or increasing access for certain groups. An emerging pattern suggests these data will enable practices to achieve health equity through transparency and an ability to display actionable insights at the point of care.