How Plans Can Improve Race and Ethnicity Data Collection for HEDIS

One question kept coming up when Keirsha Thompson, manager at National Committee for Quality Assurance (NCQA), and her team talked to payers: How can our health plan improve its performance on race and ethnicity data collection for the Healthcare Effectiveness Data and Information Set (HEDIS)?

NCQA introduced race and ethnicity stratification into HEDIS quality measures in 2022. Payers wanted to improve their performance on this measure. Since consumers often use this tool to compare plans, health plans pour a lot of energy and resources into achieving a high HEDIS score, potentially amplifying the significance of race and ethnicity stratification challenges.

However, the path toward improvement on the new HEDIS measure was unclear. Thompson and her team designed a pilot program that would yield information on best practices for race and ethnicity data collection.  

After emailing and calling payers from many corners of the industry, 14 health plans joined the initiative. They were a mix of large and small payers from various lines of business and regions. The participants shared race and ethnicity data with NCQA and engaged in qualitative interviews to give a more detailed perspective on their data collection processes.

“People were really excited about the idea of just getting some kind of unofficial pilot data and see what we could do with that, essentially just get a sense of where everything stands right now,” Thompson told HealthPayerIntelligence.

The Learning Network initiative generated helpful insights into trends, challenges, and strategies around data collection efforts.

One positive trend surfaced from the interviews: lack of consumer trust may be less of a barrier for payers in the area of race and ethnicity data collection.

In 2020, gaining consumers' trust was one of the key challenges that payers faced. Three years later, some barriers remained. However, payers had gained more traction with obtaining direct data, a sign that members were growing more comfortable with sharing this information.

Empowering health plan staff to gather race and ethnicity data was crucial for payers’ success, Thompson and her team also found.

While this may seem like an obvious criterion on the surface, Thompson underscored that gathering this data may require staff to go beyond normal processes. Health plan employees worked not only with internal staff but also with community-based organizations, social workers, public health departments, and other stakeholders to accumulate race and ethnicity information.

As a result, health plan leadership may need to allocate more funds to external data collection. They also should ensure that any employee who handles race and ethnicity data collection—from quality analytics to IT to HEDIS operations staff members—is aligned on the importance of this process and its role in advancing health equity.

“Make sure that all health plan staff who interact with the race and ethnicity data understand why they're handling that data, what its implications are, the limitations, [and] what the use cases are for dealing with that data,” Thompson said. “It's really important that staff who interact with the data understand why and what the importance is of doing this equity-focused work.”

Specifically, Thompson and her team discovered that having an IT team that understood the significance of race and ethnicity data collection was key. An aligned IT team enabled more consistent access to data, both in terms of securing access for the appropriate staff members and maintaining the technologies that gathered and stored the data.

Additionally, successful health plans prioritized self-reported and direct data on race and ethnicity. This type of information comes largely from member surveys, health plan portals, enrollment files, and state or CMS EHRs.

“We would say figuring out ways to really push member traffic towards things like member portals…would be the best way to get at this data and to get the most accurate data possible,” Thompson said.

Methods of data collection that eliminate intermediaries and deliver race and ethnicity data directly from the members are the most reliable.

However, prioritizing data sources is useful beyond the data collection process. Whether pulling race and ethnicity data for internal use or reporting on these data to external sources, payers can prioritize their data sources to ensure that they only present the most accurate data they have.

Thompson projected that NCQA may continue to work with the participating health plans—along with bringing in new payers—to see how their strategies evolve and continue gathering best practices. In the meantime, the report and other resources are available on NCQA’s website.

“Despite how hard it can be to collect and leverage this data to have those correct systems and processes in place, it really can be done,” Thompson emphasized. “Whether you're a small plan or a large one, whether you manage rural or urban populations, whether your health system has been doing this health equity focus work for 10 years or if it's something that you are new at taking on, it can be done.”