This blog post was created through discussion over the past year among various payers, federal entities, providers, quality organizations, and HL7 staff with a focus on the importance of standardized data collection and management of race and ethnicity data. The discussion below represents a synthesis of these themes and identifies ways HL7 can help support the field.
High-quality, reliable, data are key to facilitating achievement of health equity, “the state in which everyone has a fair and just opportunity to attain their highest level of health”. This includes the collection, analysis and sharing of race and ethnicity data. However, misaligned approaches to standardized data collection present challenges to action. In addition, many organizations lack awareness of or experience challenges in applying standards when collecting and exchanging such data. In this blog, we explore existing resources that can help facilitate standardized data collection and management – HL7 is here to help.
The collection of high-quality race and ethnicity data is essential to address preventable differences - disparities - in health and healthcare. Such data are used to drive assessments, identify gaps, and the support decision-making that leads to actions to close gaps while improving outcomes. There are documented use cases by payers, providers, policymakers, and industry, for research, quality improvement and care delivery purposes. These use cases demonstrate how creating more inclusive datasets can support efforts to reduce health disparities and increase access to higher-quality care for historically marginalized communities. For example, race and ethnicity data can help provide transparency on disparate access to and uptake of services that can lead to targeted quality improvement and enhanced services for under-resourced populations. For this to occur, reliable, accurate, and consistent data are needed.
Regulatory agencies underscore the importance of these data, often requiring their collection and use. There are a variety of local, state, and federal requirements, ranging from program evaluation and assessment to required reporting as part of health plan contracting. Notably, different jurisdictions may have different expectations for how this information is summarized and reported. This is further complicated by the timing and misalignment of federal expectations, standards development processes, and operational/technical implementations.
One way to begin harmonizing and reduce variation is to standardize data collection. This includes ensuring alignment of questionaries and methods of collecting these data, but also engaging with communities, individuals and their families to overcome the misconceptions of how these data will – and will not – be used. Facilitating trust and open communication could potentially include adding a component of consent to collection forms alongside detail on the intended use of race and ethnicity data. Collecting and exchanging race and ethnicity data requires a multi-stakeholder approach involving both the public and private sectors to establish a robust approach to reduce health disparities.
As organizations increasingly recognize the significance of capturing race and ethnicity data, many lack awareness of or do not easily understand the importance associated with, applying standards when collecting and exchanging such data:
- First, while regulatory bodies may mandate specific categories to be collected, the data are more useful when tested against and available for utilization in a wide variety of uses cases. The details in use cases document specific scenarios of data exchange, integration, sharing, and retrieval. Guidance for different uses (e.g., community-level versus national) will improve consistency across applicable data systems when a single standard is used.
- Second, applying terminology best practices can help organizations collect and report data consistently. Variation in data collection questions and responses causes problems that can be avoided through standard terminology and common definitions, improving communication within and between organizations. Logical definitions provided by Logical Observation Identifiers Names and Codes (LOINC) and the Centers for Disease Control and Prevention Race and Ethnicity Code set (CDCREC) can unify the legacy, heterogeneous approaches.
- Finally, data sources and collection methods must be considered. Data quality, including validity and reliability, is directly impacted by the circumstances of collection. Identifying and tracking the details alongside the data elements enables appropriate application and use of data. Without understanding the origin of race and ethnicity data (e.g., self-reported, medical record), interpretation is challenging. Advances in standards that enable identification of the data origin (e.g., use cases, terminology and provenance) are being made, but implementation is lagging.
HL7 creates and publishes a variety of standards and terminology to support organizations as they address health equity challenges, and has resources that can support the standardized collection and management of race and ethnicity data. Knowledge of and access to data exchange standards is the key to better data quality. As such, it is important to leverage existing United States Core Data for Interoperability (USCDI), Consolidated Clinical Domain Architecture (C-CDA), and FHIR® US Core resources and collect data in standardized terminology at the lowest level of granularity needed. HL7 standards can support organizations with developing consistent internal mapping processes aligned with data standards. Technical resources and implementation guidance produce better results. Additionally, implementers and organizations can evaluate their complete set of business use cases and match use cases to available terminology and implementation guide (IG) specifications. The hierarchy of the code systems can capture the granularity needed by organizations, thus obviating the need for organizations to develop their own code systems.
The HL7 Community supports the testing of specific use cases to advance adoption through HL7 Connectathons and active discussion within HL7 Workgroups. Data collection for race and ethnicity is still evolving. For example, the Office of Management and Budget (OMB) is in the process of generating updated nationally-standardized categories. Although changes may occur in the future, organizations can still make progress now, and engaging with the HL7 community can help to navigate these changes. The HL7 community supports testing and adoption to advance specific use cases. Even as data collection is still evolving, organizations can make progress now. If you are interested in participating, you can join the HL7 community and participate in relevant working groups, like Patient Care or the Gravity Project.
Standardized data collection and management for race and ethnicity data are an essential and necessary component of improving health outcomes and reducing health inequities. Misaligned approaches and a lack of awareness of standards present challenges to action. HL7 provides support to organizations to address such challenges by providing access to data exchange standards, technical resources, and implementation guidance. With a multi-stakeholder approach, we can establish a robust set of data standards to reduce health inequities and improve health outcomes for all communities.
The following individuals contributed to the authoring of this blog:
- Rachel Harrington, National Committee for Quality Assurance (NCQA)
- Fern McCree, National Committee for Quality Assurance (NCQA)
- Anne Marie Smith, National Committee for Quality Assurance (NCQA)
- Matt Elrod, MaxMD
- Lisa Nelson, MaxMD