The pursuit of health equity in the United States faces a significant and persistent obstacle: the pervasive gaps in person-centered data that render the health status and experiences of diverse communities invisible. This widespread issue of missing, incomplete, and non-standardized information, particularly concerning race and ethnicity, creates profound blind spots across the healthcare system. Without accurate and comprehensive data, it is impossible to identify, measure, or address the disparities that prevent millions from achieving their full health potential. The consequences of these data deficiencies are far-reaching, crippling the ability of healthcare providers to design effective interventions, hindering life sciences companies from conducting inclusive clinical trials, and ultimately eroding the trust between patients and the institutions meant to serve them. Recognizing this foundational crisis, a growing consensus has emerged, underscored by recent regulatory mandates from bodies like The Joint Commission and the Food and Drug Administration, that treating data as a strategic asset is no longer optional but essential for building a more equitable public health infrastructure. In response to this urgent call, a landmark initiative has forged a new path forward, creating a collaborative framework to transform data collection from a mere administrative task into a powerful tool for change.
A Collaborative Framework for Change
In an effort to confront this challenge head-on, the Reagan-Udall Foundation for the Food and Drug Administration established the RAISE (Real-World Accelerator to Improve the Standard of Collection and Curation of Race and Ethnicity Data in Healthcare) program. This initiative was designed not as a top-down mandate but as a shared space for learning and capacity-building. Between January and June of 2023, the program convened nearly 750 decision-makers and leaders in a series of 11 virtual workshops. This diverse cohort represented a comprehensive cross-section of the healthcare ecosystem, including community organizations, care delivery systems, insurance payers, life sciences firms, health technology companies, and government agencies. The workshops explored a wide range of critical topics, from developing effective incentive structures and building robust informatics infrastructure to enhancing workforce training and navigating the complexities of data exchange. Through a rigorous process of expert presentations, community polling, and iterative review, the key learnings from these sessions were synthesized into a multidimensional action framework. This framework, now a central resource for the healthcare community, translates the collective insights into actionable, real-world steps for institutions seeking to build a more equitable data foundation.
The resulting action framework is built upon a consensus that no single solution can resolve the multifaceted challenges of data collection and is therefore organized into four core priorities. A foundational priority is to Standardize the Collection of Person-Centered Data. In a fragmented healthcare landscape where patients often interact with numerous disconnected entities, standardization is essential for creating a cohesive understanding of a patient’s journey and for aggregating data meaningfully. The American Hospital Association’s web-based tool, which provides resources for standardizing race, ethnicity, and language data, serves as a prime example of this principle in action. A second, equally crucial priority is to Train the Workforce in Data Collection. Effective data gathering depends on a well-informed and motivated workforce. This training must extend beyond procedural instructions on how to ask for information; all staff, including non-clinical personnel, must be educated on why these data are vital. When employees understand how inaccurate demographic information directly contributes to health disparities, they become active partners in the mission. The Icahn School of Medicine at Mount Sinai provided a powerful case study, demonstrating a remarkable 76% improvement in the completeness of race and ethnicity data in an outpatient setting following the implementation of such a multifaceted training program.
Driving Implementation Through Incentives and Community Engagement
To drive behavioral change within large organizations, the framework highlights the critical need to Incentivize Data Collection. It calls upon organizational leadership to champion data equity by creating a clear business case that reframes this work not as an operational cost but as a strategic investment in healthier communities and long-term sustainability. Improving data quality leads to better population health management, more effective care delivery, and more inclusive research, all of which generate value. The Health Care Transformation Task Force has provided concrete examples of how to build this business case, particularly by leveraging value-based payment models and contracting to secure the necessary funding and infrastructure for enhancing demographic data collection. Complementing this is the fourth core priority: Collect Data Locally and then Aggregate. This priority emphasizes a balanced, community-centric approach. It advocates for engaging directly with local communities to understand how they self-identify and wish to be represented, then using the flexibility within electronic health records to offer response categories that reflect these preferences. Simultaneously, these locally relevant categories must be capable of being mapped to broader standards, such as the Office of Management and Budget value sets, to enable aggregation and comparison at regional and national levels. The CDC’s IDeal (Innovations in Data Equity for All Laboratory) program exemplifies this balance, fostering collaboration between community, academic, and government partners.
Operationalizing these priorities requires navigating complex cultural and trust-related barriers, which the framework addresses through several cross-cutting strategies. One of the most important is to Address the Need for Humility in Health Care. This calls for a profound cultural shift, moving beyond the transactional collection of data to a deeper understanding of its human significance. It requires implementing standardized training protocols that teach professionals not only why the data are collected but also how they connect to community health, who will have access to them, and what tangible benefits they will provide. This approach helps foster an environment of respect and mitigates the risk of unwittingly perpetuating systemic biases. Furthermore, the framework stresses the need to Address Distrust and Misalignment. Recognizing that many communities harbor legitimate distrust of healthcare institutions due to historical and ongoing harm, this strategy places a premium on transparency. Providers must be explicitly clear about why they are collecting sensitive information and how it will be used to benefit the community. Critically, patients must be given a clear and respectful option to opt out. Providing this information in multiple formats and languages is essential to respecting individual autonomy and rebuilding the trust that is foundational to any successful data initiative.
Overcoming Technical and Resource Barriers
Beyond cultural shifts, the framework outlines practical strategies for overcoming significant technical and logistical hurdles. One key strategy is to Improve Choice Without Overwhelming Respondents. This involves a careful balancing act between providing more granular, representative data options and overburdening both patients and existing information systems. The solution lies in tailoring data categories to local contexts through direct community engagement. The process of identifying and validating demographic value sets should be a collaborative, bottom-up endeavor rather than a top-down mandate. By piloting new options with community input, health systems can develop more patient-centered data practices that accurately capture the diversity of the populations they serve. Another major technical challenge is to Improve Exchangeability of Person-Centered Information. A significant amount of data fidelity is lost when information is exchanged between different systems that lack standardized protocols. This is particularly problematic for individuals of mixed or multiple ethnicities, whose nuanced identities are often lost in aggregated datasets. The strategy calls for promoting standardized methods that align both data and metadata across platforms, starting with identifying technical stress points and then incentivizing the adoption of solutions across the ecosystem to facilitate smoother data exchange.
Forging a Path Toward an Equitable Future
Ultimately, the framework acknowledged that meaningful change required a strategic allocation of resources, which in turn demanded a strong commitment from senior leadership. The final cross-cutting strategy, Address Resource Limitations, called upon leaders to be educated on the direct link between complete population data and the ability to bridge health gaps. By sharing existing resources and mapping out needed investments, such as those enabled by alternative payment models, organizations could unlock new funding opportunities. This reframed the work not as an operational burden, but as a high-return investment in addressing unmet needs and creating more representative and valuable data assets. The implementation of this comprehensive action framework depended on dedicated champions across all sectors of the healthcare ecosystem. Organizations were encouraged to use the framework as a flexible guide, partnering to identify their specific pain points and prioritizing the solutions most relevant to their context. The initiative asserted that achieving healthier communities and ensuring high-quality patient care necessitated bold leadership. By embedding the collection and integration of person-centered data into core operations, administrators, providers, researchers, and insurers were empowered to make more informed decisions, ultimately building a more efficient, effective, and equitable healthcare system for all.
