RAISE: Elevating Person-Centered Data for Healthy Communities

ABSTRACT

The availability of person-centered data is critical to more robustly characterize populations, which facilitates solutions to address unmet medical needs. The goal of RAISE (Real-World Accelerator to Improve the Standard of Collection and Curation of Race and Ethnicity Data in Healthcare) is to curate existing efforts to improve data collection, share the data with leaders in health care, and provide an enduring resource to support organizations in transforming their data systems to support healthy communities.

We developed 11 virtual workshops to share solutions and address common barriers in reporting, collecting, curating, and sharing demographic data with experts from health care delivery systems, payers, data technology companies, government agencies, research settings, and local communities. We summarized workshop proceedings into thematic areas and, through community polling, developed a multidimensional action framework to translate our learnings into actionable steps to address the most pressing gaps in the collection of person-centered data, using race and ethnicity data as an initial use case.

Community partnership is central to cocreate data systems that curate information necessary to produce reliable data that support health care and healthy communities. Doing so requires respect, intentionality, standards, education, and collaboration with partners across the health care ecosystem, including the communities themselves.

Am J Manag Care. 2025;31(Spec. No. 15):SP1100-SP1103

A key component in building healthier communities is ensuring that relevant and timely resources are available to maintain and improve health. Person-centered data that enable a characterization of who is affected by a disease or condition are essential for ensuring that the development of medical products and delivery of care are safe and effective. These data include information on demographic and social needs closely related to health outcomes.¹ High levels of missing information limit a true understanding of health status and the evaluation of medical products across all subpopulations.² There are points along the health care data continuum—reporting, collection, curation, and exchange—where information about patients can be captured or lost.

The consequences of these gaps in real-world data are significant. Without comprehensive data, health care providers lack the tools to understand how different communities experience care and treatment and to create targeted interventions. Poor demographic data leave life sciences companies with a limited ability to properly recruit for and design clinical studies that would yield generalizable results that reflect the populations that could benefit from those therapies. This generalizability allows for expanded product labeling, insurance coverage, and, importantly, the confidence of patients and their clinicians to know that medicines were designed with them in mind.

Recognizing this critical gap, the Joint Commission revised its rules to require ambulatory health care, behavioral health care, and critical access hospitals to include demographic data in patients’ medical records to improve care.³ The Food and Drug Omnibus Reform Act of 2022 required the FDA to publish the FDA’s Diversity Action Plans guidance to support life sciences companies in improving demographic representation of the US population in pre- and postmarket clinical studies to understand the safety and effectiveness of pharmaceutical drugs, biologics, vaccines, medical devices, and diagnostic tests.⁴

The Reagan-Udall Foundation for the Food and Drug Administration, an independent nonprofit organization created by Congress to advance medical innovation to support the FDA’s efforts to protect and promote public health, created the RAISE (Real-World Accelerator to Improve the Standard of Collection and Curation of Race and Ethnicity Data in Healthcare) project to provide an opportunity to share, learn, and build capacity to identify and prioritize solutions to improve the inclusion of demographic data throughout the health care data continuum, using race and ethnicity data as an initial use case.⁵ From January to June 2023, nearly 750 decision makers and leaders—representing community, care delivery, insurance, life sciences, health technology, and government—convened in a series of 11 workshops to share the current progress and advance best practices in data collection, curation, and exchange. Topics ranged from incentive sharing to support-informatics infrastructure and workforce training to trade-offs in data exchange. Participants used a robust process of polling and review, and key learnings from each workshop were summarized into an action framework (eAppendix [available at ajmc.com]) to help users improve the collection, curation, and exchange of race and ethnicity data in their institutions. The RAISE web page houses workshop summaries, podcasts, and other resources.⁵

Key Findings

The action framework illustrates the need for a multipronged approach to integrating person-centered data into health care settings. It reflects priorities and strategies generated by the RAISE community. Importantly, the action framework includes real-world examples of how organizations are implementing the prescribed strategies.

Action Framework Priorities

Standardize the collection of person-centered data. Common language facilitates dialogue and the exchange of data, which is necessary in a federated system such as the US to understand patients’ health care journeys. For example, the American Hospital Association created a web-based tool that provides hospitals, health care systems, clinics, and health plans with information and resources for collecting and using race, ethnicity, and language data from patients.⁶

Train the workforce in data collection. Although clinic staff (eg, registration or patient scheduling personnel) may be aware of differences in health outcomes, staff at all levels should be trained to recognize how inaccurate or incomplete demographic data can contribute to such differences. The Icahn School of Medicine at Mount Sinai in New York, New York, for example, has a multifaceted approach for improving the capture of race and ethnicity data in an outpatient setting that culminated in a 76% improvement in the completeness of this information through its training programs.⁷

Incentivizing data collection. Incentives drive behavior and are typically set by organizational leadership, who must be aware that there is a problem and that there are solutions worth investing in. The Health Care Transformation Task Force provides examples of how to build the business case for investment in healthier communities and strategies to secure sustainable support.⁸ The business imperative to create healthier communities gains more traction as data continue to show that gaps in care and outcomes are not explained by genetics alone. The examples show how value-based payment models and contracting provide investment and infrastructure to improve the collection of demographic data.

Collect data locally and then aggregate. This priority emphasizes the need to engage with local communities to understand how they represent themselves, and then to use the many tools available through electronic health record and other platform vendors to provide response categories that reflect local preferences, while enabling alignment with standards such as those from the Office of Management and Budget value sets for demographic variables. An example of how to achieve this is from the CDC’s IDeal (Innovations in Data Equity for All Laboratory) program, a community, academic, and governmental collaboration to improve the collection of demographic and social needs data.⁹

Action Framework Strategies

Each of the following strategies aligns with 1 or more of the priorities.

Address the need for humility in health care. To create healthier communities, health care institutions must practice humility. This involves not only collecting descriptive data but also understanding their profound significance. Developing and implementing standardized training protocols is key, ensuring that health care professionals understand why data are collected, how the data connect to a healthier community, who can access the data, and the tangible benefits the data provide to communities. Such training fosters a health care environment that respects different people, rather than unwittingly perpetuating biases.¹⁰

Improve choice without overwhelming respondents and existing information architecture. Improving the completeness of person-centered data requires a careful balance—expanding data options without overwhelming respondents or overburdening existing systems. This means tailoring data categories to local contexts and involving community stakeholders in governance and decision-making processes. Identifying and validating demographic value sets should not be a top-down process, but one that engages communities directly to ensure relevance and accuracy. By updating and piloting these options within the community, health care systems can create more patient-centered data practices that better capture the variety of people they serve.

Address distrust and misalignment between question and answer. The collection of sensitive information often comes up against distrust from people who have been harmed and are wary of how their information will be used. Transparency is essential: Health care providers must be clear about why they collect these data, how the data will be used, and who will have access to the data. Equally important is providing patients with the choice to opt out and ensuring information is available in multiple formats and languages. This approach respects individual autonomy and builds trust—the bedrock of any successful initiative.

Improve exchangeability of person-centered information. One significant hurdle in leveraging demographic data to improve health care outcomes is the lack of standardization in data collection and exchange, which can lead to data loss. Persons of mixed ethnicities are a key area of concern because aggregated data sets often lose the granularity of the different ethnicities in these individuals. To address this, health care systems must promote standardized methods that align information (including metadata) across platforms and reduce technical barriers. Identifying where these technical stress points occur is the first step; developing and implementing strategies to address them is the next step. Incentivizing the adoption of these standards is crucial, as it will facilitate smoother data exchange and more effective use of this critical information to advance our understanding of populations at risk and the benefits of care and therapy.

Address resource limitations. Advancing healthier communities requires not only commitment from top administrators but also a strategic approach to resource allocation. Health care leaders must understand the role that more complete data on their populations can play in bridging health gaps. Sharing available resources and mapping out needed investments, such as adopting alternative payment models, can unlock funding opportunities. It is not just about doing what’s right—it is about recognizing the potential return on investment from addressing unmet needs and generating representative data sets. Preparing strong funding proposals that highlight these opportunities will be key to driving meaningful change.

Suggestions for Moving Forward

Implementing the RAISE action framework will require champions across the health care ecosystem to partner, examine pain points, and prioritize relevant solutions. Users can talk with their organization about what challenges are addressable and tailor the suggested strategies as needed. The path to healthier communities and quality patient care requires bold leadership. Data collection should not be seen as a compliance task but as a transformative tool to advance health care and health outcomes. Collecting and integrating these data into core health care operations will allow administrators, providers, researchers, and insurers to make informed decisions at all levels, building efficiency and effectiveness.

Acknowledgments

The authors thank Richardae Araojo, PharmD, MS, and Christine Lee, PharmD, PhD, for their critical review of the manuscript.

Author Affiliations: Reagan-Udall Foundation for the Food and Drug Administration (CR-W, HCH, JCE, LAB-M), Washington, DC; Evidence to Practice (AC, JB-C),
Baltimore, MD.

Source of Funding: The RAISE project was fully funded by the Food and Drug Administration (FDA) of the US Department of Health and Human Services (HHS) as part of a multi-year (2023 & 2024) financial assistance award totaling $875,000. The contents are those of the authors and do not necessarily represent the official views of nor an endorsement by FDA/HHS, or the US government.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (CR-W, AC, JCE, LAB-M); acquisition of data (CR-W, AC, JB-C); analysis and interpretation of data (CR-W, AC, HCH, JCE); drafting of the manuscript (CR-W, AC, HCH, JB-C); critical revision of the manuscript for important intellectual content (JCE, LAB-M); obtaining funding (CR-W, AC, LAB-M); administrative, technical, or logistic support (AC, HCH, JB-C, JCE, LAB-M); and supervision (CR-W).

Send Correspondence to: Carla Rodriguez-Watson, PhD, MPH, Reagan-Udall Foundation for the FDA, 1333 New Hampshire Ave NW, Ste 420, Washington, DC 20036. Email: crodriguezwatson@reaganudall.org.

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