HealthJuly 25, 2023

How a standards-based approach for social determinants of health (SDOH) data improves health equity

Discover how data-driven approaches are driving initiatives to incorporate social determinants of health data to improve health equity.

What is your organization doing to move the needle forward in the quest for health equity and mitigating barriers to care? What challenges are you experiencing? Likely there are several obstacles, especially given that many of the issues have been rooted in societal inequalities, historical and contemporary injustices, disparities in health and healthcare, and adverse social determinants of health.

Summarized below are a handful or so of current initiatives and drivers for change intended to help forge us ahead, using a data-driven approach, as we traverse our way through these very complex issues. Some of the bottlenecks in reaching such goals include navigating through large seas of data needed to support initiatives, ensuring the data is high quality, and keeping up with the latest industry standards both from a semantic and syntactical perspective for semantic interoperability.

Health equity, health disparities, and social determinants of health (SDOH)

CMS, the largest provider of health insurance in the United States, is rolling out a new framework for health equity. In CMS Framework for Health Equity 2022-2032 CMS writes: “Consistent with the Department of Health and Human Services’ Healthy People 2030 Framework, CMS recognizes that addressing health and health care disparities and achieving health equity should underpin efforts to focus attention and drive action on our nation’s top health priorities.”

What is health equity?

Per CMS, health equity is the attainment of the highest level of health for all people, where everyone has a fair and just opportunity to attain their optimal health regardless of race, ethnicity, disability, sexual orientation, gender identity, socioeconomic status, geography, preferred language, or other factors that affect access to care and health outcomes.

What are social determinants of health?

Social Determinants of Health (SDOH), as defined in Healthy People 2030, are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks. And that last part is key here. They have a major impact on people’s well-being and quality of life.

The World Health Organization adds that social determinants of health are mostly responsible for health inequities – the unfair and avoidable differences in health status seen within and between countries. One of the overarching goals of Healthy People 2030, which sets data-driven national objectives to improve health and well-being, is specifically related to SDOH: “To create social, physical, and economic environments that promote attaining the full potential for health and well-being for all.” Since SDOH contributes to wide health disparities and inequities, mitigating social risk and needs is a primary approach to achieving health equity!

Improving SDOH data quality and interoperability to address health equity

Quality data around SDOH is necessary to accurately identify, act on, and measure social risk, needs, and health disparities. During the April 2023 CMS National Stakeholder Call, CMS outlined a key initiative to prioritize health equity data improvement across the agency stating, “Accurate data enables us to identify disparities and gaps and aids in the creation of evidence-based policies that meet the needs of the communities we serve”. In order to address any barriers inhibiting care at the individual and community level, we must ensure that the data is accessible to the right people, at the right time. Having the ability to share critical data between health and social care ecosystems, determining differences in care and outcomes for cohorts of patients and other analytic initiatives need accurate, reliable SDOH data.

Accurate data enables us to identify disparities and gaps and aids in the creation of evidence-based policies that meet the needs of the communities we serve.

Impact of the HTI-1 proposed rule

In order to address this challenge around sharing SDOH data, the ONC has introduced the Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing (HTi-1) proposed rule, to support the improvement of care delivery for clinicians and care experience for individuals by improving access to interoperable data. The proposed rule seeks to implement provisions of the 21st Century Cures Act and make updates to the ONC Health IT Certification Program in order to advance interoperability in healthcare. Data consistency and reliability are necessary and foundational in this quest.

How SDOH data is exchanged to achieve health equity

The ONC further recognizes the importance and potential of “data-driven technologies” to impact health equity. They have recently published the Social Determinants of Health Information Exchange Toolkit – Foundational Elements for Communities, a resource to support communities working toward achieving health equity through SDOH information exchange and the use of interoperable, standardized data to represent SDOH. Various approaches are outlined in the toolkit including emerging standards from the Gravity Project that expand available SDOH core data for interoperability using HL7 FHIR, among others.

What is The Gravity Project?

The Gravity Project is a diverse, multidisciplinary, collaborative community that has successfully introduced a nationally recognized set of consensus-driven standards-based terminologies that support care across multiple social risk domains such as food insecurity, housing Instability, transportation insecurity, and material hardship. The goal in developing the data standards is to support the collection, use, and exchange of data to address the social determinants of health across the four primary activities of care: screening, diagnosis, goal setting, and intervention activities.

In addition to the all-important race, ethnicity, preferred language, and other needed data elements that were included in previous ONC minimum data sets, the ONC has added SDOH data elements and associated vocabulary standards to the United States Core Data for Interoperability (USCDI), with the version 2 publication. Standards recommendations have also been added in the Social, Psychological, and Behavioral Data category in the ONC Interoperability Standards Advisory (ISA) Vocabulary/Code Set/Terminology Standards and Implementation Specifications.

Supporting health equity through technology

The Wolters Kluwer, Health Language team supports The Gravity Project through our membership and other efforts toward standards-based health data interoperability that underpins health equity, reducing disparities, and mitigating barriers to effective and efficient care. Inspiring our work is our belief that all people, everywhere, should have access to the best care possible. Supporting health equity through innovative thinking and solutions will help chart the course ahead.

Eliminating health disparities and addressing social risk and needs is essential to advancing health equity. Data elements are used to document screening assessments, diagnoses/problems, goals, and interventions. When SDOH data is codified to standards, we are then able to share information that is semantically sound, interoperable, that is meaningfully recognized. By ensuring data accuracy and completeness we can then accurately enable analytics to help measure outcomes, differences across a variety of populations, summarize findings, and identify evidence-based interventions for priority populations.

Learn more about how Health Language solutions support healthcare stakeholders facilitate accurate and comprehensive data collection and analysis enabling clients to take action on SDOH insights.

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Lisa Wolf, BSN, RN-BC
Solution
Health Language Analytics Integrity
Leverage consistent and accurate datasets to easily derive insights, whether analyzing big data to understand population trends or auditing an individual patient record.
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