SDOH Data: Use Cases, Maintaining Privacy, and Health Data Ecosystems

Social determinants of health (SDOH) describes the conditions in which people are born, grow, live, work, and age. These factors have a significant impact on overall health outcomes and can help us understand the root causes of health disparities. SDOH data consists of information related to:

• Economic stability
• Education
• Social and community context
• Health and healthcare access
• Neighborhood and built environment

How to Use SDOH Data

The World Health Organization (WHO) believes SDOH accounts for 30-55% of health outcomes. By integrating SDOH data with clinical and claims data, researchers, healthcare providers, and payers can gain a more comprehensive understanding of patient populations, enabling them to develop targeted interventions and allocate resources more effectively.

Example: COVID-19 disproportionately impacts African American, Hispanic, and low-income populations

Studies have indicated disproportionate impact of COVID-19 on African American and Hispanic populations, as well as socioeconomically disadvantaged groups.

Insights incorporating SDOH data showed the disproportionate impact of COVID-19 on African American, Hispanic, and low-income populations, including:

• African Americans and Hispanics make up a larger than expected share of both cases and deaths.
• Households earning less than $49k annual income showed higher than expected representation in both cases and deaths.

Researchers Enhance Predictive Analytics with SDOH Data

Big data in healthcare has the power to transform patient care and patient outcomes. Using SDOH data with other real world data empowers researchers to run predictive analytics, gain insights, and make more informed decisions.

By analyzing patterns, healthcare organizations can:

• Identify at-risk individuals and communities
• Predict future health outcomes
• Implement preventive measures

For instance, by examining factors such as income, education levels, and access to nutritious food, healthcare providers can identify areas with higher rates of obesity and diabetes and develop targeted interventions to combat health issues.

Predictive analytics can also be used to optimize care management and resource allocation. By identifying patients with complex needs or those at risk for re-admission, healthcare providers can focus their efforts on high-risk patients, improving care coordination and reducing unnecessary hospitalizations.

Example: SOURCE Collaborative explores non-healthcare factors that may impact blindness from diabetes

By using SDOH to enrich clinical research, SOURCE found that lower levels of affluence in a patient’s residential community was associated with progression to blindness.

This allowed them to speculate several causes such as:

• Limited access to healthy foods
• Lack of parks or difficulty finding places to exercise
• Lack of eye care professionals in those locales

Analyzing social determinants of health alongside other patient data harnesses the potential of big data analytics, driving advancements in healthcare research and improving patient outcomes on a broader scale.

Payers Deploy Targeted Care Using SDOH Data

Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:

• Tailored Member Programs: Payers develop specialized initiatives like nutrition delivery services and transportation to and from medical appointments.
• Identifying Care Gaps: SDOH data helps payers identify gaps in care for underserved communities, enabling strategic in-home assessments and interventions.
• Future Risk Adjustment Models: Centers for Medicare & Medicaid Services (CMS) plans to incorporate SDOH-related Z codes into risk adjustment models, recognizing the significance of SDOH data in assessing healthcare needs.

Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.

Example: CDPHP supports physical and mental wellbeing with non-medical assistance

Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:

• Social isolation
• Loneliness
• Transportation barriers
• Gaps in care

By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.

Providers Optimize Value-Based Care Using SDOH Data

Value-based care organizations face challenges in fully understanding their patient panel. SDOH data can significantly assist providers in addressing these challenges and improving patient care:

• Onboard Patients Into Care Programs: Providers use SDOH data to identify patients who require additional support and connect them with appropriate resources.
• Stratify Patients by Risk: SDOH data combined with clinical information helps identify high-risk patients, enabling targeted interventions and resource allocation.
• Manage Transition of Care: SDOH data informs post-discharge plans, considering social factors to support smoother transitions and reduce readmissions.

By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.

While accessing SDOH data offers significant advantages, challenges can arise from:

• Lack of Interoperability and Uniformity: Data exists in fragmented sources like electronic health records (EHRs), public health databases, social service systems, and proprietary databases. Integrating and securing data while ensuring data integrity and confidentiality can be complex.
• Lag in Payer Claims Data: Payers can take weeks or months to release claims data. This delays informed decision-making, care improvement, analysis, and performance evaluation.
• Incomplete Data Sets in Health Information Exchanges (HIEs): Not all healthcare providers or organizations participate in HIEs. This reduces the available data pool. Moreover, varying data sharing policies result in data gaps or inconsistencies.

Overcoming these challenges require robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.

Maintaining Privacy Around SDOH Data

While the benefits are undeniable, it is crucial to maintain privacy and protect sensitive information around SDOH data. Healthcare organizations must adhere to strict data privacy regulations such as:

• The Health Insurance Portability and Accountability Act (HIPAA)
• The General Data Protection Regulation (GDPR)

These regulations ensure that patient data is handled securely and confidentially, minimizing the risk of unauthorized access or data breaches.

To maintain privacy when using SDOH data, healthcare organizations should:

• Implement robust data governance policies
• Establish clear guidelines for data access and sharing
• Utilize advanced data anonymization techniques

By following these best practices, organizations can leverage SDOH data while safeguarding patient privacy.

Health Data Ecosystems Containing SDOH Data

Organizations can access a wealth of SDOH data through the Datavant ecosystem. Datavant connects disparate data sources, empowering organizations to gain a comprehensive view of patient populations, uncover new insights, and drive better outcomes.

Datavant ecosystem provides access and connectivity to various data sources, including:

• SDOH data
• Clinical data
• Claims data
• Patient records
• Radiology images

SDOH data holds immense potential in transforming healthcare and addressing health disparities. With Datavant, healthcare organizations can securely access SDOH data, empowering them to make data-driven decisions and improve patient outcomes.