The Critical Importance of Social Determinants of Health Data for Providing Context to Real-World Data

Written by: Cheryl Reifsnyder, PhD and Nicole Darden, Project Manager, Veradigm

In the past decade, the awareness of the importance of various non-medical factors in determining individuals' health outcomes and well-being has increased substantially. These factors are known as social determinants of health (SDoH) and are defined by the World Health Organization as the conditions in which we "are born, grow, work, live, and age." Researchers estimate that SDoH drive between 30% and 50% of health outcomes-in contrast to clinical care, which impacts only 20% of county-level health outcome variation. In the U.S., researchers estimate that SDoH may be responsible for up to 40% of preventable deaths.

Given the significant impact SDoH can have on health, various professional societies and organizations have published frameworks defining SDoH and advocating for collection of SDoH data. Healthy People 2030 is one, an initiative created by the U.S. Department of Health and Human Services. Healthy People 2030 has set data-driven national objectives in 5 key SDoH areas:

1. Economic stability
2. Education access and quality
3. Healthcare access and quality
4. Neighborhood and built environment
5. Social and community context

Addressing SDoH is vital to the pursuit of improving patient health. On the individual level, SDoH data provide physicians with a more complete context for their patients' health status, facilitating shared decision-making and individualized treatment planning. On the community level, SDoH data enable more effective targeting of public health interventions.

However, SDoH can also greatly impact clinical study implementation and outcomes, making it equally important to understand how SDoH influences clinical research. This article provides an overview of SDoH's interplay with research efforts-and strategies for harnessing SDoH to achieve greater understandings into your research outcomes.

How SDoH can affect clinical research

SDoH can significantly impact the design, implementation, and outcomes of clinical research in ways including recruitment and retention of study participants, data collection, and the effectiveness of the interventions being studied.

Recruitment & retention

Research has demonstrated significant associations between participants' willingness and ability to participate in clinical studies and participant characteristics, such as biological sex, age, ethnicity, language, education level, and employment status. Individuals from low-income households or with less education may face issues such as transportation problems, distrust of clinicians, or a lack of awareness of their eligibility, making study participation more difficult. Even among those successfully recruited for participation, SDoH factors such as low income or lack of education can create social and economic barriers that make them less likely to complete the research.

Data collection

SDoH can also affect participants' ability to provide high-quality, complete data. For instance, socioeconomic status can impact patients' ability to provide accurate medical histories, which could interfere with researchers' interpretation of clinical outcomes.

Intervention effectiveness

Finally, SDoH can impact the effectiveness of the interventions being evaluated. For instance, study participants' ability to comply with the study treatment regimen may be significantly influenced by factors such as housing conditions, employment status, access to healthy food, and available social support-hindering their ability to achieve positive health outcomes and interfering with interpretation of study findings.

SDoH are critical for clinical study teams to address when designing and conducting clinical trials.

Real-world data

Historically, the life science industry has relied primarily on clinical trials for development of drugs and medical therapies, but recent years have seen an increased focus on using real-world data (RWD) for research. RWD is data on patient health and medical care gathered from sources other than conventional clinical trials. RWD can be derived from multiple sources, such as electronic health records (EHRs), patient registries, insurance claims, surveys, mobile health devices, and more.

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) requires that patient information be protected/ and shared only with patient knowledge and consent. This means RWD must be "de-identified"-processed to remove identifying information (e.g., patient names, addresses, date of birth, email addresses, etc.) before use. De-identified RWD can be a powerful tool for life science research, enabling researchers to ask questions about patient populations without requiring full-scale randomized clinical trials.

Using RWD for research can have numerous benefits, such as reducing the time to market for new therapies, reduced research costs, and improved patient diversity. However, RWD-based research is not exempt from the influence of SDoH, making it essential to include SDoH data in RWD used for research purposes.

Unfortunately, obtaining SDoH data relevant to your specific research can be extremely challenging.

SDoH-enriched EHR data

In recent years, EHRs have become one of the primary RWD sources for researchers. Growing awareness of the importance of capturing SDoH information has prompted federal, state, and local efforts to encourage the integration of SDoH data into EHRs; many EHR vendors have begun implementing structured SDoH fields into their EHRs to help clinicians collect this information during the normal course of care.

However, there's a lack of consensus on which SDoH information is the most important to capture and a lack of standardization among vendors and healthcare systems about how and from whom to collect SDoH data. As a result, EHRs have numerous inconsistencies in the types of SDoH data collected and where this data is recorded.

Meanwhile, a lack of guidelines or incentives means clinicians have been slow to adopt changes in collection of SDoH data. Providers often fail to document important social risk factors in structured EHR fields. When they do record SDoH information, it's often in unstructured fields, such as clinical notes and other free-text fields. These fields are often inaccessible to standard data extraction and analysis methods.

Despite the increased difficulty of analyzing data from unstructured fields, they are often a significant source of critical SDoH information.

SDoH & Veradigm RWD

Veradigm offers life science researchers access to comprehensive, linkable RWD for generating real-world evidence. Veradigm Network EHR Data provides streamlined access to over 154 million patient records from multiple EHR sources. Within this dataset, Veradigm has direct access to raw structured and unstructured data. Veradigm's proprietary natural language processing (NLP) models are used to extract SDoH and other meaningful elements.

Adding SDoH to EHR-based RWD addresses a critical gap in understanding the full context of patient health. SDoH data provide life science researchers insights into socioeconomic, environmental, and behavioral factors often overlooked in traditional clinical data, such as income levels, education, housing stability, and social support.

By integrating SDoH, life science researchers can:

• Enhance treatment insights: Understanding how social factors impact treatment adherence and efficacy enables more personalized care approaches.
• Advance evidence generation: Strengthening real-world evidence using SDoH provides a more comprehensive view of patients' health journeys and outcomes.
• Support market access strategies: Including SDoH data in health economics and outcomes research (HEOR) enables the demonstration of value-based outcomes.
• Refine patient segmentation: Identification of cohorts with similar social and clinical profiles can help improve trial recruitment and study design.

Combining EHR and SDoH data can also help researchers to:

• Identify care gaps
• Stratify patients by risk
• Create more complete pictures of patient wellness
• Predict current and future risks and health outcomes
• Support diversity in clinical trial modeling and recruitment-which FDA guidance now strongly advocates

The Veradigm Network EHR Data dataset includes SDoH data in multiple categories from a diverse patient population.

In January 2025, Veradigm is introducing a new data category, Mortality data derived from ambulatory EHR systems. This dataset primarily reflects outpatient care and, while valuable, has some limitations. It may not capture mortality events occurring in hospitals, emergency settings, or long-term care facilities. Also, reporting gaps or delays can arise due to the nature of ambulatory care documentation. Despite these challenges, ambulatory EHR mortality data provide meaningful insights into patient populations within outpatient settings, enabling the identification of trends and supporting real-world evidence generation. This foundational dataset enhances our understanding of outcomes in outpatient care and serves as a springboard for incorporating more comprehensive mortality insights in the future.

Adding mortality data to Veradigm's EHR datasets also offers strategic and operational benefits for life science companies, such as:

• Enhancing generation of real-world evidence: Mortality is a definitive endpoint that strengthens clinical and observational studies. It allows life science companies to demonstrate products' long-term safety and efficacy; it supports post-market surveillance by identifying mortality trends associated with drug or device use.
• Acceleration of Drug Development: Mortality data enables better identification of at-risk patient populations, refining cohort selection for clinical trials; it facilitates survival analysis to assess therapies' impacts on mortality, particularly in oncology, cardiology, and rare diseases.
• Value demonstration to payers and providers: Incorporating mortality outcomes in HEOR studies offers evidence to support reimbursement decisions by highlighting treatments' roles in reducing mortality rates.
• Regulatory and policy alignment: Mortality outcomes are crucial for meeting regulatory agency requirements for demonstrating patient benefit in areas such as accelerated approvals. These data help companies align with global health initiatives focused on reducing preventable deaths.
• Improvement of precision medicine efforts: Combining mortality data with genetic and biomarker information enables life science companies to identify new drug targets and improve treatment personalization.

Contact us to learn more about Veradigm's Real-World Data with integrated SDoH and how we can help you meet your research needs.

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