The Role of SDOH in Predictive Analytics
Expanding the Future of Healthcare
Predictive analytics is the combination of data mining, predictive modeling, and machine learning. It is widely used across industries, including healthcare, where its adoption has grown due to increased data accessibility, technological advancements, and policy initiatives. While it gained traction in the early 2000s with the rise of EHRs and digital health solutions, significant advancements have occurred since the 2010s.
A key factor shaping the market is the growing role of Social Determinants of Health (non-medical factors), which significantly impact patient outcomes. By integrating SDOH with clinical and claims data, predictive models can better anticipate risks, personalize treatments, and optimize resource allocation. In this newsletter, we will explore the evolving SDOH market landscape, key data types and the applications driving innovation in predictive analytics.
What type of data can we analyze in Healthcare?
Descriptive Data - “What happened”
Example: A patient’s existing conditions are collected through surveys, such as neighborhood income levels or housing quality, which showcases the current social environment affecting health.
Diagnostic Data - “Why it happened”
Example: High rates of missed appointments linked to transportation barriers or limited access to local healthcare facilities explain why disparities in health outcomes.
Predictive Data - “What will happen”
Example: A patient’s historical data on socioeconomic status, education levels, and neighborhood safety can predict their risk for chronic conditions, assisting in proactive and preventive healthcare.
Prescriptive - “How can we make it happen”
Example: Data on grocery store locations, neighborhood income levels, and community dietary habits, healthcare organizations can assist in designing targeted interventions, such as mobile fresh-food markets or nutrition assistance programs, to improve access to healthy food.
Market Landscape
With the growing emphasis on holistic care, the integration of SDOH data is playing a pivotal role in shaping the future of healthcare. However, a precise market size for this sector remains elusive.
Based on my market research and analysis of multiple public data sources, the global predictive analytics market, particularly in SDOH, is experiencing rapid growth, with an estimated addressable market of $3 billion (Fig. 1). This estimate is derived from industry reports and trends in healthcare technology adoption.
In the North American payer market, the estimated value is approximately $475 million, 66% of U.S. healthcare organizations already utilizing predictive analytics. Applying this adoption rate, the U.S. market for predictive analytics in SDOH is estimated to be around $315 million.
With a projected CAGR of 21% through 2030, the integration of SDOH data into predictive analytics is expected to drive significant advancements in precision healthcare and population health management.
SDOH Data and Its Impact
Types of SDOH Data
SDOH data includes socioeconomic factors, behavioral patterns, healthcare access, environmental conditions, and genetic predisposition, all of which significantly impact health outcomes. Traditional healthcare data, such as claims and EHRs, fail to capture the full scope of external influences on a patient’s well-being. To build more predictive and actionable models, health plans must integrate comprehensive datasets including:
Social & Lifestyle Data (census data, financial records, public health insights, consumer behavior data, etc.)
Example: A patient’s financial records and neighborhood data help identify those at risk of food insecurity, enabling targeted nutrition assistance programs.
Individual Behavioral Data (communication preferences, engagement patterns, digital interactions)
Example: A patient who prefers digital communication receives medication reminders through a mobile app instead of mail, improving adherence.
Clinical Data (EHRs, lab results, imaging reports, physician notes, genomic data)
Example: A patient’s genetic screening reveals a high risk for breast cancer, prompting proactive monitoring and early intervention.
Pharmacy & Ancillary Data (medication history, prescription claims, lab test utilization, medical equipment usage)
Example: A patient with asthma frequently refills their inhaler earlier than expected, signaling potential poor condition control and triggering follow-up care.
How SDOH Data Enhances Predictive Analytics?
Incorporating SDOH data allows healthcare organizations to:
Identify at-risk individuals for chronic illnesses, hospital readmissions, or poor medication adherence.
Enable targeted interventions by proactively addressing social barriers to care and allocating resources more effectively.
Enhance care coordination through data-driven insights that improve patient outcomes.
Support holistic and personalized treatment plans that address both medical and social needs.
One real-world application of SDOH-enhanced predictive analytics was during the COVID-19 pandemic where SDOH data played a key role in mitigating disease spread. Digital applications leveraged real-time data like location history, demographics, and socioeconomic factors to identify high-risk populations and predict outbreak hotspots.
Key Players in SDOH Predictive Analytics
Unite Us: Connects healthcare providers with social services to improve care coordination.
Arcadia: Supports population health management and helps reduce healthcare disparities.
IBM Watson Health: Enhances patient engagement and enables personalized care.
Refer to Fig. 1 for additional examples in this market.
Types of Predictive Models in Healthcare
The rapid adoption of machine learning and regression models has strengthened predictive capabilities in healthcare. Key models include:
Regression Models:
Provide clear and interpretable relationships between variables and health risks.
Example - Predicts the likelihood of someone falling ill so doctors can act early with data like income, access to healthy food, and previous medical visits.
Machine Learning Models:
Learn from large amounts of data to uncover complex patterns.
Example - Forecast which communities might see more cases of asthma or heart disease by analyzing data like pollution levels and food access.
While discussing the market challenges and drivers in Fig. 1, it is important to note that many predictive models exhibit bias. Studies published in Circulation: Cardiovascular Quality and Outcomes indicate that traditional risk models often underpredict adverse outcomes for socially disadvantaged groups. By integrating SDOH data, these models can become more accurate and help reduce disparities.
Market Opportunities in SDOH Predictive Analytics
The growing emphasis on personalized medicine and population health management is expanding the role of SDOH in healthcare predictive analytics. Key applications include:
Clinical Research & Drug Development
Real-Time Patient Monitoring
Medical Device Monitoring
Mental Healthcare
Patient Risk Analysis
Preventing Readmissions
Personalized Care Plans
Genomics
The future of healthcare lies in integrating medical data with the broader social context in which patients live to gain a comprehensive view of their health. As predictive analytics and SDOH integration continue to advance, stakeholders including healthcare providers, policymakers, and payers must prioritize data-driven solutions to reduce disparities and improve patient outcomes.
If you have any additional insights on SDOH applications or emerging trends in this space, I would love to hear your thoughts.