Addressing Inequities and Social Determinants of Health with Community-Wide Data Matching and Integration
Minakshi Tikoo, PhD MBI MSc MS
Minakshi Tikoo, PhD MBI MSc MS
Chief Problem Solver
Published Sep 21, 2021
Currently, issues related to equity, Covid vaccinations, and transmission of disease have created an intense and ongoing focus on social determinants of health (SDOH) and the importance of public policy to address these issues. CDC defines SDOH as, “conditions in the places where people live, learn, work, and play that affect a wide range of health and quality-of life-risks and outcomes.” These conditions are grouped into five domains: economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, and social and community context.
At NextGate, we are continually evolving to assist our customers better serve their populations and improve the discourse on the social determinants of health. How can our technology contribute to a better understanding of the person that results in better health outcomes and improved wellbeing? How can unified, standardized and comprehensive data advance the discussion on equity and disparities and support person-focused service delivery? And how can NextGate address inherent inequities and disparities that are a result of geography, the color of our skin, the social culture, and the food that we eat?
In the past decade, government funded services in healthcare and social services have highlighted the importance of SDOH in the lives of people and how the context in which people live matters and impacts our well-being. Merely highlighting the importance of context, though, does not provide us with implementable solutions that inform the service professionals about how to use these data to better serve the people. These data are complex and integration of the data for designing and delivering person-centered services is even more complex. The Healthy People 2030 provides an excellent framework for agencies to incorporate the principles of SDOH in their service delivery network with associated measures.
So how is NextGate advancing data collection and unification of SDOH? First, our Enterprise Master Person Index (EMPI) captures where people live and where they seek services; and can associate person-level attributes such as demographic data. Additionally, our patient data matching and unification model is expandable and can ingest and unify community-level data that can be translated to person-level data so that the providers can utilize that data actively to make better service decisions that respect the needs of the person. Providers can also better understand the social and environmental conditions under which their clients live at different scales.
Second, data capture afforded by the EMPI can be used to better describe the service population or catchment area of service delivery, thus assisting our providers to link these population descriptors to other easily available SDOH datasets to design and package services better. For example, data captured in the EMPI when enhanced with publicly available community data can help inform the service-focused discussions using these data coupled with geospatial analytics techniques.
Planning, operations optimization, and research can be conducted on topics like:
1. Evaluating access to transportation and medical facilities for time and cost efficiency and propose transportation routing alternatives.
2. Shift rental incentives to communities where people experience better lifestyles and improved health outcomes.
3. Compare clusters of populations that share similar social determinant and health characteristics using Diagnosis Related Groups (DRGs), Clinical Risk Groups (CRGs), or other classification systems to assess where costs and progression of disease are different. Further, these identified classification systems can be used to match people receiving services with case workers and care providers who have skill sets that match service needs.
4. Increase accuracy of data using geospatial techniques to create health score cards at micro- (neighborhood or block) rather than macro-level (town, county). This increased micro-level data accuracy will enable us to move from describing communities at the town level to being able to identify local concentrations of persons with specific health, income and environmental characteristics within individual neighborhoods, buildings, or families.
To learn more about how NextGate’s market-leading EMPI can be a foundational data tool for understanding and serving your population, visit www.nextgate.com.