Introducing Dynamic Scoring for Self-Sufficiency and Stronger Community Care Outcomes
Equipping healthcare stakeholders with the precision and insight to address community and individual-level needs.
As healthcare increasingly embraces integrated care models that address both medical and non-medical drivers of health, organizations need data that goes deeper—data that reflects real people, real circumstances, and the evolving nature of need.
Traditional measurement tools often fall short. They rely on aggregated, outdated datasets that miss the complexities within communities, leaving organizations without the precision required to design effective interventions or measure lasting impact.
Unite Us’ Self-Sufficiency models bridge that gap by delivering insights at both the individual and community levels that capture the complex and evolving realities of people’s lives. Instead of merely identifying where needs exist, these models estimate an individual’s ability to meet their own needs over time while uncovering broader patterns and trends within the community.
Why Granularity Matters
Across the country, communities are building systems of care that aim to foster long-term self-sufficiency—networks that empower residents to access resources, reduce dependency, and achieve long-term stability. To make those systems work, leaders need individual-level, predictive insights that reveal where barriers exist, how they intersect, and which interventions will make the greatest difference.
The Self-Sufficiency Score offers exactly that. Instead of relying on broad geographic averages, it uses person-level data to help healthcare systems, payers, and community organizations:
- Identify at-risk individuals earlier—before needs escalate into crises.
- Target interventions more effectively, ensuring resources reach those who will benefit most.
- Measure long-term progress toward self-sufficiency, not just short-term needs fulfillment.
When it comes to understanding community health, granularity matters. Unite Us’ models go beyond traditional frameworks by combining social, clinical, and behavioral data to give organizations the clearest possible view of their populations and the individuals within them.
How Traditional Tools Fall Short
Tools like the Area Deprivation Index (ADI) and the Social Vulnerability Index (SVI) have advanced understanding of community needs but remain limited.
The Area Deprivation Index (ADI)?
First developed by the Health Resources and Services Administration and later refined at the University of Wisconsin-Madison, the ADI measures socioeconomic conditions such as income, education, employment, and housing quality, using annual data. While useful for assessing neighborhood-level deprivation, it provides a static snapshot and lacks the flexibility to track individual or community change over time.
The Social Vulnerability Index (SVI)?
Developed in 2003 by the Centers for Disease Control and Prevention, the SVI identifies communities most at risk during disasters or public health emergencies. It aggregates five-year census data to assess social, economic, and demographic vulnerability at the census tract level—offering a broad but generalized view that doesn’t account for individual variability.
Shared Limitations
Both indices have proven useful but share limitations that can obscure true need and opportunity:
- Overgeneralized Data: Census block- and tract-level data can mask disparities within small areas or specific demographic groups.
- Lack of Cultural Considerations: Indicators such as housing or employment may hold different meanings across cultural and regional contexts.
- Lagging Data: Reliance on inconsistently refreshed census surveys means risk assessments may be based on outdated information.
- Misuse in Resource Allocation: Policymakers may over-rely on these indices, overlooking local insights and community expertise.
- Focus on Deficits Over Strengths: These measures highlight vulnerabilities but fail to capture assets, strengths, and resilience—the factors that drive self-sufficiency.
A Smarter, More Dynamic Solution: The Self Sufficiency Score
Introduced in 2018, Unite Us Population Insights is a proprietary predictive analytics solution that helps organizations understand and strengthen both individual and community self-sufficiency. By leveraging comprehensive person-level data—including social, economic, and health-related factors—it uses advanced modeling to reveal the drivers of need and opportunity, and turn those insights into strategies that build stability, enhance community resilience, and reduce healthcare and benefits costs.
How it Works
Powered by Population Insights, the Self Sufficiency Score is an individual-level composite score that evaluates how 12 factors—including food security, childcare needs, housing stability, employment, and other personal circumstances—affect an individual’s self-sufficiency. While derived at the individual level, these scores can also be aggregated to reveal patterns and trends at the community level, helping identify where support and resources are most needed.
Beyond pinpointing areas of vulnerability, the score also highlights where individuals and families are making progress towards stability and independence—helping communities focus not only on addressing needs but also on nurturing long-term self-reliance.
How It’s Used
Population Insights measures both individual needs and broader community trends—from housing and employment to engagement patterns and outcomes. Organizations use these insights to design personalized care plans, guide policy and funding decisions, and ensure every action moves individuals and communities closer to lasting stability. By connecting individual and community insights, Population Insights helps partners move beyond short-term relief toward long-term well-being and resilience.
Benefits of Unite Us’ Dynamic Predictive Analytics
With Population Insights and dynamic scoring, organizations can drive better health outcomes for both individuals and communities with:
- Tailored Interventions: Individualized insights enable targeted care plans that address root causes and accelerate progress toward self-sufficiency.
- Operational Efficiency: Scores integrate seamlessly into existing workflows, helping teams prioritize interventions and coordinate across healthcare and social service systems.
- Strategic Decision-Making: Actionable data supports smarter programming, funding, and policy decisions that strengthen community capacity.
- Improved Communication: Data-driven insights foster trust-based conversations between care providers and clients, empowering individuals to take ownership of their care journey.
Together, these capabilities build stronger, more coordinated systems of care that promote sustained resilience and well-being.
How We Made Our Predictive Models Even Better
To make Population Insights even more powerful, Unite Us introduced dynamic scoring—a major leap forward in understanding how needs evolve over time. Unlike static indices, dynamic scoring updates continuously as individuals engage with services and as community conditions shift. This provides a living measure of both personal progress and community resilience.
By applying advanced data science, Unite Us models accurately predict non-medical needs at both the individual and community levels. Healthcare systems, government agencies, health plans, and community-based organizations can use these insights to optimize resources, improve outcomes, and strengthen local systems of care.
Ultimately, these models do more than measure need—they empower communities to build the capacity, resilience, and independence required for a healthier, more sustainable future.
Building a Healthier, More Self-Sufficient Future
The future of community care depends on precision, adaptability, and collaboration. Unite Us’ dynamic scoring equips organizations with the insights they need to anticipate needs, design effective interventions, and measure real progress over time.
Learn how Unite Us can help your organization harness dynamic, person-centered data to strengthen communities and drive long-term well-being.
