06 Aug
In the shift from volume to outcome, healthcare leaders recognize that true wellness extends beyond the clinic—into homes, neighborhoods, and social environments. The CMS Accountable Health Communities (AHC) Model has demonstrated the power of systematically screening for health‑related social needs (HRSNs) and connecting patients to community‑based organizations (CBOs).
Between 2017 and 2022, AHC screened more than 1.1 million Medicare and Medicaid beneficiaries across 28 communities, testing a bold hypothesis:
Could systematically identifying and addressing social needs improve health outcomes and reduce costs?
The final evaluation, released in 2024, answers that question with data — and confirms what many frontline teams have long suspected:
Yes, it can.
But only when backed by the right workflows, partnerships, and — critically — data analytics.
The AHC Model at a Glance: What Worked
Screenings: Over 1.1 million individuals were screened using the Health-Related Social Needs (HRSN) tool.
Referrals: Nearly 412,000 individuals had at least one core need and were offered a referral.
Navigation: Approximately 150,000+ high-risk patients received dedicated navigation services from community health workers (CHWs) or care coordinators.
So what did the data show?
- Navigation services led to significant reductions in total Medicare and Medicaid expenditures:
- The greatest improvements were seen among:
- Dual-eligible beneficiaries
- Black and Hispanic patients
- Those with multiple social needs
These aren’t just metrics – they’re proof that data-informed social care saves lives, improves equity, and reduces costs.
Here’s how analytics can elevate each stage of the AHC workflow:
1. Connecting Patients to the Right Resources
Challenge: Millions of Medicare and Medicaid beneficiaries live with unmet social needs such as food insecurity, housing instability, unreliable transportation. Universal HRSN screening surfaces these challenges, but how do we prioritize the highest-risk patients and match them to the CBO best able to help?
SDOH Analytics Solution:
- Risk Stratification: By combining HRSN screening data with clinical utilization metrics (e.g., ED visits, chronic condition burden) and demographic factors, analytics models can score each patient’s social risk. Those with the highest composite scores receive immediate navigation support.
- Intelligent Referral Matching: Leverage our Care Coordination Platform to tap a FHIR-native directory—categorizing CBOs by service type, capacity, geography, language, eligibility—and surface the optimal referral for each patient.
- Workflow Automation: Integrated into the EHR via Social Data on FHIR, analytics can flag high‑risk patients on admitting or discharge, auto‑generate community referral summaries, and trigger care‑manager alerts—all without manual lookups.
Impact: Prioritized, precise referrals increase the chance that patients not only receive outreach but also engage with services—laying the foundation for improved outcomes and reduced avoidable utilization.
2. Evaluating the Effectiveness of Interventions
Challenge: The AHC Model’s third evaluation showed navigation reduced inpatient admissions and ED visits, even though only ~40% of navigated patients fully resolved their HRSNs. How do we quantify which social interventions actually move the needle on health?
SDOH Analytics Solution:
- Closed‑Loop Tracking: Using FHIR Task and CarePlan resources, community service events (e.g., “food pantry visit,” “housing intake”) flow back into the EHR. Analytics dashboards measure referral completion rates, time to first service, and the fraction of patients whose needs shift from “unresolved” to “resolved.”
- Outcome Correlation: By linking social‑service utilization data (via FHIR Encounter) with clinical outcomes – ED visits, readmissions, HbA1c trends – analytics can attribute changes in health metrics to specific interventions. For instance, patients who attend ≥3 nutrition counseling sessions may see a 12% drop in diabetic complications.
Impact: Rigorous, data‑driven evaluation illuminates “what works”, enabling care teams to refine referral protocols, allocate navigator time to high‑yield activities, and justify continued investment in social care.
3. Informing Policy and Practice at the Community Level
Challenge: Even the best‑designed referral programs falter if the local ecosystem lacks sufficient resources—food banks overwhelmed, housing programs at capacity, transportation options scarce. How can health systems and policymakers identify structural gaps and advocate for targeted investments?
SDOH Analytics Solution:
- Geospatial Needs Mapping: Aggregating HRSN screening scores by ZIP code or census tract, analytics layers social‑risk heatmaps atop community resource inventories. This pinpoints service deserts –neighborhoods with high food‑insecurity scores but no nearby food pantries within a 2‑mile radius.
- Capacity Monitoring: Real‑time referral flux (via FHIR ServiceRequest) and completion status feed a “CBO Capacity Index.” Areas where ≥70% of referrals bounce unfilled signal urgent need for expanded services or new vendor partnerships.
- Trend Analysis: Year‑over‑year comparisons reveal emerging social crises—e.g., spike in utility‑need screenings during winter months. Analytics can model seasonal patterns and preemptively mobilize emergency assistance.
- Data‑Driven Advocacy: Health systems can export anonymized, aggregated SDOH reports – like the NY 1115 Waiver Briefs- to policymakers and funders, showing exactly where to direct community health grants, workforce development dollars, or affordable housing initiatives.
Impact: SDOH analytics transforms raw screening data into actionable community insights, guiding resource allocation, policy change, and cross‑sector collaboration to address root causes of inequity.
Bringing It All Together with Social Data on FHIR
The linchpin for scalable, sustainable SDOH analytics is interoperability. By adopting and leveraging FHIR resources (Observation, QuestionnaireResponse, ServiceRequest, CarePlan, Task) through a FHIR Facade, healthcare organizations can:
- Standardize the capture of social‑risk data across clinical and community systems.
- Automate referral workflows with discrete, computable data.
- Aggregate and analyze outcomes without custom interfaces or manual exports.
- Share insights across stakeholders namely payers, public health, CBOs, while preserving patient consent and privacy.
This FHIR‑native foundation accelerates each phase of the AHC Model, from identifying high‑need individuals, to measuring intervention efficacy, to shaping community health strategy.
Healthcare leaders, population health teams, and community coalitions: it’s time to move beyond one‑off social needs pilots. Embed SDOH analytics, underpinned by Social Data on FHIR, into your AHC workflows. By doing so, you’ll deliver precision referrals, evidence‑driven evaluation, and data‑guided policy, driving better health, lower costs, and true equity for the communities you serve.
The Aigilx Health Perspective: Building the Future of SDOH Analytics
At Aigilx Health, we’ve spent years immersed in the complexities of healthcare interoperability and social care coordination. We understand that solving for SDOH isn’t just about identifying needs — it’s about connecting data, workflows, and people in meaningful ways.
While our analytics platform is currently in development, it’s being designed from the ground up to meet the real-world challenges surfaced by models like AHC:
- FHIR-native architecture ensures seamless integration with EHRs, HIEs, and CBO platforms — enabling interoperability at scale.
- Our forthcoming tools will enable care teams to track screening, referrals, and resolution outcomes with clarity and precision.
- We’re building with a focus on community-level insights, referral intelligence, and closed-loop feedback to power more strategic interventions.
- And our infrastructure prioritizes data governance and patient privacy — essential for trust and compliance in multi-stakeholder ecosystems.
Most importantly, we bring deep expertise in SDOH implementation strategy, CMS-aligned workflows, and population health analytics. Whether you’re launching a new initiative or scaling an existing one, we can help you architect systems that support whole-person care with measurable impact.
The future of health is data-driven, socially informed, and interoperable. At Aigilx Health, we’re not just watching it unfold, we’re building for it.
Aigilx health specializes in developing Interoperability solutions to create a healthcare ecosystem and aids in the delivery of efficient, patient-centric and population-focused healthcare.
