Interoperability & Data Ingestion
Integrating SDoH data alongside clinical and claims sources using standards-based and pragmatic ingestion patterns.
In theory, SDoH plays a critical role in population health and Value-Based Care outcomes. In practice, it is one of the hardest data domains to operationalize.
Healthcare teams often struggle because:
As a result, SDoH data is frequently collected but rarely trusted or used consistently in downstream analytics and care workflows.
Enabling SDoH analytics typically involves more than simply adding new data sources.
It requires the ability to:
This use case sits at the intersection of interoperability, data engineering, and analytics readiness.
We approach SDoH analytics as a data foundation and enablement challenge, not a point solution.
Our typical approach includes:
Integrating SDoH data alongside clinical and claims sources using standards-based and pragmatic ingestion patterns.
Standardizing SDoH attributes, resolving inconsistencies, and applying governance controls to improve data trust.
Structuring longitudinal datasets that link SDoH factors with utilization, quality, and risk signals.
Preparing datasets that can be reliably consumed by population analytics, care management logic, or reporting workflows.
This approach ensures SDoH data becomes usable and defensible, rather than isolated or experimental.
In the walkthrough, you’ll see a simulated visual demonstration of how this use case is typically enabled. This walkthrough focuses on approach and architecture, not a pre-built product.
Get a short walkthrough showing how care gap management and care plan workflows can be enabled using interoperable data, analytics, and automation.