Data Integration & Readiness
Preparing clinical and claims datasets required for quality evaluation, aligned to measure specifications.
Quality measures sit at the heart of Value-Based Care programs, yet calculation accuracy and consistency remain persistent challenges.
Organizations commonly struggle with:
As a result, quality measures often become points of contention instead of trusted decision inputs.
Enabling quality measure calculation goes beyond reporting—it requires robust data engineering and deterministic logic.
This use case typically involves the ability to:
When done correctly, quality measure calculations become reliable building blocks for analytics, reporting, and downstream workflows.
We approach quality measure calculation as a data and logic enablement challenge, not as a packaged measurement product.
Our typical approach includes:
Preparing clinical and claims datasets required for quality evaluation, aligned to measure specifications.
Operationalizing value sets, code mappings, and measure definitions in a controlled and maintainable way.
Designing deterministic calculation pipelines that support consistent execution and refresh cycles.
Ensuring calculation outputs can be traced back to source data and logic to support audit and review needs.
This approach enables quality measures to be transparent, explainable, and scalable.
In the walkthrough, you’ll see a simulated visual demonstration of how quality measure calculation enablement typically works. The walkthrough focuses on calculation enablement patterns, not a pre-built measurement engine.
Get a short walkthrough showing how care gap management and care plan workflows can be enabled using interoperable data, analytics, and automation.