Data Integration & Longitudinal Structuring
Aggregating multi-source data into patient-centric timelines that support feature generation.
Risk stratification is foundational to population health and Value-Based Care strategies.
Yet many initiatives struggle to move beyond pilots or inconsistent results.
Common challenges include:
As a result, risk stratification outputs are often questioned, difficult to reproduce, or hard to operationalize.
Enabling ML-based risk-stratified cohorts is primarily a data engineering and feature readiness challenge, not a modeling exercise alone.
This use case typically involves the ability to:
When enabled correctly, risk-stratified cohorts become stable, interpretable inputs for downstream analytics and workflows.
We approach risk stratification by separating data readiness from model ownership, enabling flexibility and trust.
Our typical approach includes:
Aggregating multi-source data into patient-centric timelines that support feature generation.
Preparing standardized, versioned feature sets that can be reused across models and cohorts.
Enabling repeatable cohort definitions and refresh mechanisms aligned to business and clinical needs.
Ensuring cohorts can be reliably consumed by ML models, analytics, and care workflows without re-engineering.
This approach keeps risk stratification modular, explainable, and scalable.
n the walkthrough, you’ll see a simulated visual demonstration of how ML-based risk-stratified cohort enablement typically works. The walkthrough focuses on enablement patterns and data design, not a proprietary model.
Get a short walkthrough showing how care gap management and care plan workflows can be enabled using interoperable data, analytics, and automation.