chronic-condition-cohorting

Segment patient populations by applying risk models to clinical registries and claims data, grouping them into chronic disease cohorts stratified by severity and multi-morbidity patterns for targeted care management.

Chronic disease patient cohorting requires structured tables for claims and encounter data, enrollment and eligibility, pharmacy claims, along with optional lab results and demographics to build accurate risk stratification models.

Risk stratification for chronic care patients works by analyzing claims data and clinical registries to assign severity levels, identifying multi-morbidity patterns like diabetes with co-occurring kidney disease for proactive care enrollment.

Yes, you can prepare population segments for care management programs by analyzing chronic condition prevalence and grouping patients into disease-specific cohorts based on high utilization patterns and clinical risk indicators.

The best way to identify diabetic patients with high utilization patterns is to use patient cohorting techniques that analyze pharmacy claims and encounter data to stratify severity and detect multi-morbidity indicators.

Lab results are not required to stratify patients by chronic condition severity, but adding optional lab results and demographics to structured claims and enrollment data enhances risk model accuracy.