Population Segmentation
| Segment | Characteristics | Intervention Strategy |
|---|---|---|
| Healthy (60–70% of population) | Low utilization; low cost | Prevention; engagement; maintain health |
| At-risk (15–20%) | Elevated risk factors; moderate utilization | Disease prevention; health coaching; early intervention |
| Chronic condition (10–15%) | Diagnosed chronic conditions; high utilization | Disease management; care coordination; medication adherence |
| High-cost (1–5%) | Complex conditions; catastrophic claims | Case management; care coordination; stop-loss management |
Predictive Analytics
Predictive analytics uses claims data and other information to identify members who are likely to become high-cost in the future. Members with multiple chronic conditions, recent ER visits, or medication non-adherence are at high risk of future high-cost events. Early intervention for these members can prevent costly hospitalizations and ER visits.
Privacy Protections
Compliance Note
Claims data analytics must use de-identified data to protect employee privacy. The employer should receive aggregate, de-identified reports — not individual-level data. HIPAA prohibits using PHI for employment purposes. Work with your TPA or analytics vendor to ensure all reports are properly de-identified before they are provided to the employer.