This is a great question, and I see “future” as the key word here. That’s because a health plan has to be looking ahead in order to reach its full potential for being effective, successful and cost-efficient.

Predictive modeling helps self-funded employers and plan sponsors do this. A self-funded plan is not like a fully insured plan where you receive incomplete data at year’s end with limited ability to reflect on what’s happened, after it’s happened. Instead, self-funding allows for predictive modeling, which lets you dig into complete data at any time to realize what your biggest risks are for the future and how to best manage them.

It works by doing a comprehensive review of data received through health risk assessments, biometric screenings and health claims (all in accordance with HIPAA guidelines to avoid identifying individuals). Things like diagnostic coding, prescription usage and lab test trends are studied to see what conditions will likely cost your plan the most to treat. These findings give employers a clearer picture of where to focus – and potentially adjust – their efforts in disease management and intervention.

The intervention part is big. Why so? Employers may discover a condition becoming more prevalent among their health plan participants, and since the data is accessible to them, they can be proactive about addressing it. This could mean everything from offering employees more educational resources to providing one-on-one health coaching … strategies meant to intervene early instead of waiting too long as costs add up.

Receiving (and reacting to) health plan data after the fact doesn’t allow much ability to plan for the future. Predictive modeling does.

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