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For Insurers & Benefit Plans

Claims Cost Calculator

Estimate what unmanaged metabolic conditions may be costing your plan in excess claims, and what proactive monitoring could realistically offset.

Your covered population

Assumptions below are pre-filled from published research and are fully editable. Adjust them to match your own claims data for a more accurate estimate.

Diabetes carries a much higher published excess cost per member than general obesity-related conditions, since it includes insulin, supplies, and related medications.
Total covered members, or the covered group you're evaluating a program for.
Roughly what share of your covered population has this condition. Use your own claims data if you have it.
Of the excess claims cost, roughly what share proactive monitoring and early intervention could realistically offset. A conservative starting estimate is 15 percent.
How much more, on average, a member with this condition costs the plan per year compared to a member without it. The pre-filled figure is a published average, adjust if you know your own.
What it would cost per covered life, per month, to offer the monitoring program. Leave blank to see gross savings only.

Your estimate

Covered members with this condition --

Covered lives × the prevalence rate you entered.

Total current excess claims cost --

Affected members × the excess annual cost per affected member. This is what unmanaged metabolic conditions are estimated to be adding to your claims spend today.

Gross annual savings potential --

The total current excess cost × the claims reduction rate you entered. This is the value proactive monitoring could realistically offset, before subtracting program cost.

This is an early-stage estimate. A pilot program gives you real numbers specific to your covered population.

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This estimate uses editable, published-range assumptions for condition prevalence, excess claims cost per affected member, and estimated claims reduction from proactive monitoring. It is not specific to your plan's actual claims data, and is not a guarantee of savings or outcomes. Actual results depend on population composition, program design, and participation rates.