CKM Benefit Design Modeler

The Clinical Knowledge Manager (CKM) system incorporates a sophisticated benefit design modeling capability, allowing yours to evaluate “what-if” assumptions concerning the financial impact of prospective changes in their health and disability plan designs.

The benefit design modeler allows you to:
a) Forecast the cost and utilization impact of changes in current health plan designs, and b) Model the likely cost and utilization pattern of new health plan designs.

These models may incorporate demand-side assumptions about the shift of employee enrollment across health benefit plans. To support these capabilities, users may choose from a number of forecasting / rating methodologies:
• Manual rating
• Experience rating
• Blended rating

The benefit design modeler incorporates the latest approaches to consumer directed healthcare through HRA and FSA accounts. You may model the impact of prospective change on employee out of pocket expense as well as on overall you cost. You may also evaluate the impact of alternative HRA plan designs allowed under current law, such as putting an FSA account first before the HRA is tapped.

Manual and Blended Rating
To support manual rating and blended rating, the CKM system incorporates standard actuarial approaches to medical and prescription drug cost trend rates, to area adjustment factors (3 digit zip or SMSA), stop loss options, and so on.

The CKM system is able to import details of plan design such as tier structures, benefit coverages, network offerings, and exclusions/limitations from computerized procurement systems. By importing rather than entering plan design details, the user may focus upon elements of change rather than documentation of existing provisions.

Experience and Blended Rating
To support experience rating and blended rating approaches, the benefit design modeler relies upon the data mining capability of the CKM system. The CKM analytic engine assembles and organizes appropriate retrospective data from within the data warehouse to populate the historical component of the rating model.


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