A first concern with the proactive approach is its cost/benefit ratio. Although all members will be offered the program, only a subset will engage.
- Of those who do engage, only some of them will improve their health status.
- Of those who do improve their health condition, that improvement will be to varying degrees.
With all this variability, it makes sense that a payor cannot be certain that the cost of investing in those programs is balanced by its benefits.
Overcoming Uncertainty
One solution to help overcome this hesitation is the application of data analytics.
- Regular assessments of claims data can show what the clinical impact and cost of the program actually was for each category assessed.
- This can also reveal trends over time to see general improvements associated with the program.
Beyond the data for populations, this reporting also reduces uncertainty by showing the conditions and costs of a member population down to the individual. Successive years’ comparisons can show to what degree those proactive pro- grams improved clinical outcomes and reduced costs, per person.
Knowing who engaged, under what clinical conditions, at what cost, and resulting in what outcome provides the level of detail to alleviate uncertainty for the payor.
Link to Full Article in the Journal of Compensation and Benefits:
