For many employers and benefits brokers, predictive modeling seems to be a jumble of confusing math.
But learning a little about how predictive modeling works may provide inspiration for new ways to use these tools, which improve on traditional forecasting techniques that rely entirely on demographic information and financial factors.
Modern predictive risk models differ from the old models in 2 important ways.
First, todays predictive risk models make heavy use of clinical information drawn from health care claims. The expanded breadth of detail makes a difference in the quality of a forecast: key utilization measures are diagnosis, procedure and pharmacy codes as well as dates and place of service, and basic patient demographic information.
Second, the new predictive risk models attempt to identify future high-cost individuals, not simply count cases that already have generated huge bills. This represents an important departure from experience-rating mechanisms that simply adjust prior period claims to reflect anticipated cost and utilization trends.
The basic strategy for applying a predictive risk model is easy to understand: You classify plan members by health status according to clinical indicators, then you use assign factors called risk weights that reflect each classifications anticipated resource utilization. Although anticipated resource utilization often refers to cost, it might also refer to inpatient days, office visits, surgeries or prescriptions, for example.
A model might, for example, handle weighting by assigning employees with uncontrolled diabetes a high number of debits and employees in perfect health a low number of debits. The average number of debits is calculated across the entire group and then compared to the expected average for all groups. In practice, the schedule of debits should also reflect the fact the predictive risk model cannot account for all of the anticipated variability in cost.