What is predictive modeling primarily used for in health benefits?

Predictive modeling is primarily utilized in health benefits to identify potential high-risk and high-cost users. This technique employs statistical algorithms and machine learning to analyze data from various sources, including medical history, demographic information, and utilization patterns, to forecast which individuals are likely to incur significant healthcare expenses in the future.

By identifying these high-risk individuals early, organizations can implement targeted interventions, preventive care programs, and wellness initiatives aimed at managing health risks and reducing costs. This proactive approach not only improves health outcomes for employees but also helps organizations allocate their resources more effectively and create strategies that may lower overall healthcare expenditures.

The other options serve different purposes: calculating average healthcare costs focuses on understanding overall expenditure; determining employee satisfaction evaluates the perceptions and experiences of employees regarding their benefits; and estimating future benefit plan designs involves forecasting changes to benefit offerings based on trends and organizational needs. However, none of these directly leverage predictive modeling in the same way that identifying high-risk and high-cost users does.