A predictive modeling approach to increasing the economic effectiveness of disease management programs

Area of Science:

• Health Insurance Business
• Data-Driven Management
• Predictive Modeling

Background:

• Predictive Modeling (PM) is increasingly vital in global health insurance for customer relationship management, risk evaluation, and medical management.
• Optimizing disease management programs (DMPs) is crucial for economic efficiency within health insurance.
• Existing DMP selection methods often lack predictive power and can be costly.

Purpose of the Study:

• To illustrate a Predictive Modeling (PM) approach for optimizing candidate selection in disease management programs (DMPs).
• To demonstrate the economic potential of data-driven business management in health insurance through effective DMP selection.
• To present a Generalized Linear Model (GLM) as an accessible and stable PM technique for health insurance companies.

Main Methods:

• Development and application of a Generalized Linear Model (GLM) for predictive candidate selection in DMPs.
• Utilizing a small portfolio from an emerging country to test the model's stability and performance.
• Comparison of the GLM approach against sophisticated regression techniques and standard market practices.

Main Results:

• The proposed GLM approach demonstrates stability, even in challenging data environments common in emerging markets.
• The GLM-based PM strategy effectively optimizes candidate selection for disease management programs.
• The model competes with expensive professional PM vendor solutions and surpasses traditional non-predictive DMP selection methods.

Conclusions:

• Generalized Linear Models (GLM) offer a practical and effective solution for predictive modeling in health insurance DMPs.
• Data-driven candidate selection using PM, specifically GLM, can significantly enhance the economic viability of DMPs.
• This approach provides a competitive and superior alternative to existing methods for DMP selection in the health insurance industry.