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Health Care Management Science

, Volume 17, Issue 3, pp 284–301 | Cite as

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

  • Andreas Bayerstadler
  • Franz Benstetter
  • Christian Heumann
  • Fabian Winter
Article

Abstract

Predictive Modeling (PM) techniques are gaining importance in the worldwide health insurance business. Modern PM methods are used for customer relationship management, risk evaluation or medical management. This article illustrates a PM approach that enables the economic potential of (cost-)effective disease management programs (DMPs) to be fully exploited by optimized candidate selection as an example of successful data-driven business management. The approach is based on a Generalized Linear Model (GLM) that is easy to apply for health insurance companies. By means of a small portfolio from an emerging country, we show that our GLM approach is stable compared to more sophisticated regression techniques in spite of the difficult data environment. Additionally, we demonstrate for this example of a setting that our model can compete with the expensive solutions offered by professional PM vendors and outperforms non-predictive standard approaches for DMP selection commonly used in the market.

Keywords

Health insurance Selection for disease management programs Predictive modeling Generalized linear model Comparison of methods 

Notes

Acknowledgments

We would like to thank the health insurance company concerned for providing us claims data and the three PM vendors for participating in the test.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Andreas Bayerstadler
    • 1
  • Franz Benstetter
    • 1
  • Christian Heumann
    • 2
  • Fabian Winter
    • 1
  1. 1.Munich Health, Munich ReMunichGermany
  2. 2.Institute of StatisticsLudwig-Maximilians-Universität MünchenMunichGermany

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