title: A predictive modeling approach to increasing the economic effectiveness of disease management programs.
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.
authors.affiliation: Munich Health, Munich Re, Königinstraße 107, 80802, Munich, Germany, abayerstadler@munichhealth.com.
authors.forename: Andreas
authors.initials: A
authors.lastname: Bayerstadler
authors.forename: Franz
authors.initials: F
authors.lastname: Benstetter
authors.forename: Christian
authors.initials: C
authors.lastname: Heumann
authors.forename: Fabian
authors.initials: F
authors.lastname: Winter
mesh_terms: D000367:Age Factors; D002908:Chronic Disease; D019468:Disease Management; D005006:Ethnicity; D006801:Humans; D007343:Insurance Carriers; D007348:Insurance, Health; D018803:Models, Economic; D009790:Occupations; D010349:Patient Compliance; D015397:Program Evaluation; D012737:Sex Factors