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

• Published in: Health care management science Vol. 17; no. 3; pp. 284 - 301
• Main Authors: Bayerstadler, Andreas, Benstetter, Franz, Heumann, Christian, Winter, Fabian
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.09.2014; Springer Nature B.V
• Subjects: Age Factors; Business and Management; Chronic Disease; Chronic illnesses; Comparative analysis; Cost analysis; Cost control; Costs; Customer relationship management; Datasets; Disease Management; Econometrics; Ethnic Groups; Generalized linear models; Health Administration; Health care expenditures; Health Informatics; Health insurance; Health insurance companies; Humans; Insurance Carriers; Insurance companies; Insurance industry; Insurance, Health; Management; Models, Economic; Occupations; Operations Research/Decision Theory; Patient Compliance; Patients; Program Evaluation; Regression analysis; Sex Factors; Health insurance; Selection for disease management programs; Generalized linear model; Predictive modeling; Comparison of methods
• ISSN: 1386-9620; 1572-9389
• DOI: 10.1007/s10729-013-9246-y

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.