[Prevention and cost control in the German healthcare sector].

Background: The German healthcare system is struggling with increasing costs. Besides the current extra burden due to the corona pandemic, the vast majority of Germans pursue unhealthy lifestyles which will lead to additional morbidity and costs in the future. Objectives: This contribution sketches out an idea, on how analyses on claims data from the Statutory Health Insurance (SHI) in Germany may contribute to better usage of preventive health services to counteract the onset and progress of morbidities and hence ensure stable premium income from the insured. Effective health communication may further enable demand for preventive measures. Materials and methods: An idea is developed and discussed in which, in addition to the existing possibilities of the SHI to work towards preventive health behavior, results of secondary data analysis may be used for preventive measures and behavior. Results and conclusions: A machine-learning-based analysis is the core of a class of prediction models for prevention of illnesses. The models exploit the information from routine data and provide recommendations for prevention services, which in turn may be promoted to the insured via targeted, tailored, and personalized communication, e.g., via mHealth apps. The high potential for cost reductions as well as the possibilities to exploit them via data analytics provide a promising perspective for sustained cost control in the healthcare sector.