Effertz Tobias
Fakultät für Betriebswirtschaft, Institut für Recht der Wirtschaft, Universität Hamburg, Moorweidenstr. 18, 20148 Hamburg, Deutschland.
Pravent Gesundh. 2023 Mar 17:1-9. doi: 10.1007/s11553-023-01021-y.
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.
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.
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.
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.
德国医疗保健系统正面临成本不断增加的问题。除了当前因新冠疫情带来的额外负担外，绝大多数德国人都有不健康的生活方式，这将在未来导致更多的发病率和成本。
本论文概述了一种想法，即如何通过对德国法定医疗保险（SHI）的理赔数据进行分析，更好地利用预防性健康服务，以应对疾病的发生和发展，从而确保从参保人那里获得稳定的保费收入。有效的健康沟通可能会进一步激发对预防措施的需求。
提出并讨论了一种想法，即除了法定医疗保险现有的促进预防性健康行为的可能性外，二次数据分析的结果可用于预防措施和行为。
基于机器学习的分析是一类疾病预防预测模型的核心。这些模型利用常规数据中的信息，并为预防服务提供建议，进而可通过有针对性、量身定制和个性化的沟通方式（例如通过移动健康应用程序）向参保人推广。成本降低的巨大潜力以及通过数据分析利用这些潜力的可能性，为医疗保健部门持续控制成本提供了一个充满希望的前景。
