Entwicklung eines selbstlernenden Risikoscores an Real-World-Datenquellen: Die RABATT-Studie

The RABATT study (risk scores for an algorithm-based objectifiable clarification on treatment success and treatment recommendation) is funded for 3 years from 2019 to 2022 by the Innovation Fund of the German Federal Joint Committee (Gemeinsamer Bundesausschuss). The RABATT study aims to develop intelligent methods to use available health insurance claims data and registry data in the treatment of patients with peripheral arterial disease (PAD). The study consortium will develop self-learning algorithms and mobile applications to overcome the paucity of evidence and provide informed objective clarification and optimal choice of treatment for patients with PAD. This project further aims to answer essential questions in terms of data privacy and liability rights, social law and ethics in research and quality improvement. This article provides an overview on relevant questions and methodological project stages.