Rehabilitation (REHA) is a big cost driver in the Swiss healthcare system. A major contributor in the field of REHA are cardiac diseases. The need in cardiological REHA has changed significantly with the changing disease patterns and patient groups. Diagnoses have become more complex and patients older, and this trend continues.
This project aims at optimizing the entire REHA process in a clinic. The approach is to personalize the cardiac REHA of patients based on their condition upon their entry into the clinic. By analyzing past diagnoses, treatments and the resulting data, patient profiles are created and categorized. This data is complemented with demographic data as well as information about secondary diagnoses to define models for typical cardiac surgery patient profiles by using machine learning (ML) and artificial intelligence (AI). The end goal is to validate the existing hypothesises from NMS.