Digital Health

Fraunhofer FIT has developed a toolbox for the rapid development of digital health applications. This integrates applications, methods and solutions from several national and European research projects. It allows to build cross-sector networked Medical Data Spaces that can be used to quickly meet urgent needs of the digital healthcare market. 

In the last few years, the demand for digital health applications has been rising steadily. The COVID-19 pandemic in particular has helped to accelerate the use of such systems. An especially promising approach in this area is to build networked health information systems, also known as Medical Data Spaces.

These systems enable seamless integration and exchange of health information, thus providing a comprehensive view of health data and improving the efficiency and quality of medical care. They can be used profitably in clinical studies, for genome data, in care and nursing, in the analysis of patient risks or for prognoses and decisions on medical care.

The Digital Health department of Fraunhofer FIT has been conducting research in the field of integrated healthcare for many years. Based on the results of various national and EU-wide research projects, we have developed a wide range of technologies and methods and integrated them into a toolbox. It allows to quickly implement new services that specifically address the different requirements of doctors, nursing staff, patients and relatives. Current standards of national and EU-wide health data spaces, the legal framework for medical devices and clinical trials, as well as data protection are taken into account.

One example is the patient chatbot developed in our AI-NET-PROTECT project, which lets patients access the health information system of GPs and specialists when and where they need to see their individual data. In order to strengthen the users' trust in this digital health solution, the chatbot ensures that health data are handled in strict confidentiality and in compliance with the highest security standards.

Another example of an application that we implemented using the toolbox is an ML service for IT-supported care consultation visits. It was developed and tested in the EFRE-NRW project INGE – integrate4care. This ML service supports home care and records the situation of people in need of care and their relatives.