Platform for the rapid development of digital health applications

Demand for digital health apps has been steadily increasing in recent years. In particular, the COVID-19 pandemic has accelerated the use of such systems. Networked health information systems that create "medical data spaces" can be profitably used in clinical trials, for genomic data, in care delivery, in patient risk analysis, in medical care forecasting and decision-making, and in short-term and long-term planning.

The Digital Health department of Fraunhofer FIT has been conducting research in the field of integrated healthcare for many years and has developed a wide range of technologies and methods in several national and international research projects. The toolbox now presented integrates these applications. It allows to quickly implement new services that can specifically address the different requirements of physicians, nursing staff, patients, and relatives.

"An important aspect – besides user-friendliness – is the interoperability of the services we develop. We take current standards of national and EU-wide health data spaces, the legal framework for medical devices and clinical trials, as well as data protection into account," Prof. Dr. Thomas Berlage, head of the Digital Health department of Fraunhofer FIT, points out.

One 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 (https://www.gewi-institut.de/projekte/inge/). This ML service supports home care and records the situation of people in need of care and their relatives. Further components are results of the European projects CAREPATH (https://www.carepath.care) and ESCAPE (https://escape-project.org), where we work on IT platforms that improve clinical practice in the treatment and management of multimorbid patients.

Fraunhofer FIT also presents a new pain app designed to replace patient interviews using paper forms. The app was developed in cooperation with pain physicians. It makes the patients’ pain history data digitally available for machine learning processes., We also developed a service platform for the evaluation and training of models. It can integrate further data sources and machine-generated models, thus providing maximum benefit for patients and physicians.

MEDICA-tradefair.com; Source: Fraunhofer-Institut für Angewandte Informationstechnik FIT