The current increase in the number of infections during the COVID-19 pandemic poses a particular challenge to hospitals, as important resources in the hospital environment such as beds in intensive care units (ICU), ventilators as well as medical staff are in short supply. The task of the egePan Unimed project (Entwicklung, Testung und Implementierung von regional adaptiven Versorgungsstrukturen und Prozessen für ein evidenzgeleitetes Pandemiemanagement) is to review and harmonize pandemic management concepts in Germany and internationally, to evaluate their practicability using scientific methods and to integrate them into a framework plan. The overriding goals are to ensure adequate resource management within a region in order to avoid inefficient use of intensive and inpatient care capacities.
The team around Prof. Hinz is significantly involved in the risk stratification and definition of COVID-19 risk groups and therefore strives for an optimized allocation of patients at the beginning of the disease. To achieve this goal, different approaches of explainable artificial intelligence are applied. The results will then be made available to the treating physicians in the form of an app or a web interface in order to provide them with the best possible support during treatment.