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AI-based system for the digital emergency room of the future

The healthcare systems of tomorrow will be increasingly based on digital applications. Artificial intelligence and the availability of data can make a significant contribution to ensuring quality medical care and treatment in the future as well. The fortiss project ZNAflow is focusing exactly on this issue by paving the way for an AI-based assistance system in the central emergency room that is designed to improve the quality of treatment over the long-term and effectively relieve the ER staff.
 fortiss scientist Norman Schaffer
Scientist Norman Schaffer from the Requirements Engineering competence filed, is in charge of the ZNAflow project at fortiss.

The central emergency room (ER) is an important starting point for the acute treatment of medical emergencies. In this area, efficient workflows and timely provisioning of resources such as staff and medical technology are of the utmost importance. If the hospital experiences an unusually high    patient load for example, the medical treatments must be carried out in an especially targeted manner to prevent a shortage of patient care or overworked staff.

AI-based systems can support and improve clinical processes

Artificial intelligence is playing a key role in the digitalization of the German healthcare system because it allows more efficient design of the hospital workflows. With this in mind, the ZNAflow project involves the research and testing of AI-based assistance systems within the central ER. The project is intended to help staff identify critical bottlenecks at an early stage and initiate partly-automated, targeted measures, including changes in the shift schedules, the timely transfer of patients or reserving diagnostic capacity. This approach is aimed at significantly improving patient care according to the urgency of treatment while supporting the ER staff.

To develop the assistance system within the ZNAflow project, researchers are analyzing the use of various data sources such as internal historical and current from the ER, data from the public healthcare system, weather data or information related to upcoming large-scale events. fortiss is providing support in the continuous survey of requirements for a future prototype. Norman Schaffer and his fellow researchers in the Requirements Engineering and Machine Learning field of competence are therefore developing a model for forecasting patient loads. They are also working with the consortium on the ethical, legal and social aspects of using data-based solutions in emergency room environments.

Hospital digitalization means first and foremost change for people

With the data-based support of process management, the goal is to open up further opportunities for optimization of the healthcare system over the long-term. That means interactive assistance systems, such as the one being developed within the ZNAflow project, can ensure reliable workflows in a wide range of clinical areas. In the future this will make a major contribution to sustainably improving the quality of patient care and the quality of work within the medical professions.

ZNAflow is being financed by the German Federal Ministry of Education and Research (BMBF) within the framework of the initiative “Fighting the impacts of corona, ensuring prosperity and strengthening future capabilities”, and through the research program “AI-based assistance systems for process-supported healthcare applications”. Other partners in the project are AGAPLESION gAG, AGAPLESION evangelisches Krankenhaus in Mittelhessen gGmbH, DOCYET GmbH and the Technical University of Munich (TUM), which acts as the network coordinator.

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