Sepsis is a common and severe condition that affects up to 40 000 people in Sweden every year with a mortality of up to 20%. Timely identification and treatment are crucial for the outcome. The majority of the patients have their first contact with the prehospital care, and should ideally be identified already in the ambulance. Screening tools that are used today rely on vital signs and are criticized for low precision. A prehospital AI based clinical decision support system (CDSS) have great potential to increase the accuracy of the early assessment and thus shorten the time to treatment, increase the chance of survival and reduce long-term complications. Patient records from earlier clinical research will be used to develop an AI based CDSS. Selected AI methods will be evaluated in terms of performance and user experience. The CDSS will be incorporated in an existing ambulance IT-support system. The system should use real-time data and notify the user if there is a risk of sepsis. This approach differs from the current situation in several ways. Firstly, the paramedics do not have to suspect sepsis and secondly, also the patient’s symptom description can be included. The latter has proved to be of great prognostic value. Visualization and utilization plays a significant role in the project. Presentation of the results from the CDSS as well as impact on the care process will be evaluated together with target users. Another important part of the project is to investigate regulatory issues and long term management of the system.
Docent vid Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Biomedical Electromagnetics
Professor vid Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers), Software Engineering for Cyber Psysical Systems
Funding Chalmers participation during 2018–2020 with 516,088.00 SEK
Funding Chalmers participation during 2018–2020 with 354,604.00 SEK
Areas of Advance