Översikt
- Datum:Startar 16 januari 2026, 09:00Slutar 16 januari 2026, 13:00
- Plats:KB-salen, Kemigården 4, Chalmers.
- Opponent: Senior research fellow Mark van Heijl, the University Medical Center Utrecht, Netherlands
- AvhandlingLäs avhandlingen (Öppnas i ny flik)
For severely injured patients, minimizing time to definitive care is crucial to increase the chance of survival and to reduce the risk of lifelong disabilities. The ambulance team is therefore key to providing optimal care to injured patients, as the remaining flow of care depends on the accuracy in the patient assessment and the decision on transport destination. However, current tools used for patient assessment of injured patients function as checklists with limited accuracy. It is hypothesized that Artificial Intelligence (AI) is needed to find more valuable patterns in patient data and thereby improve the support for the ambulance team. This work therefore explores a prehospital Clinical Decision Support System (CDSS) that utilizes AI to predict the risk of a patient being severely injured at the incident scene.
First, an AI model named On Scene Injury Severity Prediction (OSISP) was developed and evaluated on Swedish trauma data. Next, the OSISP model was applied and evaluated on Norwegian trauma data to estimate performance and triage accuracy on future patients. Following model development, packaging OSISP as a CDSS was studied by exploring workflow integration in a workshop together with clinical and industrial representatives. Scientific literature was reviewed to find inspiration for how to communicate OSISP’s predictions to the ambulance team. Lastly, a tablet prototype was built and tested together with ambulance teams. The findings of this work demonstrate that OSISP has a theoretical value as a CDSS for ambulance teams during patient assessment of injured patients, both during model development and CDSS building, and offers a promising solution for future work to continue design refinement and initiate prospective evaluations.
First, an AI model named On Scene Injury Severity Prediction (OSISP) was developed and evaluated on Swedish trauma data. Next, the OSISP model was applied and evaluated on Norwegian trauma data to estimate performance and triage accuracy on future patients. Following model development, packaging OSISP as a CDSS was studied by exploring workflow integration in a workshop together with clinical and industrial representatives. Scientific literature was reviewed to find inspiration for how to communicate OSISP’s predictions to the ambulance team. Lastly, a tablet prototype was built and tested together with ambulance teams. The findings of this work demonstrate that OSISP has a theoretical value as a CDSS for ambulance teams during patient assessment of injured patients, both during model development and CDSS building, and offers a promising solution for future work to continue design refinement and initiate prospective evaluations.
