ASAP PoC Improved Civil and Military prehospital Point-of-Care decisions through Data Fusion and AI

Important results from the project

The project largely met the set goals, but for some goals the objectives had to be adjusted and reprioritized due to limitations in the form of long lead times to access clinical data and unforeseen changes in personnel at the participating parties. The project has increased knowledge about how AI models can be developed in triple helix collaboration and how they can be practically used by ambulance personnel to assess critical patients. It has also led to a new project in defense medicine.

Expected long term effects

We hope that our work on mapping the use of AI methods and practical implementation of interfaces contributes to use in future prehospital healthcare. We believe that the legal analysis and development of MAIN (Model for AI utilizatioN) will provide increased opportunities for healthcare solutions to go from research to market. We expect that new decision support for trauma and stroke will lead to improved care and reduce mortality and morbidity for the patients.

Approach and implementation

The project was implemented through 8 work packages: 1) Orchestration 2) Clinical process analysis & requirements spec 3) AI algorithms development 4) ASAP PoC system environment 5) MAIN (Model for AI utilizatioN) 6) Simulations & evaluating prototypes 7) Legal and privacy issues 8) Dissemination. Existing knowledge from literature was obtained through scientific review articles, new knowledge obtained through workshops and practical tests with clinicians through Digital Health Sandbox (DHS).

External links

ASAP-PoC (point of care) - IoT Sverige