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Digital, Innovative, Sustainable, and Knowledge-based Acute Municipal Healthcare Services Illness and Trauma

Acute Disease Clinical Trial

Official title:
Developing and Implementing Digital, Innovative, Sustainable, and Knowledge-based Municipal Healthcare Services in Acute Illness and Trauma

Clinical Trial Summary

Through introducing physicians in front in the medical assessment and decision-making processes in acute and sub-acute illness in the municipalities, as well as including machine learning in analyzing prospective and retrospective data, the project will develop and implement innovative and knowledge-based digital diagnostic tools and decision-making support systems to be used in the municipalities. As such, the project will contribute to early identification of severe illness, prevent deterioration of disease, and facilitate early medical intervention.

Clinical Trial Description

The overall objective of the project is to determine if and how innovative digital decision-making tools based on artificial intelligence (AI) can help to develop more accessible, efficient, cost-effective, and sustainable municipal healthcare services. More specifically, the project aims are: - to explore different outcomes of a municipal rapid response car manned with dedicated physicians - to compare outcomes from Norwegian and Swedish out-of-hospital EMS in acute illness - to explore how decision-making tools based on AI can be used to optimize dispatch - to explore how decision-making tools based on AI can assist dispatched personnel and in prehospital care - to determine the quality and efficacy of the different systems explored through cost-analyses and exploration of stakeholders' experiences with the decision-making tools ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT05708768
Study type Observational
Source Ostfold University College
Contact Ann-Chatrin L Linqvist, Prof
Phone +4741668797
Email ann.c.leonardsen@hiof.no
Status Recruiting
Phase
Start date October 1, 2022
Completion date December 31, 2027
View Details
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