Artificial Intelligence (AI) Support in Medical Emergency Calls

More than 12.000 patients suffer acute stroke in Norway every year, but less than half of them reach hospital within the current treatment window for thrombolysis. Stroke is the third-highest cause of death and the number one cause of severe disability requiring long time care at institutions. Consequently this has a high impact on society, patients and relatives, in addition to high costs related to care estimated to approximately 10 billion NOK per year. Although there are few studies on emergency medical communication centres (EMCC) in Norway, some have shown that the performance of the emergency medical communication centres can be improved. This project will seek to amend EMCC´s handling of acute stroke inquiries using artificial intelligence (AI), thus contributing to getting the patient to hospital in time for optimal treatments.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Stroke, Acute Apoplexy; Brain Behavior Brain Diseases Cardiovascular Diseases Central Nervous System Diseases Cerebrovascular Disorders Communication Communication, Multidisciplinary Disease Attributes Emergencies Nervous System Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Stroke Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Haukeland Universitetssykehus, Kirurgisk serviceklinikk, Nasjonalt kompetansesenter for helsetjenestens kommunikasjonsberedskap

Bergen, 5021, Norway

About this study

In this project, the investigators will collect data from all stroke patients discharged from Helse Bergen in 2019 (approx. 1000 patients) via the Norwegian Stroke Registry (NSR). For these patients, structured hospital data from Helse Bergen will be retrieved, and based on these and the spoken content of their emergency call regarding the stroke, the investigators will use machine learning to calculate the stroke risk. The connection of historical hospital data to the spoken words in the emergency call, amplifies the analysis of emergency calls in a novel way, in comparison to sound analysis alone.

After retrieving and connecting stroke patient data, the investigators train the deep network using data from 2019. Accordingly, testing will be performed based on patients from the first half of 2020. A separation of the data into training, test, and validation assures that our trained network does not over fit on the training data and can reproduce similar results on previously unseen patients. Finally, the investigators will compare the performance of the AI with the current system through statistical analyses on data from a period of approximately one year of live usage of the AI in AMK Bergen. This will enable us to evaluate to what degree the system is able to improve within the decision process of the EMCC operators in terms of sensitivity and specificity.

Summarized, the primary objective is to build a robust, working prototype of an AI system capable of real-time identification of acute stroke for improved assessment in emergency medical calls.

Our secondary objectives are:

To implement an AI system capable of providing fast prediction of whether a patient is suffering from acute stroke or not based on audio from emergency call and available data sources within the hospital records
To prove that AI systems can be used to assist and improve the triage decision procedure of the EMCC operator.

The anticipated result is to deliver fast (i.e. seconds) prediction scores to assist the EMCC operator in recognizing acute stroke patients, which provides an improved sensitivity and specificity compared to manual assessment only.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

All callers to medical emergency number 113 in Bergen

Exclusion criteria

Age <18

Treatment and study plan

Artificial intelligence on emergency calls

Other

AI listens to all calls to Bergen EMCC, detecting calls regarding possible stroke.

Primary outcomes

Stroke recognition in medical emergency calls

Time frame: Sept. 22 - Sept. 23

Survey AI's ability to recognize stroke, compared to the current system

Sponsors and collaborators

Lead sponsor

Haukeland University Hospital

Other

Collaborators

Helse Vest
Helsetjenestens driftsorganisasjon for nødnett HF (HDO)
Oslo University Hospital
The Norwegian Heart and Lung Patient Organization
The Norwegian Stroke Register
Western Norway University of Applied Sciences

Registry information

Official study title

Artificial Intelligence (AI) Support in Medical Emergency Calls - "The AISMEC-study" -Can Artificial Intelligence Improve the Precision in Identifying Acute Stroke in Emergency Medical Calls?

Acronym: AISMEC

Important dates

Study start: 2020
Primary completion: 2026
Study completion: 2026
First posted: Dec 1, 2020
Registry last updated: Sep 22, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Active, Not Recruiting