Stroke, AcuteAISMECNCT04648449
Artificial Intelligence (AI) Support in Medical Emergency Calls
An observational study of Stroke, Acute and Apoplexy; Brain, run by Haukeland University Hospital at 1 site in Norway.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Stroke, Acute, Apoplexy; Brain, Emergencies
- What is being tested?
- Artificial intelligence on emergency calls
- How many people
- 1,000 participants
- Where
- 1 siteNorway
- Study dates
- 1 Sep 2020to 31 Aug 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: All calls to 113 in Bergen will be subject to the AI. A warning will be given the EMCC-operator if risk of stroke, when combining contents in the call and historical data in the hospital record, exceeds a certain limit.
Who may be able to join
- All callers to medical emergency number 113 in Bergen
Who cannot join
- Age <18
Trial locations and contacts
1 site in 1 country.
Norway1 site
Haukeland Universitetssykehus, Kirurgisk serviceklinikk, Nasjonalt kompetansesenter for helsetjenestens kommunikasjonsberedskap
Bergen, Bergen, 5021
About this study
Official 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?
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.
Read the detailed description
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.
- Being tested
- Artificial intelligence on emergency calls
- Keywords
- EMCC, Stroke, Apoplexy, Emergency Medical Service Communication Systems, Dispatch, Artificial intelligence, Machine learning
- Medical subject headings
- Stroke, Emergencies, Communication
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence on emergency callsOther
AI listens to all calls to Bergen EMCC, detecting calls regarding possible stroke.
What is being measured
The primary outcome is the main question the study is designed to answer.
Stroke recognition in medical emergency calls
Survey AI's ability to recognize stroke, compared to the current system
Measured: Sept. 22 - Sept. 23
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Dec 2020
- Study start1 Sep 2020
- Primary completion31 Aug 2026 (estimated)
- Study completion31 Aug 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Stroke, AcuteStudies of the same condition, open and under way.
- More trials run by Haukeland University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Haukeland University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Helse Vest, Western Norway University of Applied Sciences, Oslo University Hospital, The Norwegian Heart and Lung Patient Organization, Helsetjenestens driftsorganisasjon for nødnett HF (HDO), The Norwegian Stroke Register
- Responsible party
- Sponsor
- Investigators
- Guttorm Brattebo, Professor II, Study director, Haukeland University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04648449
- Sponsor's protocol ID
- 108573
- Acronym
- AISMEC
- Status last verified
- Mar 2025
- First submitted
- 23 Nov 2020
- Last updated
- 22 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Not relevant at this stage.
Study at a glance
From the registry record, last updated by the sponsor on 22 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official 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?
- ClinicalTrials.gov ID
- NCT04648449
- Acronym / sponsor ID
- AISMEC / 108573
- Keywords
- EMCC, Stroke, Apoplexy, Emergency Medical Service Communication Systems, Dispatch, Artificial intelligence, Machine learning
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Haukeland University HospitalUniversity, hospital or other
- Collaborators
- Helse Vest, Western Norway University of Applied Sciences, Oslo University Hospital, The Norwegian Heart and Lung Patient Organization, Helsetjenestens driftsorganisasjon for nødnett HF (HDO), The Norwegian Stroke Register
- Study start
- 1 Sep 2020 (actual)
- Primary completion
- 31 Aug 2026 (estimated)
- Study completion
- 31 Aug 2026 (estimated)
- First posted
- 1 Dec 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All calls to 113 in Bergen will be subject to the AI. A warning will be given the EMCC-operator if risk of stroke, when combining contents in the call and historical data in the hospital record, exceeds a certain limit.
Key inclusion criteria (1 listed)
- All callers to medical emergency number 113 in Bergen
Key exclusion criteria (1 listed)
- Age <18
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence on emergency callsAI listens to all calls to Bergen EMCC, detecting calls regarding possible stroke. | Other | – |
Primary outcome
Stroke recognition in medical emergency calls
Time frame: Sept. 22 - Sept. 23
Survey AI's ability to recognize stroke, compared to the current system
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Sep 2025.
- Individual participant dataSharing planned: No. Not relevant at this stage.
Related trials for Stroke, Acute
Recruiting
Connectomic Alterations Following Acute Ischemic Stroke in the MCA Territory
Ischemic Stroke
RecruitingObservational study
Clevidipine for the Antihypertensive Treatment of Acute Intracerebral Hemorrhage
Intracerebral Hemorrhage, Stroke
Active, not recruitingPhase 4
A Study Testing How Well the Drug Extract of Ginkgo Biloba Leaves Tablets Works for People With Memory or Thinking Problems After a Stroke When Added to Standard Treatment
Ischemic Stroke Hospitalized, Impairment, Cognitive
Not yet recruitingPhase 4
Anti-inflammatory and Anti-thrombotic Therapy With colcHicine and Low Dose Rivaroxaban for Major Adverse Cardiovascular Events Reduction in Ischemic Stroke
Ischemic Stroke
Not yet recruitingPhase 3
INTRA-ARTERIAL MeVO TREATMENT (IMP-ACT) Within the ACT-GLOBAL Adaptive Platform Trial-NCT06352632
Stroke, Acute Ischemic, Medium Vessel Occlusion
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Guttorm Brattebo, Professor II study director
Haukeland University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stroke, Acute and Apoplexy; Brain, run by Haukeland University Hospital at 1 site in Norway.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 22 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Norway. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Stroke recognition in medical emergency calls (time frame: Sept. 22 - Sept. 23). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Sep 2025.