Clinical trials: Medical Emergency Calls
Out-Of-Hospital Cardiac Arrest
Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.
Emergency medical Services Copenhagen has developed a machine learning model that analyzes the calls to 1-1-2 (9-1-1) in real time. The model are able to recognize calls where a cardiac arrest is suspected.
- Trial name
- NCT04219306
- Testing
- Alert on dispatchers screen 'Suspect cardiac arrest'
- Where
- Denmark
- Volunteers
- 5,242 people
- Sponsor
- Emergency Medical Services, Capital Region, Denmark
Acute Coronary Syndrome
Medical Telephone Triage of Emergency Calls for Thoracic Pain
Thoracic pain is the main symptom of acute coronary syndrome (ACS), urgent and serious illness. Whereas hospital mortality decreased until reaching 10%, out-of-hospital mortality remains high: half of the deaths occur during the…
- Trial name
- DOREMI2 (NCT02042209)
- Where
- 3 sites in France
- Volunteers
- 4,205 people
- Sponsor
- University Hospital, Toulouse
Subarachnoid Hemorrhage
Presentation of Subarachnoid Haemorrhage
It is the primary aim of this study to identify symptoms and/or specific words (trigger words) indicative of spontaneous subarachnoid haemorrhage (sSAH) during emergency telephone calls to the Emergency Medical Service Copenhagen…
- Trial name
- NCT03980613
- Testing
- Triage
- Where
- Denmark
- Volunteers
- 892 people
- Sponsor
- Copenhagen University Hospital, Rigshospitalet
Emergency Medical Dispatch Centre, Video Streaming
Dispatch of Emergency Call Using Video Streaming Compared With Traditional Telephone Communication
In the Emergency Medical Dispatch Center, all EMS dispatchers were divided into one of two clusters with 11 EMS dispatchers in each cluster.
- Conditions
- Emergency Medical Dispatch Centre, Video Streaming, Emergency Call, Prehospital Emergency Medical Services
- Trial name
- CAM-VISION (NCT05742412)
- Testing
- Video streaming (smartphone camera), Telephone (telephone microphone)
- Where
- Denmark
- Volunteers
- 20,539 people
- Sponsor
- Central Denmark Region
Stroke, Acute, Apoplexy; Brain
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.
- Conditions
- Stroke, Acute, Apoplexy; Brain, Emergencies, Communication, Multidisciplinary
- Trial name
- AISMEC (NCT04648449)
- Testing
- Artificial intelligence on emergency calls
- Where
- Norway
- Volunteers
- 1,000 people
- Sponsor
- Haukeland University Hospital
Competency, Confidence, Self
The DREAM-VR Study: Does a Virtual Reality Tool Help Dental Students Master Acute Medical Emergencies Better?
The goal of this prospective educational cross-over study is to compare the competency and self-reported confidence in managing acute medical emergencies in fourth year dental students at the Academic Centre for Dentistry (ACTA)…
- Conditions
- Competency, Confidence, Self, Medical Emergency Calls, Dentistry
- Trial name
- DREAM-VR (NCT07811349)
- Testing
- DentEmergencyVR tool, Mannikin group
- Where
- Netherlands
- Volunteers
- 140 people, healthy volunteers welcome
- Sponsor
- Denise van Diermen
Medical Emergency Calls
Social Inequalities in Emergency Call and Emergency Response Patterns
Background: Inequality in access to healthcare is a challenge internationally. Despite that medical emergency calls can be considered as access point to pre-hospital emergency care and hospital admission in emergency situations…
- Trial name
- NCT02649036
- Where
- Denmark
- Volunteers
- 80,829 people
- Sponsor
- Emergency Medical Services, Capital Region, Denmark