Out-Of-Hospital Cardiac Arrest Clinical Trial
Official title:
Can a Machine Learning Recognise of Out-of-Hospital Cardiac Arrest During Emergency Calls and Assist Medical Dispatchers
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. The aim of the study is to investigate the effect of a computer
generated alert in calls where cardiac arrest is suspected.
The study will investigate
1. whether a potential increase in recognitions is due to machine alerts or the increased
focus of the medical dispatcher on recognizing Out-of-Hospital cardiac Arrest (OHCA)
when implementing the machine
2. if a machine learning model based on neural networks, when alerting medical dispatchers
will increase overall recognition of OHCA and increase dispatch of citizen responders.
3. increased use of automated external defibrillators (AED), cardiopulmonary resuscitation
(CPR) or dispatch of citizen responders in cases of OHCA on machine recognised OHCA vs.
medical dispatcher recognised OHCA.
Chances of survival after out-of-hospital cardiac arrest decrease 10% per minute from
collapse until CPR is initiated. dispatcher assisted telephone CPR will be initiated only in
cases where the dispatcher recognizes the cardiac arrest.
In a previous project "Can a computer through machine learning recognise of Out-of-Hospital
Cardiac Arrest during emergency calls" (supported by TrygFoundation), the investigators
found, it was possible to create a Machine Learning (ML) model, which could recognise OHCA
with higher precision than medical dispatchers at the Emergency Medical Dispatch Center
(EMDC-Copenhagen).
In this study the model andt is effect is to be documented in the EMDC-Copenhagen. For this
purpose, a computer server running the ML-model are created. This server is integrated in the
network at EMDC-Copenhagen, making it possible to push alerts to the medical dispatcher, when
a cardiac arrest is recognised by the model.
With aid of machine learning, the hypothesis is, that recognition of OHCA is improved, and
happen both more frequent and faster than present.
An instruction for the medical dispatchers is developed, which guides the medical dispatcher
in instance of an alert from the machine.
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