Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.

Out-Of-Hospital Cardiac Arrest

Machine learning, Artificial intelligence, Dispatcher assisted telephone CPR, Heart Arrest, Heart Diseases, Cardiovascular Diseases

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.

Denmark