iRescU - Data for Social Good Saving Lives Bridging the Gaps in Sudden Cardiac Arrest Survival
📝 Abstract
Currently every day in the USA 1000 people die of sudden cardiac arrest (SCA) outside of hospitals or ambulances - before emergency medical help arrives - in the streets, workplaces, schools and homes of our cities, adults and children. Brain death commences in 3 minutes, and often the ambulance just can’t be there in time. Citizen cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use can save precious minutes and lives. Using public access AED’s saves lives in SCA- however AEDs are used in <2% of cardiac arrests, though could save lives in 80% if available, findable, functioning, and used. The systems problem to solve is that there is no comprehensive or real time accessible database of the AED locations, and also it is not known that they are actually being positioned where they are needed. The iRescU project is designed to bridge this gap in SCA survival, by substantially augmenting the AED database. Utilizing a combination of AED crowd sourcing and geolocation integrated with existing 911 services and SCA events and projected events based on machine learning data information to help make the nearest AED accessible and available in the setting of a SCA emergency and to identify the areas of greatest need for AEDs to be positioned in the community. Helping to save lives and address preventable death with a social good approach and applied big data.
💡 Analysis
Currently every day in the USA 1000 people die of sudden cardiac arrest (SCA) outside of hospitals or ambulances - before emergency medical help arrives - in the streets, workplaces, schools and homes of our cities, adults and children. Brain death commences in 3 minutes, and often the ambulance just can’t be there in time. Citizen cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use can save precious minutes and lives. Using public access AED’s saves lives in SCA- however AEDs are used in <2% of cardiac arrests, though could save lives in 80% if available, findable, functioning, and used. The systems problem to solve is that there is no comprehensive or real time accessible database of the AED locations, and also it is not known that they are actually being positioned where they are needed. The iRescU project is designed to bridge this gap in SCA survival, by substantially augmenting the AED database. Utilizing a combination of AED crowd sourcing and geolocation integrated with existing 911 services and SCA events and projected events based on machine learning data information to help make the nearest AED accessible and available in the setting of a SCA emergency and to identify the areas of greatest need for AEDs to be positioned in the community. Helping to save lives and address preventable death with a social good approach and applied big data.
📄 Content
1
iRescU – Data for Social Good Saving Lives Bridging the Gaps in
Sudden Cardiac Arrest Survival-
Harnessing Crowd Sourcing, Geolocation and Big Data for Social Good
Nadine Levick
EMS Safety Foundation
New York City, NY, USA
nlevick@attglobal.net
ABSTRACT
Currently every day in the USA 1000 people die of
sudden cardiac arrest (SCA) outside of hospitals or
ambulances - before emergency medical help arrives -
in the streets, workplaces, schools and homes of our
cities, adults and children. Brain death commences in 3
minutes, and often the ambulance just can’t be there in
time. Citizen cardiopulmonary resuscitation (CPR) and
automated external defibrillator (AED) use can save
precious minutes and lives. Using public access AED’s
saves lives in SCA- however AEDs are used in <2% of
cardiac arrests, though could save lives in 80% if
available, findable, functioning, and used. The systems
problem to solve is that there is no comprehensive or
real time accessible database of the AED locations, and
also it is not known that they are actually being
positioned where they are needed. The iRescU project
is designed to bridge this gap in SCA survival, by
substantially augmenting the AED database. Utilizing
a combination of AED crowd sourcing and geolocation
integrated with existing 911 services and SCA events
and projected events based on machine learning data
information to help make the nearest AED accessible
& available in the setting of a SCA emergency and to
identify the areas of greatest need for AEDs to be
positioned in the community. Helping to save lives and
address preventable death with a social good approach
and applied big data.
1.INTRODUCTION:
BACKGROUND TO THE PROJECT PROBLEM –
SUDDEN CARDIAC ARREST AND AED GEOLOCATION
Social good applications are often highly interdisciplinary in
nature and usually require close collaboration between diverse
disciplines, technical IT practitioners, subject matter experts, and
social sector experts. The optimization of sudden cardiac arrest
management and its systems engineering embodies this concept.
First of all, what is a SCA? It is when the heart suddenly stops
pumping effectively and/or begins fibrillating or quivering and
there is no effective circulation of blood and the victim rapidly
loses consciousness, collapses and becomes unresponsive. Brain
death commences within 3 minutes unless CPR is commenced
and an AED used. However, the sad fact remains that despite
major advances in technology, data management, machine
learning, acute health care and emergency medical services –
sudden cardiac arrest survival has hardly improved in the past 30
years across the nation. In the field of public safety, public health,
community outreach and social good addressing this existing
problem of survival of sudden cardiac arrest is truly a systems
engineering challenge.
Of the 1000 people who die each day in the setting of an out of
hospital cardiac arrest in the USA, sadly, there is a less than 2%
utilization of AEDs. This is even though there is a predicted
potential 80% survival rate with use of an AED in the first few
minutes after a SCA, as ~80% of SCAs may have a reversible
shockable rhythm with use of an AED. However, currently in the
USA, for the vast majority of cardiac arrests the nearest location
of a public access AED is not known. While there are estimated to
be ~1 million AEDs in the USA, fewer than 10% devices exist in
any database. Current average survival rates in the USA after
SCA are one tenth of that 80% figure – around 8%. In the New
York metro region where this pilot is being developed, cardiac
arrest survival rates are amongst the lowest in the country – less
than 5%. Out of hospital SCA is a major problem both nationally
and in the target region for this preliminary project, with annual
deaths that are over ten times those of the annual road toll.
Whilst vast resources are expended on numerous complex public
safety initiatives and infrastructure such as road safety – the data
management dimensions of bridging the gaps in the system of
SCA survival are in need of optimization and a paradigm shift.
There are gaps in process and key information regarding:
performing CPR, capture and validation of AED location, the
AED functionality and accessibility and utilization of AEDs. A
comprehensive, reliable, dynamic and easily accessible national
AED geolocation database has yet to be created. The goal of our
project is to pilot a sustainable and inexpensive geolocated AED
database augmented by using new communication technologies,
such as social media, QR codes, the cloud and machine learning.
Integrating the complex data of where AEDs are currently located,
their functionality and accessibility, real time access to this data in
a SCA emergency, both by the 911 system and the bystander
public, and information on SCA event geolocation for the
deployment of add
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