  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.
