Improving Resuscitation Equity using AI

For urgent, life-threatening medical conditions such as cardiac arrest and stroke, treatment is time-sensitive and must be received promptly to maximize the likelihood of patient survival. Previous research has shown that areas with lower socioeconomic status have higher rates of cardiac arrest incidence as well as lower rates of receiving timely treatment and patient survival. This suggests that current practices have an inherent inequity of care across socioeconomic levels.
Artificial intelligence methods present new opportunities for emergency medical service (EMS) systems to optimize their response by identifying cardiac arrest patients sooner and strategically deploying EMS resources to lower response delays. However, the effects of these methods with respect to the equity of care across socioeconomic status is unclear.
The proposed project will be conducted with the Scottish Ambulance Service to develop equitable, AI-driven resource allocation policies such as the placement of public defibrillators and recruitment of community-based responders in ways that can optimize both the effectiveness and the equity of care for cardiac arrest patients, and compare the impact that these policies can have to that of existing practices.

Faculty Supervisor:

Timothy Chan

Student:

Partner:

University of Edinburgh

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program: