Robust Data-Driven Design of a Smart Cardiac Arrest Response System

This paper studies the data-driven design of a smart emergency response system for out-of-hospital cardiac arrest (OHCA) that involves drones for automatic external defibrillator delivery and community responders alerted via a mobile application, in addition to ambulances. Our study is motivated by the widespread exploration of drones for delivery service, and the emergence of mobile applications that crowdsource community for emergency response. Based on a historical OHCA dataset with community responders’ response records from Singapore, we develop a robust joint deployment model of drone and ambulance to maximize the survivability of the response system while accounting for data uncertainty in OHCA occurrence and responder behavior. We discretize the planning area into finite demand regions, and allow different regions to have different OHCA demand rates, alert response probabilities and alert response time distributions from responders. Each of these attributes is only known to reside in an uncertainty/ambiguity set constructed from historical data. Our objective is to maximize the worst-case demand-weighted survival rate in the presence of uncertainty. We reformulate the resulting robust deployment model as a mixed-integer linear program, which can be efficiently solved by a proposed row-and-column generation algorithm with convergence guarantee. We illustrate our model and solution approach using real data from Singapore. We find that (i) hedging against uncertainty leads to a higher survival rate of the response system, compared to a sample average approximation deployment approach; (ii) while adding more drones/ambulances to the system exhibits diminishing return, a few drones are sufficient to increase the survival rate dramatically; and (iii) the impact of the behavior of responders on survival outcomes is more significant than that of simply adding drones/ambulances. We also discuss several managerial insights from the numerical experiments.