Optimizing the Deployment of Public Access Defibrillators

Optimizing the Deployment of Public Access Defibrillators

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Article ID: iaor20165117
Volume: 62
Issue: 12
Start Page Number: 3617
End Page Number: 3635
Publication Date: Dec 2016
Journal: Management Science
Authors: , ,
Keywords: location, simulation, optimization, programming: nonlinear
Abstract:

Out‐of‐hospital cardiac arrest is a significant public health issue, and treatment, namely, cardiopulmonary resuscitation and defibrillation, is very time sensitive. Public access defibrillation programs, which deploy automated external defibrillators (AEDs) for bystander use in an emergency, reduce the time to defibrillation and improve survival rates. In this paper, we develop models to guide the deployment of public AEDs. Our models generalize existing location models and incorporate differences in bystander behavior. We formulate three mixed integer nonlinear models and derive equivalent integer linear reformulations or easily computable bounds. We use kernel density estimation to derive a spatial probability distribution of cardiac arrests that is used for optimization and model evaluation. Using data from Toronto, Canada, we show that optimizing AED deployment outperforms the existing approach by 40% in coverage, and substantial gains can be achieved through relocating existing AEDs. Our results suggest that improvements in survival and cost‐effectiveness are possible with optimization. This paper was accepted by Dimitris Bertsimas, optimization.

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