Optimizing Defibrillator Deployment

doi:10.1017/9781108872188.010

6 - Optimizing Defibrillator Deployment

from Part II - Optimizing Healthcare Systems

Published online by Cambridge University Press: 21 April 2022

Timothy C.Y. Chan and

Edited by

David Scheinker and

Sze-chuan Suen: Affiliation:
University of Southern California
David Scheinker: Affiliation:
Stanford University, California
Eva Enns: Affiliation:
University of Minnesota

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Summary

In this chapter, we explore how data-driven modeling can improve the understanding of OHCA risk, help identify the limitations of current AED placement strategies, and guide the development of optimal AED networks to increase the chance of AED use and OHCA survival. More specifically, we frame AED network design and related response efforts as a facility location problem, focusing on the maximum coverage location and p-median problems. We also highlight how novel tools that combine techniques from areas including information theory and machine learning with optimization models can shape the future of OHCA response efforts and AED placement strategies.

Keywords

Resuscitation cardiac arrest public access defibrillation automated external defibrillator drones optimization

Information

Type: Chapter
Information: Artificial Intelligence for Healthcare
Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
, pp. 110 - 140

DOI: https://doi.org/10.1017/9781108872188.010 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2022

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