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6 - Optimizing Defibrillator Deployment

from Part II - Optimizing Healthcare Systems

Published online by Cambridge University Press:  21 April 2022

By
Timothy C.Y. Chan  and
Christopher L.F. Sun
Edited by
Sze-chuan Suen ,
David Scheinker  and
Eva Enns
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

Resuscitationcardiac arrestpublic access defibrillationautomated external defibrillatordronesoptimization
Type
Chapter
Information
Artificial Intelligence for Healthcare
Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
 , pp. 110 - 140
Publisher: Cambridge University Press
Print publication year: 2022

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References

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