Optimizing the Deployment of Public Access Defibrillators

Timothy C.Y. Chan, Derya Demirtas, Roy H. Kwon

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)
48 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)3617-3635
JournalManagement science
Volume62
Issue number12
DOIs
Publication statusPublished - 2016

Fingerprint

Defibrillation
Defibrillators
Cardiac
Model Evaluation
Location Model
Kernel Density Estimation
Integer
Cost-effectiveness
Optimization
Public Health
Reformulation
Spatial Distribution
Emergency
Nonlinear Model
Resuscitation
Coverage
Probability Distribution
Public health
Cost effectiveness
Generalise

Keywords

  • Facility location
  • Coverage models
  • Kernel density estimation
  • Automated external defibrillator
  • Cardiac arrest
  • Location analysis
  • Operations research

Cite this

Chan, Timothy C.Y. ; Demirtas, Derya ; Kwon, Roy H. / Optimizing the Deployment of Public Access Defibrillators. In: Management science. 2016 ; Vol. 62, No. 12. pp. 3617-3635.
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Optimizing the Deployment of Public Access Defibrillators. / Chan, Timothy C.Y.; Demirtas, Derya; Kwon, Roy H.

In: Management science, Vol. 62, No. 12, 2016, p. 3617-3635.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Optimizing the Deployment of Public Access Defibrillators

AU - Chan, Timothy C.Y.

AU - Demirtas, Derya

AU - Kwon, Roy H.

PY - 2016

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N2 - 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.

AB - 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.

KW - Facility location

KW - Coverage models

KW - Kernel density estimation

KW - Automated external defibrillator

KW - Cardiac arrest

KW - Location analysis

KW - Operations research

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JF - Management science

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