Facility Location under Uncertainty and Spatial Data Analytics in Healthcare

Research output: ThesisPhD Thesis - Research external, graduation externalAcademic

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Abstract

Out-of-hospital cardiac arrest (OHCA) 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, have been shown to reduce the time to defibrillation and improve survival rates. The focus of this thesis is on data-driven decision making aimed at improving survival from OHCA by analyzing cardiac arrest risk and optimizing AED deployment. This work establishes a unique marriage of data analytics and facility location optimization to address both the demand (cardiac arrest) and supply (AED) sides of the AED deployment problem. In the demand side, we analyze the spatiotemporal trends of OHCAs in Toronto and show that the OHCA risk is stable at the neighborhood level over time. In other words, high risk areas tend to remain high risk, which supports focusing public health resources for cardiac arrest intervention and prevention in those areas to increase the efficiency of these scarce resources and improve the long-term impact. In the supply side, we develop a comprehensive modeling framework to support data-driven decision making in the deployment of public location AEDs, with the ultimate goal of increasing the likelihood of AED usage in a cardiac arrest emergency. As a part of this framework, we formulate three optimization models that consider probabilistic coverage of cardiac arrests using AEDs and address specific, real-life scenarios about AED retrieval and usage. Our models generalize existing location models and incorporate differences in bystander behavior. The models are mixed integer nonlinear programs, and a contribution of this work lies in the development of mixed integer linear formulation equivalents and tight and easily computable bounds. Next, 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
QualificationDoctor of Philosophy
Awarding Institution
  • University of Toronto
Supervisors/Advisors
  • Chan, Timothy C.Y., Supervisor
  • Kwon, Roy H., Supervisor, External person
Place of PublicationToronto
Publisher
Publication statusPublished - 2016

Fingerprint

Healthcare
Uncertainty
Facility location
Integer
Public health
Decision making
Emergency
Resources
Supply side
Canada
Scenarios
Cost-effectiveness
Marriage
Survival rate
Location model
Kernel density estimation
Model evaluation
Optimization model
Probability distribution
Modeling

Keywords

  • Facility location
  • Location analysis
  • Automated external defibrillator
  • Cardiac arrest
  • Mathematical programming
  • Integer linear programming
  • Coverage models

Cite this

@phdthesis{f551a98040b2449f8151336d23263c43,
title = "Facility Location under Uncertainty and Spatial Data Analytics in Healthcare",
abstract = "Out-of-hospital cardiac arrest (OHCA) 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, have been shown to reduce the time to defibrillation and improve survival rates. The focus of this thesis is on data-driven decision making aimed at improving survival from OHCA by analyzing cardiac arrest risk and optimizing AED deployment. This work establishes a unique marriage of data analytics and facility location optimization to address both the demand (cardiac arrest) and supply (AED) sides of the AED deployment problem. In the demand side, we analyze the spatiotemporal trends of OHCAs in Toronto and show that the OHCA risk is stable at the neighborhood level over time. In other words, high risk areas tend to remain high risk, which supports focusing public health resources for cardiac arrest intervention and prevention in those areas to increase the efficiency of these scarce resources and improve the long-term impact. In the supply side, we develop a comprehensive modeling framework to support data-driven decision making in the deployment of public location AEDs, with the ultimate goal of increasing the likelihood of AED usage in a cardiac arrest emergency. As a part of this framework, we formulate three optimization models that consider probabilistic coverage of cardiac arrests using AEDs and address specific, real-life scenarios about AED retrieval and usage. Our models generalize existing location models and incorporate differences in bystander behavior. The models are mixed integer nonlinear programs, and a contribution of this work lies in the development of mixed integer linear formulation equivalents and tight and easily computable bounds. Next, 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.",
keywords = "Facility location, Location analysis, Automated external defibrillator, Cardiac arrest, Mathematical programming, Integer linear programming, Coverage models",
author = "Derya Demirtas",
year = "2016",
language = "English",
publisher = "University of Toronto Press",
address = "Canada",
school = "University of Toronto",

}

Demirtas, D 2016, 'Facility Location under Uncertainty and Spatial Data Analytics in Healthcare', Doctor of Philosophy, University of Toronto, Toronto.

Facility Location under Uncertainty and Spatial Data Analytics in Healthcare. / Demirtas, Derya .

Toronto : University of Toronto Press, 2016. 115 p.

Research output: ThesisPhD Thesis - Research external, graduation externalAcademic

TY - THES

T1 - Facility Location under Uncertainty and Spatial Data Analytics in Healthcare

AU - Demirtas, Derya

PY - 2016

Y1 - 2016

N2 - Out-of-hospital cardiac arrest (OHCA) 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, have been shown to reduce the time to defibrillation and improve survival rates. The focus of this thesis is on data-driven decision making aimed at improving survival from OHCA by analyzing cardiac arrest risk and optimizing AED deployment. This work establishes a unique marriage of data analytics and facility location optimization to address both the demand (cardiac arrest) and supply (AED) sides of the AED deployment problem. In the demand side, we analyze the spatiotemporal trends of OHCAs in Toronto and show that the OHCA risk is stable at the neighborhood level over time. In other words, high risk areas tend to remain high risk, which supports focusing public health resources for cardiac arrest intervention and prevention in those areas to increase the efficiency of these scarce resources and improve the long-term impact. In the supply side, we develop a comprehensive modeling framework to support data-driven decision making in the deployment of public location AEDs, with the ultimate goal of increasing the likelihood of AED usage in a cardiac arrest emergency. As a part of this framework, we formulate three optimization models that consider probabilistic coverage of cardiac arrests using AEDs and address specific, real-life scenarios about AED retrieval and usage. Our models generalize existing location models and incorporate differences in bystander behavior. The models are mixed integer nonlinear programs, and a contribution of this work lies in the development of mixed integer linear formulation equivalents and tight and easily computable bounds. Next, 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 (OHCA) 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, have been shown to reduce the time to defibrillation and improve survival rates. The focus of this thesis is on data-driven decision making aimed at improving survival from OHCA by analyzing cardiac arrest risk and optimizing AED deployment. This work establishes a unique marriage of data analytics and facility location optimization to address both the demand (cardiac arrest) and supply (AED) sides of the AED deployment problem. In the demand side, we analyze the spatiotemporal trends of OHCAs in Toronto and show that the OHCA risk is stable at the neighborhood level over time. In other words, high risk areas tend to remain high risk, which supports focusing public health resources for cardiac arrest intervention and prevention in those areas to increase the efficiency of these scarce resources and improve the long-term impact. In the supply side, we develop a comprehensive modeling framework to support data-driven decision making in the deployment of public location AEDs, with the ultimate goal of increasing the likelihood of AED usage in a cardiac arrest emergency. As a part of this framework, we formulate three optimization models that consider probabilistic coverage of cardiac arrests using AEDs and address specific, real-life scenarios about AED retrieval and usage. Our models generalize existing location models and incorporate differences in bystander behavior. The models are mixed integer nonlinear programs, and a contribution of this work lies in the development of mixed integer linear formulation equivalents and tight and easily computable bounds. Next, 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 - Location analysis

KW - Automated external defibrillator

KW - Cardiac arrest

KW - Mathematical programming

KW - Integer linear programming

KW - Coverage models

M3 - PhD Thesis - Research external, graduation external

PB - University of Toronto Press

CY - Toronto

ER -