Facility Location under Uncertainty and Spatial Data Analytics in Healthcare

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

Facility Location under Uncertainty and Spatial Data Analytics in Healthcare

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.Ph.D

The effects of zoledronic acid on ECG: a prospective study on patients with bone metastatic cancer

Conclusion. There were no significant alterations in ECG in the acute period, indicated that ZA had no arrhythmia potential in the early period in patients with no underlying cardiac disease. However: patients receiving ZA should be monitored more closely because of the risk of arrhythmia which may ensue due to hypocalcemia, hypomagnesemia, or other chemotherapeutics

Wait!What does that mean?: Eliminating ambiguity of delays in healthcare from an OR/MS perspective

Waiting time in healthcare is a significant problem that occurs across the world and often has catastrophic effects. There are various terms used for waiting time (“sojourn”, “throughput” etc.) and there is no consensus on how these terms are defined. Ambiguous definitions of waiting time make it difficult to compare and measure the problems related to waiting times and delays in healthcare. We present a systematic search and review of the Operations Research and Management Science (ORMS) literature on delays in healthcare services. We search for articles from 2004 to 2019 and base our search strategy on a well-known healthcare planning and control decision taxonomy. An important step towards reducing the ambiguity in the definitions is to distinguish between access time and waiting time. We provide clear definitions and examples of access time and waiting time, and we classify our search results according to three categories: article type, healthcare service investigated and ORMS technique used to solve the delay problem. We find that half of the ORMS research on the waiting and access time problem is done on Ambulatory Care services. We provide tables for each healthcare service that highlight key definitions, the techniques that are used most often and the healthcare environment where the research is done. This research highlights the significant ORMS research that is done on access and waiting time in healthcare as well as the remaining research opportunities. Moreover, it provides a common language for the ORMS community to solve critical waiting time issues in healthcare

Optimizing the Deployment of Public Access Defibrillators

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

Increased carotid-femoral pulse wave velocity and common carotid artery intima-media thickness obtained to assess target organ damage in hypertensive patients are closely related

Background: Increased carotid-femoral pulse wave velocity (CF-PWV) and increased carotid intima-media thickness (IMT) in hypertension (HT) patients are indicators of asymptomatic organ damage. The relationship between carotid IMT and CF-PWV has been shown; studies comparing CF-PWV and IMT values within different vascular regions are limited. We aimed to investigate the relationship between IMT value measured from different anatomical regions and CF-PWV, and the localization of IMT that determines increased CF-PWV best. Methods: This study included 312 patients with HT. CF-PWV measurements with Doppler ultrasonography (USG). Vascular IMTs were measurements of common-internal carotid, brachial, and femoral arteries with B-mode USG (CC-IMT, IC-IMT, B-IMT, and F-IMT, respectively). Patients were divided into two groups according to their CF-PWV value (Increased CF-PWV >10 m/s and normal CF-PWV ≤10m/s). Results: Increased CF-PWV was detected in 54 (17.3%) of HT patients. The patient group with increased CF-PWV was older, and their CC-IMT, IC-IMT and F-IMT values were found to be higher. The other 3 IMT increases excluding B-IMT were closely related to the CF-PWV increase. Only age and CC-IMT values were found to be most closely related to CF-PWV. CC-IMT and age were found to be independently associated with increased CF-PWV. CC-IMT (each-0.1 mm) and age (each year) were found to augment the development of increased CF-PWV by 50.3% and 14.6%, respectively. Conclusion: There is a close relationship between CC-IMT and CF-PWV increase in HT. It was thought that it would still be more useful to look at the increase of CC-IMT compared to other vascular regions for screening asymptomatic organ damage

Overcoming Spatial and Temporal Barriers to Public Access Defibrillators Via Optimization.

BACKGROUND: Immediate access to an automated external defibrillator (AED) increases the chance of survival for out-of-hospital cardiac arrest (OHCA). Current deployment usually considers spatial AED access, assuming AEDs are available 24 h a day. OBJECTIVES: The goal of this study was to develop an optimization model for AED deployment, accounting for spatial and temporal accessibility, to evaluate if OHCA coverage would improve compared with deployment based on spatial accessibility alone. METHODS: This study was a retrospective population-based cohort trial using data from the Toronto Regional RescuNET Epistry cardiac arrest database. We identified all nontraumatic public location OHCAs in Toronto, Ontario, Canada (January 2006 through August 2014) and obtained a list of registered AEDs (March 2015) from Toronto Paramedic Services. Coverage loss due to limited temporal access was quantified by comparing the number of OHCAs that occurred within 100 meters of a registered AED (assumed coverage 24 h per day, 7 days per week) with the number that occurred both within 100 meters of a registered AED and when the AED was available (actual coverage). A spatiotemporal optimization model was then developed that determined AED locations to maximize OHCA actual coverage and overcome the reported coverage loss. The coverage gain between the spatiotemporal model and a spatial-only model was computed by using 10-fold cross-validation. RESULTS: A total of 2,440 nontraumatic public OHCAs and 737 registered AED locations were identified. A total of 451 OHCAs were covered by registered AEDs under assumed coverage 24 h per day, 7 days per week, and 354 OHCAs under actual coverage, representing a coverage loss of 21.5% (p < 0.001). Using the spatiotemporal model to optimize AED deployment, a 25.3% relative increase in actual coverage was achieved compared with the spatial-only approach (p < 0.001). CONCLUSIONS: One in 5 OHCAs occurred near an inaccessible AED at the time of the OHCA. Potential AED use was significantly improved with a spatiotemporal optimization model guiding deployment