Mass-casualty incidents (MCI) cause a sudden increase in demand of medical resources in a region. The most important and challenging task in addressing an MCI is managing overwhelmed resources with the goal of increasing total number of survivors. Currently, most of the decisions following an MCI are made in an ad-hoc manner or by following static guidelines that do not account for amount of available resources and number of the casualties. The purpose of this dissertation is to introduce and analyze sophisticated service prioritization and resource allocation tools. These tools can be used to produce service order strategies that increase the overall number of survivors. There are several models proposed that account for number and mix of the casualties, and amount and type of the resources available. Large number of the elements involved in this problem makes the model very complex, and thus, in order to gain some insights into the structure of the optimal solutions, some of the proposed models are developed under simplifying assumptions. These assumptions include limitations on the number of casualty types, handling of deaths, servers, and types of resources. Under these assumptions several characteristics of the optimal policies are identified, and optimal algorithms for various scenarios are developed. We also develop an integrated model that addresses service order, transportation, and hospital selection. A comprehensive set of computational results and comparison with the related works in the literature are provided in order to demonstrate the efficacy of the proposed methodologies. / Ph. D.

Disaster preparedness is absolutely necessary as the number of both man-made and natural disasters increases worldwide. Not confined to any regions or people, disasters can result in mass casualties. The United States is not spared from these incidents. Ever since the 9/11 terrorist attacks, the United States has tried to establish more effective and efficient emergency management systems at all levels in order to respond to any type of disaster. Due to the effort, much improvement in disaster preparedness was observed when mass casualty events happened within the last 10 years. Although there are many independent studies for each mass casualty event, there are very few studies done to compare multiple mass casualty incidents and find commonly shared lessons. This paper aims to determine whether there are any similarities among three mass casualty events - 2005 Hurricane Katrina, the 2009 Aurora Theatre Shooting, and the 2013 Boston Marathon Bombing. Because the response to the 2013 Boston Marathon Bombing was the most seamless among the three, the most in-depth investigation was done on this incident. Through the examination, the study will also prove if any of the lessons learned from these events can be implemented in future mass casualty incidents. To do so, many current reports and literature reviews were analyzed. The conclusion gained from this study is that there are indeed commonly occurring challenges in disasters and various aspects of disaster preparedness that require practice and preparation. In addition, learning from others' unfortunate mass casualty incidents and their lessons is an important part of strengthening the existing disaster preparedness systems.

The purpose of this study was to determine hospital preparedness for an internal mass casualty/active shooter event at Tennessee hospitals. Data were collected during May of 2019 by surveying the CEOs of the 86 acute care hospitals in Tennessee. The survey solicited responses about training, preparedness, and internal evaluation of procedures. CEOs of 28 (32.5%) of Tennessee’s acute care hospitals responded to the survey. Just over half (53.6%) of those responding indicated that they believed their facility was prepared or well prepared for an active shooter event. The mean responses of CEOs who had experienced an active shooter event were significantly lower than those CEOs who had not. Seventy-two percent of CEOs indicated that policies and procedures for active shooter/mass casualty events were updated at least every other year.

There is a paucity of literature evaluating mass casualty systems and no clear 'gold standard' for measuring the efficacy of information decision support systems or triage systems that can be used in mass casualty events. The purpose of this research was the preliminary validation of a comprehensive conceptual model for a mass casualty continuum of care. This research examined key relationships among entities/factors needed to provide real-time visibility of data that track patients, personnel, resources and potential hazards that influence outcomes of care during mass casualty events.A modified Delphi technique was used to validate the proposed model using a panel of experts. The four research questions measured the extent to which experts agreed that the: 1) ten constructs represent appropriate predictors of outcomes of care during mass casualty events; 2) proposed relationships among the constructs provide valid representations of mass casualty triage; 3) proposed indicators for each construct represent appropriate measurements for the constructs; and 4) the proposed model is seen as useful to the further study of information and technology requirements during mass casualty events. The usefulness of the online Delphi process was also evaluated.A purposeful sample of 18 experts who work in the field of emergency preparedness/response was selected from across the United States. Computer, Internet and email applications were used to facilitate a modified Delphi technique through which experts provided initial validation for the proposed conceptual model. Two rounds of the Delphi process were needed to satisfy the criteria for consensus and/or stability related to the constructs, relationships and indicators in the model. Experts viewed the proposed model as relatively useful (Mean = 5.3 on a 7-point scale). Experts rated the online Delphi process favorably.Constructs, relationships and indicators presented in this model are viewed as preliminary. Future research is needed to develop the tools to measure the constructs and then test the model as a framework for studying effects and outcomes of mass casualty events. This study provides a foundation for understanding the complex context in which mass casualty events take place and the factors that influence outcomes of care.

Traumatic haemorrhage is a leading preventable cause of critical mortality in mass casualty events (MCEs). Treatment requires the rapid provision of high volumes of packed red blood cells (PRBC) to meet the surge in casualty demand these events generate. The increasing frequency of MCEs coupled with the threat of more violent mechanisms risks overwhelming hospital based transfusion systems. The overall objective of this research was to improve understanding of blood use in MCEs using a mathematical modelling approach. A computerised discrete event simulation model was designed, developed and validated using civilian and military transfusion databases, a review of historical MCEs and discussion with experts involved in all aspects of in-hospital MCE PRBC provision. The model was experimented with across increasing casualty loads to optimise event outcomes under varied conditions of: stock availability, laboratory processing procedures and individual PRBC supply. The model indicated even in events of limited size the standard on-shelf PRBC stock level was insufficient to adequately meet demand amongst bleeding casualties. Restocking during an event allowed for equivocal treatment results if performed early following an event and this would be most effective if activated by central suppliers. Modifications to transfusion laboratory processing procedures were found to be of limited benefit in improving outcomes due to the principally automated nature of the techniques they employ. Conversely, the use of restricting excessive individual provision of both overall PRBC and emergency type O PRBC to individual casualties did show potential for managing scenarios where only a finite supply of stock existed or an accurate estimation of expected casualties was available.

Dissertation (Ph.D.)--University of South Florida, 2009. / Title from PDF of title page. Document formatted into pages; contains 295 pages. Includes vita. Includes bibliographical references.

The need for increasing efficiencies for medical resource delivery during a mass casualty incident/event is a paramount logistical planning factor that could mean life or death to the citizens affected by a disaster. As such, Washington State has prioritized emergency management and preparedness. Using the just-in-time system by way of Baghbanian' s complex adaptive decision-making theory as the foundation, gave purpose to this qualitative study. This was accomplished by analysis of emergency management professional responses, and to what degree, improvements can be made to the medical resource delivery system during a mass casualty incident/event. Data were collected through semi structured interviews with a random sample of 12 experienced emergency professionals from the State of Washington. This study was guided by primary research questions that focused on emergency managers and their understanding and adaptability toward preparedness. Interview data were deductively coded and analyzed through a thematic analysis procedure. The key theme of this study is that participants perceived slight differences in logistical and operational approaches that vector into transportation and operational understanding as the main factors influencing medical resource delivery. The positive social change association of this study is that it provides emergency managers, first responders, and medical staff with recommendations for analysis and planning development for medical resource delivery, thereby mitigating the life and death implications for citizens in future disasters.

In this thesis we introduces the user interface design and forensic analysis tool for DIORAMA system. With an Android device, DIORAMA provides emergency personnel the ability to collect information in real time, track the resources and manage them. It allows the responders and commanders to mange multiple incidents simultaneously. This thesis also describes the implementations of commander app and responder app, as well as two different communication strategies used in DIORAMA. Several trials and simulated mass casualty incidents were conducted to test the functionalities and performance of DIORAMA system. All responders that participated in all trials were very satisfied with it. As a result, DIORAMA system significantly reduced the evacuation time by up to 43% when compared to paper based triage systems.

Disaster events have emphasized the importance of healthcare response activities due to the large number of victims. For instance, Hurricane Katrina in New Orleans, in 2005, and the terrorist attacks in New York City and Washington, D.C., on September 11, 2001, left thousands of wounded people. In those disasters, although hospitals had disaster plans established for more than a decade, their plans were not efficient enough to handle the chaos produced by the hurricane and terrorist attacks. Thus, the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) suggested collaborative planning among hospitals that provide services to a contiguous geographic area during mass casualty disasters. However, the JCAHO does not specify a methodology to determine which hospitals should be included into these cooperative plans. As a result, the problem of selecting the right hospitals to include in exercises and drills at the county level is a common topic in the current preparedness stages. This study proposes an efficiency index to determine the efficient response of cooperative-networks among hospitals before an occurrence of mass casualty disaster. The index built in this research combines operations research techniques, and the prediction of this index used statistical analysis. The consecutive application of three different techniques: network optimization, data envelopment analysis (DEA), and regression analysis allowed to obtain a regression equation to predict efficiency in predefined hospital networks for mass casualty disasters. In order to apply the proposed methodology for creating an efficiency index, we selected the Orlando area, and we defined three disaster sizes. Then, we designed networks considering two perspectives, hub-hospital and hub-disaster networks. In both optimization network models the objective function pursued to: reduce the iii travel distance and the emergency department (ED) waiting time in hospitals, increase the number of services offered by hospitals in the network, and offer specialized assistance to children. The hospital network optimization generated information for 75 hospital networks in Orlando. The DEA analyzed these 75 hospital networks, or decision making units (DMU's), to estimate their comparative efficiency. Two DEAs were performed in this study. As an output variable for each DMU, the DEA-1 considered the number of survivors allocated in less than a 40 miles range. As the input variables, the DEA-1 included: (i) The number of beds available in the network; (ii) The number of hospitals available in the network; and (iii) The number of services offered by hospitals in the network. This DEA-1 allowed the assignment of an efficiency value to each of the 75 hospital networks. As output variables for each DMU, the DEA-2 considered the number of survivors allocated in less than a 40 miles range and an index for ED waiting time in the network. The input variables included in DEA-2 are (i) The number of beds available in the network; (ii) The number of hospitals available in the network; and (iii) The number of services offered by hospitals in the network. These DEA allowed the assignment of an efficiency value to each of the 75 hospital networks. This efficiency index should allow emergency planners and hospital managers to assess which hospitals should be associated in a cooperative network in order to transfer survivors. Furthermore, JCAHO could use this index to evaluate the cooperating emergency hospitals’ plans. However, DEA is a complex methodology that requires significant data gathering and handling. Thus, we studied whether a simpler regression analysis would substantially yield the same results. DEA-1 can be predicted using two regression analyses, which concluded that the average distances between hospitals and the disaster locations, and the size of the disaster iv explain the efficiency of the hospital network. DEA-2 can be predicted using three regressions, which included size of the disaster, number of hospitals, average distance, and average ED waiting time, as predictors of hospital network efficiency. The models generated for DEA-1 and DEA-2 had a mean absolute percent error (MAPE) around 10%. Thus, the indexes developed through the regression analysis make easier the estimation of the efficiency in predefined hospital networks, generating suitable predictors of the efficiency as determined by the DEA analysis. In conclusion, network optimization, DEA, and regressions analyses can be combined to create an index of efficiency to measure the performance of predefined-hospital networks in a mass casualty disaster, validating the hypothesis of this research. Although the methodology can be applied to any county or city, the regressions proposed for predicting the efficiency of hospital network estimated by DEA can be applied only if the city studied has the same characteristics of the Orlando area. These conditions include the following: (i) networks must have a rate of services lager than 0.76; (ii) the number of survivors must be less than 47% of the bed capacity EDs of the area studied; (iii) all hospitals in the network must have ED and they must be located in less than 48 miles range from the disaster sites, and (iv) EDs should not have more than 60 minutes of waiting time. The proposed methodology, in special the efficiency index, support the operational objectives of the 2012 ESF#8 for Florida State to handle risk and response capabilities conducting and participating in training and exercises to test and improve plans and procedures in the health response.

Emergency managers are often faced with the toughest decisions that can ever be made, people's lives hang in the balance. Nevertheless, these tough decisions have to be made, and made quickly. There is usually too much information to process to make the best decisions. Decision support systems can relieve a significant amount of this onus, making decision while considering the complex interweaving of constraints and resources that define the boundary of the problem. We study these complex emergency management, approaching the problem with discrete optimization. Using our operational research knowledge to model mass casualty incidents, we seek to provide solutions and insights for the emergency managers. This dissertation proposes a novel deterministic model to optimize the casualty transportation and treatment decisions in response to a MCI. This deterministic model expands on current state of the art by; (1) including multiple dynamic resources that impact the various interconnected decisions, (2) further refining a survival function to measure expected survivors, (3) defining novel objective functions that consider competing priorities, including maximizing survivors and balancing equity, and finally (4) developing a MCI response simulation that provides insights to how optimization models could be used as decision-support mechanisms. / Doctor of Philosophy / Emergency managers are often faced with the toughest decisions that can ever be made, people's lives hang in the balance. Nevertheless, these tough decisions have to be made, and made quickly. But to make the best decisions, there is usually too much information to process. Computers and support tools can relieve a significant amount of this onus, making decision while considering the complex interweaving of constraints and resources that define the boundary of the problem. This dissertation provides a mathematical model that relates the important decisions made during a MCI response with the limited resources of the surrounding area. This mathematical model can be used to determine the best response decisions, such as where to send casualties and when to treat them. This model is also used to explore ideas of fairness and equity in casualty outcomes and examine what may lead in unfair response decisions. Finally, this dissertation uses a simulation to understand how this model could be used to not only plan the response, but also update the plan as you learn new information during the response roll-out.