Prioritizing Patients for Emergency Evacuation From a Healthcare Facility

The success of a healthcare facility evacuation depends on communication and decision-making at all levels of the organization, from the coordinators at incident command to the clinical staff who actually carry out the evacuation. One key decision is the order in which each patient is chosen for evacuation. While the typical planning assumption is that all patients are to be evacuated, there may not always be adequate time or resources available to move all patients. In these cases, prioritizing or ordering patients for evacuation becomes an extremely difficult decision to make. These decisions should be based on the current state of the facility, but without knowledge of the current patient roster or available resources, these decisions may not be as beneficial as possible. Healthcare facilities usually consider evacuation a last-resort measure, and there are often system redundancies in place to protect against having to completely evacuate all patients from a facility. Perhaps this is why there is not a great deal of research dedicated to improving patient transfers. In addition, the question of patient prioritization is a highly ethical one. Based on a literature review of 1) suggested patient prioritization strategies for evacuation planning as well as 2) the actual priorities given in actual facility evacuations indicates there is a lack of consensus as to whether critical or non-critical care patients should be moved away from a facility first in the event of a complete emergency evacuation. In addition, these policies are \u27all-or-nothing\u27 policies, implying that once a patient group is given priority, this entire group will be completely evacuated before any patients from the other group are transferred. That is, if critical care patients are given priority, all critical care patients will be transferred away from the facility before any non-critical care patient. The goal of this research is to develop a decision framework for prioritizing patient evacuations, where unique classifications of patient health, rates of evacuation, and survivability all impact the choice. First, I provide several scenarios (both in terms of physical processing estimates as well as competing, ethically-motivated objectives) and offer insights and observations into the creation of a prioritization policy via dynamic programming. Dynamic programming is a problem-solving technique to recursively optimize a series of decisions. The results of the dynamic programming provide optimal prioritization policies, and these are tested with simulation analysis to observe system performance under many of the same scenarios. Because the dynamic programming decisions are based on the state of the system, simulation also allows the testing of time-based decisions. The results from the dynamic programming and simulation, as well as the structural properties of the simulation are used to create assumptions about how evacuations could be improved. The question is not whether patient priorities should be assigned - but how patient priorities should be assigned. Associated with assigning value to patients are a variety of ethical dilemmas. In this research, I attempt to address patient prioritization from an ethical perspective by discussing the basic principles and the potential dilemmas associated with such decisions. The results indicate that an all-or-nothing, or a \u27greedy\u27 policy as discussed in the literature may not always be optimal for patient evacuations. In some cases, a switching policy may occur. Switching policies begin by evacuating patients from one classification and then switch to begin evacuations from the second patient class. A switch can only be made once; after a switch is made, all remaining patients from the new group should be evacuated. When there are no more patients of that group remaining in the system, the remaining patients from the class that was initially given priority should be evacuated. In the case of critical and non-critical care patients, switching policies first give priority to non-critical care patients. When the costs of holding patients in the system are not included in the models - and the decisions are just based on maximizing the number of saved lives - the switching policies may perform as good or better than the greedy policies suggested in the literature. In addition, when holding costs are not included, it is easier to predict whether the optimal policy is a greedy policy or a switching policy. Prioritization policies can change based on the utility achieved from evacuating individual patients from each class, as well as for other competing objective functions. This research examines a variety of scenarios - maximizing saved lives, minimizing costs, etc. - and provides insights on how the selection of an objective impacts the choice. Another insight of this research is how multiple evacuation teams should be allocated to patients. In the event that there is more than one evacuation team dedicated to moving a group of patients, the two teams should be allocated to the same patient group instead of being split between the multiple patient groups

Disaster management from a POM perspective : mapping a new domain

We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/ logistics journals. In reviewing these papers our objective is to assess and present the macro level “architectural blue print” of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queuing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights in disaster research. Recommendations for future research are provided

A DECISION PROCESS FOR SURFACE MEDICAL EVACUATION ROUTING UNDER ADVERSARY THREAT AND UNCERTAIN DEMAND USING ONLINE OPTIMIZATION

Emerging threats have focused U.S. Navy operating concepts on agile, distributed tactical forces in the littoral and maritime zones. Given the nature of the threat and its location, medical evacuation via air may be infeasible due to hostile conditions or distance, requiring a shift to a surface or subsurface strategy. Medical demand and adversary actions are unpredictable in warfare, therefore a decision process for routing that accounts for uncertainty is required. Using the principles of the U.S. Marine Corps Rapid Response Planning Process and online optimization, we propose a decision process for surface medical evacuation routing against an adversary given uncertain demand that can be applied to manned and autonomous transport operations. We showcase the computational tractability of the decision process by developing an algorithm to route a medical transport through a network then implement the algorithm as a simulation model in Python. The base case of the model is compared to two modified cases under perfect information to discuss the risks of modeling with inappropriate assumptions. Multiple runs of the simulation model are then used to propose a process to develop a distance multiplier to estimate the impact of adversary presence in existing simulation models without a complete re-design.Outstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited

Online Optimisation of Casualty Processing in Major Incident Response

Recent emergency response operations to Mass Casualty Incidents (MCIs) have been criticised for a lack of coordination, implying that there is clear potential for response operations to be improved and for corresponding benefits in terms of the health and well-being of those affected by such incidents. In this thesis, the use of mathematical modelling, and in particular optimisation, is considered as a means with which to help improve the coordination of MCI response. Upon reviewing the nature of decision making in MCIs and other disaster response operations in practice, this work demonstrates through an in-depth review of the available academic literature that an important problem has yet to be modelled and solved using an optimisation methodology. This thesis involves the development of such a model, identifying an appropriate task scheduling formulation of the decision problem and a number of objective functions corresponding to the goals of the MCI response decision makers. Efficient solution methodologies are developed to allow for solutions to the model, and therefore to the MCI response operation, to be found in a timely manner. Following on from the development of the optimisation model, the dynamic and uncertain nature of the MCI response environment is considered in detail. Highlighting the lack of relevant research considering this important aspect of the problem, the optimisation model is extended to allow for its use in real-time. In order to allow for the utility of the model to be thoroughly examined, a complementary simulation is developed and an interface allowing for its communication with the optimisation model specified. Extensive computational experiments are reported, demonstrating both the danger of developing and applying optimisation models under a set of unrealistic assumptions, and the potential for the model developed in this work to deliver improvements in MCI response operations

Disaster management in industrial areas: perspectives, challenges and future research

Purpose: In most countries, development, growth, and sustenance of industrial facilities are given utmost importance due to the influence in the socio-economic development of the country. Therefore, special economic zones, or industrial areas or industrial cities are developed in order to provide the required services for the sustained operation of such facilities. Such facilities not only provide a prolonged economic support to the country but it also helps in the societal aspects as well by providing livelihood to thousands of people. Therefore, any disaster in any of the facilities in the industrial area will have a significant impact on the population, facilities, the economy, and threatens the sustainability of the operations. This paper provides review of such literature that focus on theory and practice of disaster management in industrial cities. Design/methodology/approach: In the paper, content analysis method is used in order to elicit the insights of the literature available. The methodology uses search methods, literature segregation and developing the current knowledge on different phases of industrial disaster management. Findings: It is found that the research is done in all phases of disaster management, namely, preventive phase, reactive phase and corrective phase. The research in each of these areas are focused on four main aspects, which are facilities, resources, support systems and modeling. Nevertheless, the research in the industrial cities is insignificant. Moreover, the modeling part does not explicitly consider the nature of industrial cities, where many of the chemical and chemical processing can be highly flammable thus creating a very large disaster impact. Some research is focused at an individual plant and scaled up to the industrial cities. The modeling part is weak in terms of comprehensively analyzing and assisting disaster management in the industrial cities. Originality/value: The comprehensive review using content analysis on disaster management is presented here. The review helps the researchers to understand the gap in the literature in order to extend further research for disaster management in large scale industrial cities.Peer Reviewe

PRIORITIZATION AND DISTRIBUTION OF CASUALTIES IN DISASTER RESPONSE MANAGEMENT

This dissertation focused on two different problems that typically arise in the aftermath of disasters. In the first part of the dissertation, we study the problem of how casualties should be prioritized and distributed to different medical facilities in the aftermath of mass casualty incidents (MCIs) with the objective of maximizing the expected total number of survivors. Assuming that casualties have been triaged into two classes differentiated by their severity levels and medical needs, the decision-maker needs to prioritize and distribute casualties using a limited number of ambulances to multiple medical facilities with different capacities. By explicitly taking into consideration the capacity and service time at each medical facility, we formulate this sequential decision-making problem as a Markov decision process (MDP). Based on this MDP formulation, we propose heuristic policies that prescribe decisions on prioritization and distribution of casualties. We then employ discrete-event simulations to demonstrate the benefits of using the proposed heuristics against some benchmark policies under several realistic mass casualty incident scenarios such as terrorist attacks, major traffic accidents, and earthquakes. In the second part of the dissertation, we study the resource allocation problem in urban search and rescue operations that follow natural disasters. Specifically, we consider a scenario in which some individuals are trapped at various locations within a geographical area and there is a limited time window during which these individuals can be rescued. We model the problem as an MDP. Then, we characterize the optimal policy under the assumption that individuals belong to only one of two locations. We propose heuristics for the general version of the problem. Finally, the proposed heuristics are examined with a simulation.Doctor of Philosoph

Disaster management in industrial areas: Perspectives, challenges and future research

Purpose: In most countries, development, growth, and sustenance of industrial facilities are given utmost importance due to the influence in the socio-economic development of the country. Therefore, special economic zones, or industrial areas or industrial cities are developed in order to provide the required services for the sustained operation of such facilities. Such facilities not only provide a prolonged economic support to the country but it also helps in the societal aspects as well by providing livelihood to thousands of people. Therefore, any disaster in any of the facilities in the industrial area will have a significant impact on the population, facilities, the economy, and threatens the sustainability of the operations. This paper provides review of such literature that focus on theory and practice of disaster management in industrial cities. Design/methodology/approach: In the paper, content analysis method is used in order to elicit the insights of the literature available. The methodology uses search methods, literature segregation and developing the current knowledge on different phases of industrial disaster management. Findings: It is found that the research is done in all phases of disaster management, namely, preventive phase, reactive phase and corrective phase. The research in each of these areas are focused on four main aspects, which are facilities, resources, support systems and modeling. Nevertheless, the research in the industrial cities is insignificant. Moreover, the modeling part does not explicitly consider the nature of industrial cities, where many of the chemical and chemical processing can be highly flammable thus creating a very large disaster impact. Some research is focused at an individual plant and scaled up to the industrial cities. The modeling part is weak in terms of comprehensively analyzing and assisting disaster management in the industrial cities. Originality/value: The comprehensive review using content analysis on disaster management is presented here. The review helps the researchers to understand the gap in the literature in order to extend further research for disaster management in large scale industrial cities.Scopu

Challenges of torrential flood risk management in Serbia

Torrential floods are the natural hydrological hazards manifesting as a consequence of extreme rainfall episodes which have a quick response from the watersheds of small areas, steep slopes and intensive soil erosion. Taking in consideration the nature of torrential flood (sudden and destructive occurrence) and the fact they are the most frequent natural hazards in Serbia, torrential flood risk management is a real challenge. Instead of partial solutions for flood protection, integrated torrential flood risk management is more meaningful and effective. The key steps should be an improvement of the legal framework on national level and an expansion of technical and biological torrent control works in river basins. Consequences for society can be significantly reduced if there is an efficient forecast and timely warning, rescue and evacuation and if affected population is educated about flood risks and measures which can be undertaken in case of emergency situation. In this paper, all aspects of torrential flood risk management are analyzed

ESSAYS ON LONG-TERM CARE AND INSURANCE MANAGEMENT

This thesis is composed of two parts. The first part encompasses two essays on long-term care (LTC) in Europe and Switzerland. LTC can be defined as help provided to elderly in needs with activities of daily living (ADL) that can be delivered as informal care by persons from the social network or as formal care by professional caregivers. The first paper focuses on individuals’ characteristics that influence the probability to report limitations in ADLs and the probability to report the receipt of formal care in Europe. We find a significant effect of social, demographic and medical factors on both quantities of interest. Our results highlight the lower effect of cancer when compared to other diseases. Further, we observe a decrease of formal care usage when the partner is present in the household. This effect is stronger for domestic tasks and in case of male respondents. In addition, elderly in countries with family care LTC schemes report less formal care than those in countries with other schemes. The second essay extends the measure of dependence by considering instrumental ADLs and functional limitations. Thereby the focus is on identifying the relationship between formal and informal care, that is, whether this link is complementary or substitutional. By using a Structural Equation Modeling approach and data from a Swiss survey, the link is found to be both of complementary and substitutional nature. The second part of the thesis is dedicated to insurance management. We study the impact of natural and man-made catastrophes on the valuation of insurance companies. Furthermore, we analyze the relationship between the effect on the valuation and companies’ characteristics, namely the market capitalization, the subsector, the revenues and the geographical origin. We do not find any clear pattern of the stock price behavior for any type of the considered catastrophes. Despite the fact, that we find no significance of the geographical origin coefficient in the regression, we observe that North American companies are more influenced by local events, such as hurricanes on US territory and experience almost no effect from external ones. In contrast, European companies respond to all events, including international ones. Further, reinsurance companies are found to be more sensitive than property and casualty (P&C) companies. Finally, in the last essay, we study the properties of stochastic programming for the asset allocation in a framework of Swiss pension funds under solvency constraints. We empirically study the convergence of the initial asset allocation with respect to the quality of the approximation of the stochastic returns. We observe results for the probability of deficit as well as for the expected value of the deficit given shortage. Further, we test the sensitivity of the above-mentioned characteristics to changes in internal model parameters. Finally, the effect of misestimating the stocks’ volatility and the bonds’ expected return on the initial asset allocation is examined. -- Cette thèse est composée de deux parties. La première partie comprend deux essais sur les soins de longue durée en Europe et en Suisse. Les soins de longue durée peuvent être définis comme l’aide fournie aux personnes âgées ayant des difficultés avec les activités de la vie quotidienne (AVQ) et peuvent être dispensés sous forme de soins informels par des personnes de leur entourage ou sous forme de soins formels dispensés par des aidants professionnels. Le premier essai porte sur les caractéristiques des individus qui influencent la probabilité de rapporter des limitations dans les AVQ et sur la probabilité de recourir à des soins formels en Europe. Nous constatons un effet significatif des facteurs sociaux, démographiques et médicaux sur les deux quantités d’intérêt. Nos résultats mettent en évidence un effet moindre du cancer par rapport à d’autres maladies. De plus, nous observons une diminution de l’utilisation des soins formels lorsque le partenaire est présent dans le ménage. Cet effet est plus fort pour les tâches domestiques et dans le cas des sujets masculins. En outre, les personnes âgées des pays où les régimes de soins de longue durée sont basés sur l’aide de la famille font état de moins de soins formels que celles des pays dotés d’autres régimes. Le deuxième essai se concentre sur la mesure de la dépendance en prenant en compte les activités instrumentales de la vie quotidienne (AIVQ) et les limitations fonctionnelles. Ainsi, l’accent est mis sur l’identification de la relation entre les soins formels et informels, c’est-à-dire si ce lien est complémentaire ou substitutionnel. En utilisant une approche de modélisation par équation structurelle et des données provenant d’une enquête suisse, le lien s’avère être à la fois complémentaire et substitutif. La deuxième partie de la thèse est consacrée à la gestion des assurances. Nous étudions l’impact des catastrophes naturelles et d’origine humaine sur l’évaluation des sociétés d’assurance. En outre, nous analysons la relation entre les caractéristiques des sociétés et l’effet sur leur évaluation, à savoir la capitalisation boursiére, le sous-secteur, les revenus et l’origine géographique. Pour tous les événements catastrophiques considérés, nous ne trouvons pas de tendance claire dans le comportement du cours de l’action. Malgré le fait que le coefficient de régression correspondant à l’origine géographique n’est pas significatif, nous observons que les entreprises nord-américaines sont davantage influences par des événements locaux, tels que des ouragans sur le territoire américain, et pratiquement pas affectées en cas de catastrophes en dehors du territoire. En revanche, les entreprises européennes sont sensibles à tous les événements, y compris internationaux. En outre, les sociétés de réassurance se révèlent plus sensibles que les sociétés d’assurance Incendie, Accidents et Risques Divers (IARD). Enfin, dans le dernier essai, nous étudions les propriétés de la programmation stochastique dans le cadre de la répartition des actifs pour des fonds de pension suisses soumis à des contraintes de solvabilité. Nous étudions de manière empirique la convergence de la répartition initiale des actifs par rapport à la qualité de l’approximation des rendements stochastiques. Nous observons des résultats pour la probabilité de ruine ainsi que pour la valeur attendue du déficit en cas de défaut. De plus, nous testons la sensibilité des caractéristiques susmentionnées en modifiant différents paramètres internes du modèle. Enfin, les effets d’une mauvaise estimation de la volatilité des actions et du rendement attendu des obligations sur la répartition initiale des actifs sont examinés

Using simulation to investigate impact of different approaches to coordination on a healthcare system’s resilience to disasters

Many disasters that have happened in the last decades have caused a shortage of healthcare resources and change in healthcare activities. Coordination of healthcare facilities is one of the emergency medical response strategies to ensure the continued provision of medical services during disasters. The importance of coordination in healthcare systems during disasters is well recognised in the literature, but to the best of our knowledge there has been no review of the published research in this area. In this thesis, a focused literature review of models for the coordination in the healthcare system is provided. Additionally, measures of coordination effectiveness that denote resilience are discussed. In the field of medical management, there are two types of coordination including integrative care and collaborative care. Both types of coordination aim to improve the emergency medical response by ensuring the continuity of medical services and improving healthcare capability during disasters. Integrative care mainly investigates the resource allocation within a common governance, whereas collaborative care is mainly focused on the sharing of healthcare resources across governances. Thus, integrative care is mainly implemented within a healthcare provider setting, while collaborative care is mainly implemented between the settings. However, resilience is usually perceived at community level rather than at an individual institution when responding to disasters. Improving resilience during disasters requires the capability of different healthcare providers, which can be achieved by collaborative care, rather than integrative care. In addition, the literature has commonly addressed collaborative care using optimisation approach, not simulation approach. In this regard, this study presents simulation models for resilience of the healthcare network during disasters. In collaboration with the health authorities and medical staff in Thailand who experienced a number of disasters we investigated real-world activities that took place in emergency medical responses. We developed novel discrete event simulation models of collaboration in an emergency medical response in a healthcare network during disasters with the aim to improve the resilience of the healthcare network. Three strategies for collaboration in the healthcare network were defined including non-collaborative care, semi-collaborative care, and a new proposed collaborative care. Non-collaborative care strategy was in place in response to Tsunami in Phuket in 2004, while semi-collaborative care strategy is the current strategy which was implemented during the boat capsizing in Phuket in 2018. We propose a new collaborative care strategy which is defined by considering the disadvantages of the current semi-collaborative care strategy. It addresses a new collaboration in the network that enables information sharing and the classification of healthcare providers. The strategies differ with respect to the first treatment provision of patients, sharing of resources, and patient transportation The simulation models were validated and verified by using the boat capsizing real-world event. The model validations were in line with the available system outputs including the number of patients in different categories, resource allocation, patient allocation and average patient waiting times at healthcare providers. A generic metric of resilience proposed in the literature was adapted to be used in healthcare context. Our analysis yielded managerial insights into the emergency planning as follows. In all defined scenarios, the new collaborative care strategy had a considerable impact on improving the resilience and enabled faster return to the pre-disaster state of healthcare network than other strategies. The semi-collaborative care strategy frequently provided the worst resilience in almost all the defined scenarios. However, it provided better resilience than the non-collaborative care strategy when the number of affected patients was relatively small. Even though simulation enabled investigation of the impact of different strategies for collaboration in the network on the resilience, the patient allocation might not be optimal. We developed a mixed integer programming model to address the allocation of patients in collaborative care in which ambulances transport multiple patients to healthcare providers in one trip. The developed model will provide further insights into the collaborative care in disasters management