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

A review of traffic models for wildland-urban interface wildfire evacuation

Recent years have seen an increased prevalence of wildfires, some of which has spread into the wildland-urban interface and lead to large-scale evacuations. Large-scale evacuations gives rise to both logistical and traffic related issues. To aid in the planning and execution of such evacuations reliable modelling tools to simulate evacuation traffic are needed. Today no traffic model exists which is dedicated only to simulate wildfire evacuation in the wildland/urban interface. The aim of this thesis is to identify benchmark characteristics needed in such a model and review 12 existing models, both traffic models and evacuation models, and their potential usefulness in WUI wildfire scenarios. The thesis concludes that some models can be tuned to represent aspects of a WUI fire evacuation and that future research should focus on integrating traffic modelling with modelling of fire/smoke spread and pedestrian movement

A Bus Allocation Model for Major Industrial Disasters

The presented research is part of a broader project DIEM-SSP—Disasters and Emergencies Management for Safety and Security in Industrial Plants –aiming at managing major industrial emergencies by considering both medical and engineering/logistics issues. When a disaster occurs, it is necessary to immediately provide relief plans. Many decisions must be made in very short time, which may have a relevant impact on the consequences of the disaster. For an efficient and smart exploitation of available resources, it is necessary to mitigate damages. From a logistics point of view, one of the major issues in the event of a major industrial disaster (fire, explosion or toxic gas dispersion) is to evacuate the external population that can be affected by the disaster to specific evacuation areas. The purpose of the research is to determine the optimal number and allocation of vehicles (buses) which must be involved in order to evacuate the population located in a defined risk area around the emergency site and the optimal location for evacuation areas. For that reasons, a dynamic version of the bus allocation problem is proposed using a mixed-integer programming model

Egress from a hospital ward during fire emergency

There are many issues in a hospital evacuation, related both to conditions of the patients and to building complexity. Moreover, as consequences of fire, there may be delays in surgeries and medical diagnosis, or interruption in treatment for both inpatient and outpatient. This work identifies and assesses problems that arise in the egress from the ward located at third floor of the Campus Bio-Medico University Hospital of Rome, using a simulation software. Moreover, we perform a comparison between simulation results and experimental results by means of a real fire drill. We have considered a maximum of 116 people in the ward to its maximum capacity. We have created three different fire scenarios: fire in the electrical room, in the kitchen room and in a patient room. The time needed to evacuate fully the ward was far behind the fire resistance time of the structures. More than that, there was an overcrowded area in the ward that acted as a bottleneck: the so-called “smoke proof filter”; this area is intended to separates the two near wards and, although built according to the Italian fire department regulation, it holds back people and beds

Learning from crisis: the 2015 and 2017 avalanches in Longyearbyen

Longyearbyen has been hit by two avalanches in 2015 and 2017 causing severe damages to housing and two fatalities. In this study we investigate organised learning processes regarding emergency preparedness and response following the avalanches. Longyearbyen provides a case of particular interest as climatic change rapidly is altering the environmental conditions, including the risk of avalanches. First, the study outlines the organisation, scope and participation of learning processes, that is, who learns, when and what is the scope. Second we investigate whether the lessons learnt are single-loop or double-loop; if they focus on corrective actions of existing systems and policies, or if they address the more fundamental aspects, such as norms, strategies and policies. Third, we consider how contextual factors influence learning. Finally, we investigate how learning has been followed up by implementation. The study concludes that the first avalanche of 2015 led to a broad and inclusive evaluation and learning process and a series of recommended measures, including the establishment of an avalanche warning system. It also initiated a broader double-loop process of reassessing risks, redrawing the plans and maps of Longyearbyen, and raising physical preventive barriers. However, the second avalanche demonstrated the limitations of the established system in 2015. This spurred a range of corrective actions to the system, but also it established that in a time of climate change, historical experience no longer provides a basis for assessing risks

SIMULATION AND MATHEMATICAL MODELING TO SUPPORT COMMUNITY-WIDE EVACUATION DECISIONS FOR MULTIPLE POPULATION GROUPS

Evacuating a large population from an at-risk area has been the subject of extensive research over the past few decades. In order to measure trip completion and total evacuation times accurately, most researchers have implemented some combination of simulation and optimization methods to provide vehicular flow and congestion data. While the general at-risk population comprises the majority of travelers on the road network, there are often specific groups to consider when assessing the ability to evacuate an entire population. In particular, healthcare facilities (e.g., hospitals) may require evacuation, and the trip times may become an important health issue for patients being evacuated. Emergency vehicles from these facilities will share the same roadways and exit paths that are used by the local community, and it becomes increasingly important to minimize long travel times when patient care must be provided during transport. As the size of the area to model grows larger, predicting individual vehicle performance becomes more difficult. Standard transportation-specific micro-simulation, which models vehicle interactions and driver behaviors in detail, may perform very well on road networks that are smaller in size. In this research, a novel modeling approach, based on cell transmission and a speed-flow relationship, is proposed that combines the \u27micro\u27 and \u27meso\u27 approaches of simulation modeling. The model is developed using a general purpose simulation software package. This allows for an analysis at each vehicle level in the travel network. In addition, using these method and approaches, we can carry out dynamic trip planning where evacuees decide their route according to current road and traffic conditions. By translating this concept to an actual implementation, a traffic management center could identify current best travel routes between several origins and destinations, while continuing to update this list periodically. The model could suggest routings that favor either a user-optimal or system-optimal objective. This research also extended the concept of dynamic traffic assignment while modeling evacuation traffic. This extension includes the utilization of Wardrop\u27s System Optimum theory, where flow throughout the network is controlled in order to lower the risk of traffic congestion. Within this framework traffic flow is optimized to provide a route assignment under dynamic traffic conditions. This dissertation provides a practical and effective solution for a comprehensive evacuation analysis of a large, metropolitan area and the evacuation routes extending over 100 miles. Using the methodologies in this dissertation, we were able to create evacuation input data for general as well as special needs populations. These data were fed into the tailored simulation model to determine critical evacuation start times and evacuation windows for both the community-wide evacuation. Moreover, our analysis suggested that a hospital evacuation would need to precede a community-wide evacuation if the community-wide evacuation does not begin more than 24 hours before a hurricane landfall. To provide a more proactive approach, we further suggested a routing strategy, through a dynamic traffic assignment framework, for supporting an optimal flow of traffic during an evacuation. The dynamic traffic assignment approach also provides a mechanism for recommending specific time intervals when traffic should be diverted in order to reduce traffic congestion

Local public sector allocation of scarce emergency assets : an evaluation of the fire service.

The methodology utilized by public sector managers to allocate scarce resources determines the level, efficiency, and effectiveness of service delivery. These decisions are influenced by a myriad of factors, not the least of which is the ideal goal to distribute services in a fair and equitable manner. This ideal becomes problematic if service outcomes are important to public decision-makers, because the level of need for these resources is not spread equally across local jurisdictions. Therefore, when goods and services are located or distributed equally to all “customers,” many do not receive enough assistance and others receive more than they prefer. This causes inefficient service delivery that fails to maximize potential positive outcomes with the available limited resources. This is particularly true with the geographic distribution of fire service resources across most communities in this country. This research effort attempts to model the demographic characteristics that drive emergency service demand and workload across local jurisdictions. Specifically, data about demographic characteristics was collected at the Census block group level and compared to emergency response data collected by the Charlotte Fire Department. The findings from this effort are promising, as the bivariate correlation and multivariate regression analyses indicate that economic and structural factors common to all local communities can be used to confidently predict demand and workload on local public safety systems. Measuring these characteristics at the block group level permitted the opportunity to isolate homogenous groups within the population that have risk characteristics associated with more or less demand for these services. These findings provide a solid base to support the development of an alternative model for locating these critical emergency resources according to demand and workload to better meet the needs of individual communities

Organizational Complexity, Plan Adequacy, and Nursing Home Resiliency: A Contingency Perspective

Some social and organizational behavior scientists measure resiliency through anecdotal qualitative research, i.e. personality analyses and stories of life experience. Empirical evidence remains limited for identifying measurable indicators of resiliency. Therefore, a testable contingency model was needed to clarify resiliency factors pertinent to organizational performance. Two essential resiliency factors were: 1) a written plan and 2) affiliation with a disaster network. This contingency study demonstrated a quantifiable, correlational effect between organizational complexity, disaster plan adequacy and organizational resiliency. The unit of analysis, the skilled nursing facility proved vulnerable, therefore justifying the need for a written emergency management plan and affiliation with a disaster network. The main purpose of this research was to verify the significance of emergency management plans within a contingency framework of complexity theory, resource dependency, systems theory, and network theory. Distinct sample moments quantified causal relationships between organizational complexity (A), plan adequacy (B) and resiliency (C). Primary and secondary research data were collected from within the context of public health and emergency management sectors within the State of Florida

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit

The Role of Telehealth in Disaster Management: Lessons for the Philippines

According to the WorldRiskIndex 2016, the Philippines is the third country most at risk of disasters in the world. Typhoon Haiyan, the strongest on local record, caused widespread destruction to life and property. Current disaster management strategies in the country do not include telehealth as a formal tool in disaster mitigation, response, or recovery. This study reviewed research incorporating telehealth in disaster management from multiple low and lower middle income countries like the Philippines to address this gap by identifying lessons the country might be able to adopt. Studies show that most initiatives centre on evaluating telehealth’s effectiveness during the response phase. Unsurprisingly, mobile technology and satellite communications predominated, and most projects were launched using donor funding. Use of telehealth in disaster management in the Philippines could begin by recognising and including telehealth in formal government protocols. The government could leverage the National Telehealth Service Program of the University of the Philippines National Telehealth Center. Documentation and systematic research on telehealth’s expected positive contributions to disaster preparedness and response should also be initiated