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Investigates the increased waiting time costs imposed on society due to inappropriate use of the emergency department by patients seeking non‐emergency or primary care. Proposes a simple economic model to illustrate the effect of this misuse at a public or not‐for‐profit hospital. Provides evidence that non‐emergency patients contribute to lengthy delays in the ER for all classes of patients. Proposes a priority queuing model to reduce average waiting times.

Using computer simulation, this paper seeks to model the emergency care process in a hospital and evaluate the effects of some proposed changes to improve patient wait times in the process.

The paper is based upon a case study conducted at the hospital and uses historical data provided by the hospital to simulate the emergency care process.

The simulation results demonstrate that the changes proposed can shorten patient wait times in the emergency care process. The proposed changes involve adding another payment station and a new short‐stay ward in the process. Based upon the results, the paper supports the implementation of the changes proposed.

A couple of limitations are recognized in this paper. First, the simulation does not consider varying the capacity of resources and locations involved in the emergency care process. Second, the simulation does not consider patients by clinical disciplines in which they are treated.

The simulation results show that computer simulation can be an effective decision support tool in modelling the emergency care process and evaluating the effects of changes in the process. The results would be helpful to those who are considering reengineering and improving emergency care or other similar processes in hospitals.

Based upon a case study using real‐world data, this paper extends the line of studies on computer simulation in healthcare by considering not only patient wait times in the emergency care process but also some ways to improve patient wait times and their effects on the process.

The purpose of this paper is to analyze contemporary uses and gratifications (U&G) of the media, focusing on the differences between emergency and ordinary times, and between media consumers in the border region and in the home front during the Israel-Gaza War (2014).

The study used a questionnaire containing 184 items. This significant number of items was necessary due to the large number of media channels and potential uses examined. Due to the length of the questionnaire, and the inclusion of individuals who are not habitual internet users, data were collected in the field rather than through a telephone survey or online. The list of media and uses was compiled based on a review of existing literature regarding functions of media in emergencies.

Television and news websites are dominant suppliers of national and local information, but mobile and social channels lead in terms of social uses, discussions, requests and provision of assistance. The same channels were almost always used during emergencies and ordinary times to satisfy a specific need. The leading channels – television, Facebook, WhatsApp and SMS – were used significantly more on the frontlines than on the home front. The findings demonstrate that people use diverse media, but channels that are live, visual, social and mobile are dominant.

Very few academic studies have compared media uses during ordinary times and emergencies, and those existing focus on the uses of a specific medium. The present study examines various U&G of traditional and new media during the war, compares uses during the war with uses during ordinary times, and compares the population in the border region with the population in the home front.

The purpose of this paper is to identify the initial emergency response time of fire fighting teams in Malaysia.

In an emergency incident time is of the essence, and the basic philosophy of an emergency response agency is to respond as quickly as possible to minimize the loss of life and property damage. In the current study, emergency response performance refers to team members' speed in responding to emergency situations, which was measured as the time taken for the team members to get to the fire truck from the waiting room in selected fire stations in Malaysia. The data collection period lasted for five months.

This study found that the overall average initial emergency response time was 84 seconds, while the overall average weighted initial emergency response time was 3.71 seconds per meter. The current study has demonstrated that the average initial emergency response performed by fire fighting teams in Malaysia is apparently better than that reported by previous studies by other emergency responders.

This paper presents empirical evidence of the initial emergency response time of fire fighters in Malaysia, by taking into account the distance traveled by the responders. As such, the performance measure obtained gives a meaningful indicator. The finding of the current study is then compared to emergency response performance by other emergency response agencies in other countries.

The COVID-19 pandemic has changed many facets of urban life and operations, including emergency incidents. This study examines how COVID-19 has brought about changes in, and shifting patterns of, emergency incidents in the City of Vaughan, Ontario, Canada. This study aims to derive insights that could potentially inform planning and decision-making of fire and rescue service operations as further stages of the pandemic unfold.

Standard temporal analysis methods are applied to investigate the changes in the number and nature of emergency incidents, as recorded sequentially in the city's fire and rescue service incident report database, through various phases or waves of the pandemic and the associated public health measures that have been introduced.

The study analyses show a decrease in the number of emergency calls compared to previous reference years. Vehicle-related incidents show the highest decline, and changes in daily and hourly pattens are consistent with public health measures in place during each stage of the pandemic. The study concludes that the COVID-19 pandemic has had a significant impact on demand for emergency services provided by the fire department.

The authors believe this is the first study applying temporal analysis on a city's emergency incident response data spanning various phases/waves of the COVID-19 pandemic. The analysis may be replicated for other municipal fire services, which can generate further insights that may apply to specific local conditions and states of the pandemic.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

This paper challenges and expands commonplace assumptions about problems of time and temporality in emergencies. In traditional emergency powers theory “emergency time” is predominantly an “exceptional time.” The problem is that there is “no time” and the solution is limited “in time”: exceptional behavior is allowed for a special time only, until the emergency is over, or according to formal sunset clauses. But what is characteristic of many emergencies is not the problem of “no time” but the ways in which time is legally structured and framed to handle them. Using the Israeli High Court of Justice 1999 decision on the use of physical interrogation methods under conditions of necessity, this paper illustrates how legally significant emergency-time structures that lay beyond the problematic of exceptional time, gravely implicate the way that “exceptional measures” are practiced and regularized.

While scheduling and transporting emergency materials in disasters, the emergency materials and delivery vehicles are arriving at the distributing center constantly. Meanwhile, the information of the disaster reported to the government is updating continuously. Therefore, this paper aims to propose an approach to help the government make a transportation plan of vehicles in response to the disasters addressing the problem of material demand and vehicle amount continual alteration.

After elaborating the features and process of the emergency materials transportation, this paper proposes an emergency materials scheduling model in the case of material demand and vehicle amount continual alteration. To solve this model, the paper provides the vehicle transportation route allocation algorithm based on dynamic programming and the disaster area supply sequence self-learning algorithm based on ant colony optimization. Afterwards, the paper uses the model and the solution approach to computing the optimal transportation scheme of the food supply in Lushan earthquake in China.

The case study shows that the model and the solution approach proposed by this paper are valuable to make the emergency materials transportation scheme precise and efficient. The problem of material demand and vehicle amount changing continually during the process of the emergency materials transportation is solved promptly.

The model proposed by this paper improves the existing similar models in the following aspects: the model and the solution approach can not only solve the emergency materials transportation problem in the condition of varying demand and vehicle amount but also save much computing time; and the assumptions of this model are consistent with the actual situation of the emergency relief in disasters so that the model has a broad scope of application.