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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.

Major incidents in tunnel environment will pose several challenges for the emergency service organisations in terms of heat, visibility and lack of experiences from working in confined environments. These aspects, in turn, could pose challenges to establish collaboration. This study aims to contribute to the field of collaborative tunnel responses by exploring how “common knowledge” (Edwards, 2011) is built by the emergency services organisations, that is, what the organisations consider important while working on a potentially common problem, and their motives for the interpretations and actions if a major tunnel incident occurs.

Participants from the road traffic control centre, emergency dispatch centre, emergency medical service, rescue service and police were included in the study. Data from four focus group sessions was analysed using thematic analysis.

The study revealed that the tunnel environment presents specific aspects of how common knowledge was produced related to lifesaving and safety. The themes structuring mechanisms to reduce uncertainty, managing information for initial priorities, aligning responsibilities without hampering each other's work and adjusting actions to manage distance, illustrated how common knowledge was produced as crucial aspects to a collaborative response. Organising management sites, grasping and communicating risks, accessing the injury victims, was challenged by the confined environment, physical distances and imbalance in access to information and preparedness activities in tunnel environments.

This study offers new insights of common knowledge, by illustrating a motive perspective on collaborative responses in tunnel incidents. Creating interoperability calls not just for readiness for action and tunnel safety, but also training activities acknowledging different interpretations and motives to further develop tunnel responses.

This study focuses on Emergency Medical Service (EMS) personnel and examines the impact of overdose calls for opioids and attitudes of EMS workers towards individuals with substance use disorders on EMS workers' mental well-being while accounting for self-reported sleep and social support.

This cross-sectional study surveyed EMS workers (N = 608) across Pennsylvania on demographic variables, frequency of overdose calls, attitudes towards opioid use and naloxone administration on measures of mental health. Multiple logistic regression models were estimated to examine the relationship of perception of opioid use and treatment and likelihood that EMS workers might experience depression.

Authors found two main findings: (1) There was a significant relationship between more negative perceptions about opioid use/naloxone and the likelihood that EMS workers might experience depression. (2) There was a significant relationship between number of overdose calls EMS workers responded to and likelihood of depression, which appeared to be alleviated by improvements in sleep and social support.

There is potential opportunity for EMS employers to minimize the impact of the opioid epidemic on EMS worker mental health. Trainings to highlight effectiveness of treatment should be further explored, along with ways to enhance social support and improve sleep for EMS workers to protect against the stress associated with responding to this public health crisis.

This study adds to the literature on the impact of the opioid epidemic as it relates to mental health outcomes for EMS professionals providing frontline care to those experiencing opioid use disorders.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.

Using Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.

Comparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.

First, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.

The results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.

The results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.

The research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.

As a human right, people need to arrive early at the hospitals when they are injured in traffic accidents. Both the mean and equality of the time of arriving at the hospital are important. This study aimed to investigate inequality in arrival time of emergent traffic accident patients to the hospital in 2018–2019 in a city in the North of Iran.

The authors extracted the data from the Guilan province trauma system databank in Poursina Hospital in Rasht in 2018 and 2019. The Gini coefficient was used to calculate inequality, and a regression model was estimated for determining the reason for inequality in time to receive hospital services.

The study showed that patients’ arrival time from the time of the accident to the time of arrival to the hospital was 64.48 ± 47.63 min (minimum of 9 min and maximum 462 min). Gini coefficient was 0.31 (p <0.001), which does not show high inequality. Regression results showed that the transfer time of patients by car was 40 min longer (p-value <0.001) than ambulances (p = 0.036). In children, the transfer time was 42 min less (p = 0.003). Other variables did not explain the inequality (p > 0.05).

According to the time of arrival of patients and Gini index, in Rasht, inequality in providing services is not in a bad condition. This indicates that the emergency department does not systematically transport people to the hospital late.

This study describes preparedness of emergency physicians and general practitioners following chemical, radiological and nuclear incidents.

Five emergency physicians and six general practitioners were interviewed individually, and data was analysed using qualitative content analysis.

The study results showed that physicians' preparedness for chemical, radiological and nuclear incidents is linked to one main category: to be an expert and to seek expertise and two categories: preparations before receiving CRN patients, and physical examination and treatment of CRN patients with subcategories.

The results have implications for further research on the complexity of generalist vs specialist competence and knowledge when responding to chemical, radiological and nuclear incidents.

This study provides insights regarding chemical, radiological and nuclear preparedness among physicians at emergency departments and primary healthcare centres.

The years following the 9/11/2001 terrorists attacks saw a marked increase in community and hospital emergency preparedness, from communications across community networks, development of policies and procedures, to attainment and training in the use of biological warfare resources. Regular drills ensured emergency and health care personnel were trained and prepared to address the next large-scale crisis, especially from terrorist and bioterrorist attacks. This chapter looks at some of the more familiar global health issues over the past two decades and the lessons learned from hospital responses to inform hospital management in preparation for future incidents.

This study is a narrative review of the literature related to lessons learned from four major events in the time period from 2002 to 2023 – SARS, MERS, Ebola, and COVID-19.

The initial search yielded 25,913 articles; 57 articles were selected for inclusion in the study.

Comparison of key issues and lessons learned among the four major events described in this article – SARS, MERS, Ebola, and COVID-19 – highlight that several lessons are “relearned” with each event. Other key issues, such as supply shortages, staffing availability, and hospital capacity to simultaneously provide care to noninfectious patients came to the forefront during the COVID-19 pandemic. A primary, ongoing concern for hospitals is how to maintain their preparedness given competing priorities, resources, and staff time. This concern remains post-COVID-19.

We present the results of one of the only in-depth studies of a police agency’s internal and external response to the coronavirus pandemic of 2020 (COVID-19). This study emphasizes the importance of law enforcement agencies conducting comprehensive case studies and after-action assessments to prepare, prevent and respond to prolonged public health crises and showcases the profound (and lingering) effects of COVID-19 on police organizations.

This multi-method case study combines document analysis, a workforce survey, a community survey, interviews and analysis of administrative data to detail and assess the agency’s internal and operational responses to the pandemic and the reactions of employees and community members to those responses.

Despite agency strategies to mitigate the pandemic’s effects, employees cited very high stress levels one year after the pandemic and a third of sworn officers considered leaving the policing profession altogether during the first two years of the COVID-19 pandemic. Several policies intended by the agency to protect employee health and maintain staffing needs kept workforce levels steady but may have increased feelings of organizational injustice in both sworn and non-sworn individuals, with variation across racial and gender groups. A jurisdiction-wide community survey indicated general support for the police department’s responses but a preference for in-person rather than telephone-based responses to service calls. Officers, however, preferred continuing remote responses even after the pandemic subsided.

To the best of our knowledge, this is one of the only in-depth case studies that examine a police agency’s internal and external responses to COVID-19 and the sworn, non-sworn and community reactions to those responses.

Theory of Constraints (TOC), though a well-established process improvement methodology in manufacturing, is still a novel philosophy for healthcare and an exhaustive review of literature is needed to summarize the key findings of various researchers. Such a review can provide a direction to the researchers and academicians interested in exploring the application of TOC in the healthcare sector. This paper aims to review the existing literature of TOC tools and techniques applied to the healthcare environment, and to investigate motivating factors, benefits and key gaps for identifying directions for future research in the domain of healthcare.

In this paper, different electronic repositories were searched using multiple keywords. The current study identified 36 articles published between January 1999 to mid-2021 to conceptualize and summarize the research questions used in the study. Descriptive analysis along with pictorial representations have been used for better visualization of work.

This paper presents a thorough literature review of TOC in healthcare and identifies the evolution, current trends, tools used, nature of services chosen for application and research gaps and recommends future direction for research. A variety of motivating factors and benefits of TOC in healthcare are identified. Another key finding of this study is that almost all implementations listed in literature reported positive outcomes and substantial improvements in the performance of the healthcare unit chosen for study.

This paper provides valuable insight to researchers, practitioners and policymakers on the potential of TOC to improve quality of services, flow of patients, revenues, process efficiency and cost reduction in different health care settings. A number of findings and suggestions compiled in the paper from literature study can be used for diagnosing, learning and making substantial changes in healthcare. The methodologies used by different researchers were analysed and combined to propose a generic step by step procedure to apply TOC. This methodology will guide the practising managers about the appropriate tools of TOC for their specific need.

Good health is always the first desire of all men and women around the globe. The global aim of healthcare is to quickly cure more patients and ensure healthier population both today and in future. This article will work as a foundation for future applications of TOC in healthcare and guide upcoming applications in the booming healthcare sector. The paper will help the healthcare managers in serving a greater number of patients with limited available resources.

This paper provides original collaborative work compiled by the authors. Since no comprehensive systematic review of TOC in healthcare has been reported earlier, this study would be a valuable asset for researchers in this field. A model has been presented that links various benefits with one another and clarifies the need to focus on process improvement which naturally results in these benefits. Similarly, a model has been presented to guide the users in implementation of TOC in healthcare.