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The SmartEUS project aims to design, develop, and test a highly innovative model for the provision of Emergency Urgency Services (EUS) based on the concept of clinical pathway (i.e., the entire emergency care chain). The project is innovative both from an organizational and technological point of view. It is based on an e-Health approach, capable of exploiting new digital technologies in a coordinated way to support the evolution of emergency health systems towards dynamic and safer processes. The paper presents the SmartEUS platform which can integrate the information from all the key players in medical emergency response (emergency operation center, emergency medical service, hospital emergency department) improving their coordination. The most relevant innovations of the proposed solution are data integration and interoperability, advanced communication and collaboration, knowledge-based resource allocation, telemedicine integration to support decision making in dispatching and resource assignment. In fact, a Decision Support System is integrated into SmartEUS cloud platform to suggest to the emergency operation center which emergency medical service and hospital emergency department. In fact, information on the patient status can include results from ambulance's on board telediagnosis equipment, alerting the hospital emergency department even before the arrive of the patient, thus guaranteeing the appropriateness of care according to patient needs (e.g., patient pre-triage index, potential pre-hospital diagnosis) and reducing emergency department overcrowding and resources saturation. [ABSTRACT FROM AUTHOR]

A mobile stroke unit (MSU) is a specialized ambulance, enabling to shorten the time to diagnosis and treatment for stroke patients. In the current paper, we present a simulation-based approach to study the potential impacts of collaborative use of regular ambulances and MSUs in prehospital transportation for stroke patients, denoted as co-dispatching. We integrated a co-dispatch policy in an existing modeling framework for constructing emergency medical services simulation models. In a case study, we applied the extended framework to southern Sweden to evaluate the effectiveness of using the co-dispatch policy for different types of stroke. The results indicate reduced time to diagnosis and treatment for stroke patients when using the co-dispatch policy compared to the situation where either a regular ambulance or an MSU is assigned for a stroke incident. [ABSTRACT FROM AUTHOR]

Ambulance travel time estimations play a pivotal role in ensuring timely and efficient emergency medical care by predicting the duration for an ambulance to reach a specific location. Overlooking factors such as local traffic situations, day of the week, hour of the day, or the weather may create a risk of inaccurately estimating the ambulance travel times, which might lead to delayed emergency response times, potentially impacting patient outcomes. In the current paper, we propose a novel framework for accurately estimating ambulance travel times using machine learning paradigms, employing real-world spatiotemporal ambulance data from the Skane region, Sweden. Our framework includes data preprocessing and feature engineering, with a focus on variables significantly correlated with travel time. First, through a comprehensive exploratory data analysis, we highlight the main characteristics, patterns, and underlying trends of the considered ambulance data set. Then, we present an extensive empirical analysis comparing the performance of different machine learning models across different ambulance travel trip scenarios and feature sets, revealing insights into the importance of each feature in improving the estimation accuracy. Our experiments indicate that the aforementioned factors play a significant role when estimating the travel time. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid fuzzy AHP (Analytic Hierarchy Process) – DEMATEL (Decision Making Trial and Evaluation Laboratory) approach to determine the most relevant criteria to measure the performance of municipal Emergency Medical Service (EMS) providers transparently and comprehensively. Allocation of emergency medical services affects public finances and the district's social management. Therefore, evaluating EMS providers' performance is crucial based on multiple factors, such as equipment, vehicle condition, pre-hospital care, diagnoses, costs, staff training, and financial stability. Multi-criteria decision-making tools can help to obtain a comprehensive and objective assessment of EMS providers' performance rather than relying solely on response time, which can be subject to random fluctuations. The proposed approach uses a hybrid fuzzy AHP-DEMATEL that determines that the managerial criteria are more relevant in the process, weighing 47.4%. This model will help ensure the quality and transparency of emergency medical services and guarantee that resource allocation is accountable. This will solve the district's current challenge when deciding whom to assign the EMS concessions. The results of this study demonstrate the significance of using a comprehensive evaluation model to ensure the quality of emergency medical services and enhance the decision-making process of state authorities. [ABSTRACT FROM AUTHOR]

Objective: Ambulance dispatch algorithms should function as clinical prediction rules, identifying high acuity patients for advanced life support, and low acuity patients for non-urgent transport. Systematic reviews of dispatch algorithms are rare and focus on study types specific to the final phases of rule development, such as impact studies, and may miss the complete value-added evidence chain. We sought to summarise the literature for studies seeking to improve dispatch in trauma by performing a scoping review according to standard frameworks for developing and evaluating clinical prediction rules.Methods: We performed a scoping review searching MEDLINE, EMBASE, CINAHL, the CENTRAL trials registry, and grey literature from January 2005 to October 2021. We included all study types investigating dispatch triage to injured patients in the English language. We reported the clinical prediction rule phase (derivation, validation, impact analysis, or user acceptance) and the performance and outcomes measured for high and low acuity trauma patients.Results: Of 2067 papers screened, we identified 12 low and 30 high acuity studies. Derivation studies were most common (52%) and rule-based computer-aided dispatch was the most frequently investigated (23 studies). Impact studies rarely reported a prior validation phase, and few validation studies had their impact investigated. Common outcome measures in each phase were infrequent (0 to 27%), making a comparison between protocols difficult. A series of papers for low acuity patients and another for pediatric trauma followed clinical prediction rule development. Some low acuity Medical Priority Dispatch System codes are associated with the infrequent requirement for advanced life support and clinician review of computer-aided dispatch may enhance dispatch triage accuracy in studies of helicopter emergency medical services.Conclusions: Few derivation and validation studies were followed by an impact study, indicating important gaps in the value-added evidence chain. While impact studies suggest clinician oversight may enhance dispatch, the opportunity exists to standardize outcomes, identify trauma-specific low acuity codes, and develop intelligent dispatch systems. [ABSTRACT FROM AUTHOR]

Communication and collaboration in transport systems are overdeveloped through Vehicular Ad hoc Networks (VANET). In VANETs, various messages are sent to activate warnings about road accidents, sharp turns, traffic jams, etc. Data security in VANETs is then essential to prevent the intrusion of hackers. Although security and safety in VANETs have been the subject of several research works, some intelligent vehicles contain extremely sensitive data that require specific high-level and real-time security solutions. This is the case for connected medical ambulances in which surgery can be performed with the help of an outside doctor. In such cases, video is the most complicated type of data to handle due to its size, content, and structure. In this paper, an elliptical approach to video representation is introduced, and a video crypto-compression scheme is proposed. The scheme described is built on Elliptic Curve Cryptography, which demonstrates good performance in computer security in recent years. [ABSTRACT FROM AUTHOR]

Emergency Medical Services are essential for health systems as their effective management can improve patient prognosis. Nevertheless, designing an optimized distribution of resources is a difficult task due to the complex nature of these systems. Moreover, locating the resources is particularly challenging in heterogeneous density territories where, in addition to their efficient management, the equity principle in the medical access of inhabitants of rural areas is also desirable. This paper approaches the ambulance (re)location–allocation problem in the geographical area of the Basque Country. The area has three major cities, which account for a third of the emergencies, while there are few emergencies in rural areas, with a sparse population. To that end, a two-stage stochastic 0-1 integer linear programming model that balances the response time between densely populated and isolated areas is proposed. Specifically, the model incorporates two relevant principles: (1) optimizing emergency attendance through the option of allocating ambulances via a multi-interval response time and (2) equitably responding to emergencies so remote areas are not neglected. Conducted experiments have been validated and indicate that the proposed model can improve the success rate in rural areas by 23 percentage points, while reducing the overall success rate by less than 9 percentage points. • We develop two data-driven ambulance (re)location–allocation stochastic models. • Two objectives: Concern for regional equity while maintaining efficiency. • Multi-interval response times are optimized in the Basque Emergency Medical Service. • Robust and global validated results show an improvement in success rates. [ABSTRACT FROM AUTHOR]

Redundancy aspects related to covering facility location problems are of extreme importance for many applications, in particular those regarding critical services. For example, in the healthcare sector, facilities such as ambulances or first-aid centers must be located robustly against unpredictable events causing disruption or congestion. In this paper, we propose different modeling tools that explicitly address coverage redundancy for the underlying service. We also evaluate, both theoretically and experimentally, the properties and behavior of the models, and compare them from a computational and managerial point of view. More precisely, by starting from three classical double-covering models from the literature (BACOP1, BACOP2, and DSM), we define three parametric families of models (namely, K -BACOP1, K -BACOP2, and K -DSM) which generalize the former to any possible K th coverage level of interest. The study of such generalizations allows us to derive interesting managerial insights on location decisions at the strategic level. The CPU performance and the quality of the solutions returned are assessed through ad-hoc KPIs collected over many representative instances with different sizes and topological characteristics, and also by dynamically simulating scenarios involving possible disruption for the located facilities. Finally, a real case study concerning ambulance service in Morocco is analyzed. The results show that, in general, K -BACOP1 performs very well, even if intrinsic feasibility issues limit its broad applicability. Instead, K -DSM achieves the best coverage and equity performances for lower levels of redundancy, while K -BACOP2 seems the most robust choice when high redundancy is required, showing smoother and more predictable trends. • Covering facility location models, focusing on service redundancy aspects. • Generalization of BACOP1, BACOP2, and DSM to encompass any level of coverage. • Theoretical comparison of the three parametric families of generalized models. • Simulation–optimization framework to empirically compare the models via ad-hoc coverage and equity KPIs. • Experiments over artificial instances and an ambulance service case-study in Morocco. [ABSTRACT FROM AUTHOR]

Advancements in mobile technology have propelled the rapid progress of remote health monitoring, particularly in the domain of mobile health (mHealth). This progress is underscored by sophisticated smart health monitoring applications designed for mobile devices. mHealth monitoring, a standout area within remote health monitoring, excels in providing continuous health monitoring for patients and enabling remote observation by physicians. These applications cater to ambulatory patients with chronic diseases, offering essential features such as Telemedicine services, doctor availabilitys, and emergency ambulance services. Despite the development of various frameworks for mHealth monitoring, existing review papers often fall short in addressing critical parameters and issues related to Telemedicine. Many reviews provide a broad overview but lack the depth needed to dissect intricate aspects like implementation challenges in Telemedicine, nuances in decision-making applications, and the dynamics of mobility for both patients and healthcare providers. Patient rescue protocols, especially in diverse locations, are also underexplored. To fill this gap, our research delves into these dimensions, providing a nuanced understanding of challenges and opportunities in mobile health monitoring, particularly within the Telemedicine context. Leveraging a comprehensive literature review, we meticulously selected over 100 research articles from reputable databases like ScienceDirect, IEEE Xplore, and Springer. We have assessed and ranked the significance of each article. The study aims to address open issues in telemedicine, contributing valuable insights to enhance remote health monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a new framework that models the novel dynamic vehicle dispatch problem with holding costs (DVDPHC), which focuses on serving stochastic demands at geographically dispersed locations in a timely manner. This framework is applicable, among others, to the post-disaster ambulance bus routing problem , where an ambulance bus must pick up (urgent) patients at geographically dispersed locations and bring them to a centrally-located hospital as quickly as possible. Solving the DVDPHC requires a dynamic decision-making rule at each decision moment for which demands to serve at the current location, and where to direct the vehicle next. We propose a heuristic based on approximate dynamic programming combined with a neural network (ADP-NN) for effectively solving the DVDPHC. Numerical experiments demonstrate that our proposed method is fast, scalable and robust. Furthermore, it keeps up with computationally heavy direct lookahead (DLA) benchmarks on 120 large representative instances, achieving on average 12.77% total cost improvement. Numerical analysis also reveals that our proposed method exhibits complex self-learned flexible behavior, such as waiting near locations in anticipation of new demand. • We introduce a new dynamic vehicle dispatch problem with holding costs (DVDPHC). • DVDPHC explicitly models the trade-off between time-sensitivity and travel costs. • DVDPHC considers stochastic demand and infrastructure rigidity. • An approximate dynamic programming with neural network (ADP-NN) approach is proposed. • ADP-NN is demonstrably fast, scalable and robust. • ADP-NN exhibits complex and flexible self-learned behavior. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 epidemic has had a significant impact in increasing the number of emergency calls, which causes significant problems to emergency medical services centers (EMS) in many countries around the world, such as Saudi Arabia, which attracts a huge number of pilgrims during pilgrimage seasons. Among these issues, we address real-time ambulance dispatching and relocation problems (real-time ADRP). This paper proposes an improved MOEA/D algorithm using Simulated Annealing (G-MOEA/D-SA) to handle the real-time ADRP issue. The simulated annealing (SA) seeks to obtain optimal routes for ambulances to cover all emergency COVID-19 calls through the implementation of convergence indicator based dominance relation (CDR). To prevent the loss of good solutions once they are found in the G-MOEA/D-SA algorithm, we employ an external archive population to store the non-dominated solutions using the epsilon dominance relationship. Several experiments are conducted on real data collected from Saudi Arabia during the Covid-19 pandemic to compare our algorithm with three relevant state-of-art algorithms including MOEA/D, MOEA/D-M2M and NSGA-II. Statistical analysis of the comparative results obtained using ANOVA and Wilcoxon test demonstrate the merits and the outperformance of our G-MOEA/D-SA algorithm. • Propose an improved MOEA/D algorithm using Simulated Annealing, called G-MOEA/D-SA. • Address real-time ambulance dispatching and relocation problems using G-MOEA/D-SA algorithm. • The paper seeks to provide an efficient response to current and future emergencies. • Use real data from Saudi Arabia in Covid-19 context for compare the effectiveness of our improved G-MOEA/D-SA algorithm. [ABSTRACT FROM AUTHOR]

Work integrated learning (WIL) in the ambulance setting is an essential part of the paramedic curriculum. However, qualified ambulance personnel are reported to experience higher suicide rates and mental stress disorders due to high pressure work environments, and there is growing concern for the wellbeing of students entering this setting. The aim of this integrative review was to explore how studies have reported paramedic students' experience of stress whilst undertaking WIL. Five studies met the inclusion criteria and were evaluated for quality according to validated tools from the Critical Appraisal Skills Program, then ranked on the level of evidence used. Data was summarised in a comprehensive research paper matrix, and findings were categorised into levels and sources of stress. Levels of stress were measured by the percentage of paramedic students who developed post-traumatic stress disorder. The primary sources of stress were experiencing death and fear of making clinical mistakes. Students also identified emotional expression as a negative attribute. Future research should prioritise identifying the levels and sources of stress students face in each year of their academic program when undertaking WIL to provide a direction for preparatory activities that may mitigate the negative effects of stress. [ABSTRACT FROM AUTHOR]

• The proposed TB-MFCC multifuse feature is comprised of data augmentation and feature extraction. • First, Pooled Multifuse Feature Augmentation uses the noise injection, stretching, shifting and pitching separately to increase the number of instances in the dataset and reduces overfitting. • Second, Triangular Bluestein Mel Frequency Cepstral Coefficients (TB-MFCC) are proposed and fused with Zero Crossing Rate (ZCR), Mel-frequency cepstral coefficients (MFCC) to extract the exact feature which increases the accuracy and reduces the Mean Squared Error (MSE). • Finally, the proposed MCNN with A-BiLSTM improves the nonlinear relationship among the features. • Thus the proposed augmentation, segmentation and classification is used to increase the accurate prediction of vehicle with reduced loss, less chance of overfitting and underfitting and avoids vanishing gradient problems. Vehicles equipped for emergencies like ambulances, fire engines, and police cruisers play a vital role in society by responding quickly to emergencies and helping to prevent loss of life and maintain order. Vehicle sound identification and classification are very important in the cities to identify emergency vehicles easily and to clear the traffic effectively. Convolutional Neural Network plays an important role in the accurate prediction of vehicles during an emergency. The main motive of this paper is to develop a suitable model and algorithms for data augmentation, feature extraction, and classification. The proposed TB-MFCC multifuse feature is comprised of data augmentation and feature extraction. First, in the proposed signal augmentation, each audio signal uses noise injection, stretching, shifting, and pitching separately and this process increases the number of instances in the dataset. The proposed augmentation reduces the overfitting problem in the network. Second, Triangular Bluestein Mel Frequency Cepstral Coefficients (TB-MFCC) are proposed and fused with Zero Crossing Rate (ZCR), Mel-frequency cepstral coefficients (MFCC), Root Mean Square (RMS), Chroma, and Tempogram to extract the exact feature which increases the accuracy and reduces the Mean Squared Error (MSE) of the model during classification. Finally, the proposed Multi-stacked Convolutional Neural Network (MCNN) with Attention-based Bidirectional Long Short Term Memory (A-BiLSTM) improves the nonlinear relationship among the features. The proposed Pooled Multifuse Feature Augmentation (PMFA) with MCNN & A-BiLSTM increases the accuracy (98.66 %), reduces the False Positive Rate (FPR) by 1.01 %, and loss (0 %). Thus the model predicts the sound without overfitting, underfitting, and vanishing gradient problems. [ABSTRACT FROM AUTHOR]

The AIRWAYS-2 cluster randomised controlled trial compared the i-gel supraglottic airway device (SGA) with tracheal intubation (TI) as the first advanced airway management (AAM) strategy used by Emergency Medical Service clinicians (paramedics) treating adult patients with non-traumatic out-of-hospital cardiac arrest (OHCA). It showed no difference between the two groups in the primary outcome of modified Rankin Scale (mRS) score at 30 days/hospital discharge. This paper reports outcomes to 6 months. Paramedics from four ambulance services in England were randomised 1:1 to use an i-gel SGA (759 paramedics) or TI (764 paramedics) as their initial approach to AAM. Adults who had a non-traumatic OHCA and were attended by a participating paramedic were enrolled automatically under a waiver of consent. Survivors were invited to complete questionnaires at three and six months after OHCA. Outcomes were analysed using regression methods. 767/9296 (8.3%) enrolled patients survived to 30 days/hospital discharge and 317/767 survivors (41.3%) consented and were followed-up to six months. No significant differences were found between the two treatment groups in the primary outcome measure (mRS score: 3 months: odds ratio (OR) for good recovery (i-gel/TI, OR) 0.89, 95% CI 0.69–1.14; 6 months OR 0.91, 95% CI 0.71–1.16). EQ-5D-5L scores were also similar between groups and sensitivity analyses did not alter the findings. There were no statistically significant differences between the TI and i-gel groups at three and six months. We therefore conclude that the initially reported finding of no significant difference between groups at 30 days/hospital discharge was sustained when the period of follow-up was extended to six months. [ABSTRACT FROM AUTHOR]

• Developing a novel integrated medical facility location and allocation planning model. • Proposing a bi-objective model. • Providing an iteration method to obtain the Pareto front efficiently. • Numerically comparing our method and the binary search method and providing managerial insights. The planning of temporary medical service facility location and the distribution of casualties to these facilities require to consider casualty triage, health deterioration conditions, and equitable distribution of limited medical resources. The paper develops a bi-objective optimization model to determine the optimal temporary medical service locations and medical service allocation plan by maximizing the number of expected survivals and minimizing the total operational cost in the way of using ambulances and helicopters. Based on ∊ –constraint method, we propose an iteration method to obtain the Pareto front of the bi-objective optimization model. Sensitivity analyses are conducted to provide helpful managerial insights. [ABSTRACT FROM AUTHOR]

Abstract Background Aquatic activities provide physical and social benefits, while the risk of drowning generates countervailing social costs. Drawing on estimates of fatal drowning gathered by Royal Life Saving Society – Australia, this paper outlines a method for estimating the economic burden attributable to fatal drowning. Methods This study estimated the burden of fatal drowning by combining Value of a Statistical Life Year (VSLY), hospitalization, productivity and emergency services costs. All unintentional fatal drowning cases in Australia between 1-July-2002 and 30-June-2017 were included. Foregone life years from each drowning were estimated based on Australian life expectancies for the year of death. The societal value of these Years of Life Lost was calculated using the VSLY for Australia, adjusted to reflect income elasticity. Corrections to discounting of VSLY were applied. Estimates of productivity losses not captured in VSLY were produced using net national capital growth. Time spent in hospital was found using coronial data and existing estimates of search, ambulance and coronial costs were adapted and incorporated. Results The study covers 4285 cases of unintentional fatal drowning over 15 years. Based on this sample and estimates for the VSLY ($203,000), the economic burden of fatal drowning for Australia over this 14 year period was $18.63 billion in 2017 Australian dollars, averaging $1.24 billion annually. Conclusions Fatal drowning represents a significant source of health burden in Australia, underlining the need for further preventative measures. Practical applications We provide an easily-understood estimate of the scale of Australia's fatal drowning problem, permitting comparison with other social problems. They can also be used in determining net benefits of proposed drowning prevention policies and to identify situations where burden of fatal drowning is disproportionate. Suggestions for improving the calculation of societal burden of illness can be incorporated in cost-benefit analyses in related fields of study. Highlights • We provide a scale estimate of Australia's fatal drowning problem, allowing comparison with other social problems. • Burden estimates can be used in determining net benefits of proposed drowning prevention policies. • Estimates of burden by demographic group and location enable identification of disproportionate burdens of fatal drowning. • Improvements to the calculation of burden of illness can be incorporated in cost-benefit analyses in related fields. [ABSTRACT FROM AUTHOR]

When ambulances' turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g., the current coronavirus disease 2019 (COVID-19) pandemic. Taking this into consideration, this paper presents a first study on the COVID-19 impact on ambulances' TT by analyzing historical data from the Departmental Fire and Rescue Service of the Doubs (SDIS 25), in France, for three hospitals. Because the TTs of SDIS 25 ambulances increased, this paper also calculated and analyzed the number of breakdowns in services, which augmented due to shortage of ambulances that return on service in time. It is, therefore, vital to have a decision-support tool to better reallocate resources by knowing the time EMSs ambulances and personnel will be in use. Thus, this paper proposes a novel two-stage methodology based on machine learning (ML) models to forecast the TT of each ambulance in a given time and hospital. The first stage uses a multivariate model of regularly spaced time series to predict the average TT (AvTT) per hour, which considers temporal variables and external ones (e.g., COVID-19 statistics, weather data). The second stage utilizes a multivariate irregularly spaced time series model, which considers temporal variables of each ambulance departure, type of intervention, external variables, and the previously predicted AvTT as inputs. Four state-of-the-art ML models were considered in this paper, namely, Light Gradient Boosted Machine, Multilayer Perceptron, Long Short-Term Memory, and Prophet. As shown in the results, the proposed methodology provided remarkable results for practical purposes. The AvTT accuracies obtained for the three hospitals were 90.16%, 97.02%, and 93.09%. And the TT accuracies were 74.42%, 86.63%, and 76.67%, all with an error margin of ± 10 min. • Analysis of the ambulances' turnaround time for three hospitals from 2015–2020. • Breakage calculation in the fire department ambulance service during COVID-19. • Predicting the ambulances' average turnaround time in a given hour and hospital. • Predicting the turnaround time for each intervention and its ambulances. [ABSTRACT FROM AUTHOR]

• This paper firstly shares the ambulance situation in China. • Number of Ambulances in China increased greatly, especially for the negative pressure ambulance. • Negative pressure ambulances had positive influence on the contain of COVID-19. • Influence of negative pressure ambulance on containment of COVID-19 is discussed. • This article offers a valuable reference on ambulance now and in the future. New coronavirus (COVID-19) is a newly emerging and highly infectious form of typical pneumonia with a high rate of transmission. The COVID-19 pneumonia has spread to 147 countries and areas as of Mar.16th 2020, which has tremendous impact on the global public health. It is well known that, in China, the pandemic has been contained effectively with a series of emergency measures. It is necessary to share the existing experience of China in the fight against the pandemic to the world. Especially, during the process of prevention and containment of the pandemic, ambulances play an important role in transporting infectious patients. In this paper, focusing on the safety and quantity of negative pressure ambulances, the influence of ambulance on newly confirmed cases is given. Analysis of negative pressure ambulance on the containment of COVID-19 is shown. The potential development of ambulance is discussed. While the pandemic still continues spreading across the world, we hope to share our experience in the implementation of these strategies by China to save more life. [ABSTRACT FROM AUTHOR]

Background Patients arriving at the Emergency Department (ED) via ambulance can experience a delay in receiving definitive care. In Australia, this phenomenon is referred to as ‘Ambulance Ramping’, ‘Patient Off Stretcher Time Delay’ or ‘Offload Delay’. As a direct consequence of crowding, and in the context of a worldwide increase in ED and ambulance usage, hospital and ambulance service function is hampered. The aim of this review was to synthesize the literature with respect to the conceptualisation, meaning, antecedents and consequences of Ambulance Ramping. Methods This was a scoping review and synthesis of the literature. Six search terms were employed: emergency medical technician; paramedic; ambulance; hospital emergency services; delay; and ambulance ramping. Journal articles that discussed Ambulance Ramping (or similar terms), and were published in English between 1983 and March 2015 were included. PubMed and CINAHL Plus databases were searched, with secondary searches of reference lists and grey literature also undertaken. Results Thirteen papers were selected and inform this review. Several terms are used internationally to describe phenomena similar to Ambulance Ramping, where there is a delay in patient handover from paramedics to ED clinicians. Antecedents of Ambulance Ramping included reduction/limitation of ambulance diversion, patient acuity, the time of day, the day of the week, insufficient ED staff, insufficient ED beds, and high ED workload. Consequences of Ambulance Ramping include: further delays in patients’ ability to receive definitive care and workforce stressors such as missed meal breaks, sick leave and staff attrition. Conclusion While the existing research literature indicates that Ambulance Ramping is problematic, little is known about the patient’s experience of Ambulance Ramping; this is required so that an enhanced understanding of its implications, including those for emergency nurses, can be identified. [ABSTRACT FROM AUTHOR]

Objective: Dynamic ambulance redeployment policies tend to introduce much more flexibilities in improving ambulance resource allocation by capitalizing on the definite geospatial-temporal variations in ambulance demand patterns over the time-of-the-day and day-of-the-week effects. A novel modelling framework based on the Approximate Dynamic Programming (ADP) approach leveraging on a Discrete Events Simulation (DES) model for dynamic ambulance redeployment in Singapore is proposed in this paper.Methods: The study was based on the Singapore's national Emergency Medical Services (EMS) system. Based on a dataset comprising 216,973 valid incidents over a continuous two-years study period from 1 January 2011-31 December 2012, a DES model for the EMS system was developed. An ADP model based on linear value function approximations was then evaluated using the DES model via the temporal difference (TD) learning family of algorithms. The objective of the ADP model is to derive approximate optimal dynamic redeployment policies based on the primary outcome of ambulance coverage.Results: Considering an 8min response time threshold, an estimated 5% reduction in the proportion of calls that cannot be reached within the threshold (equivalent to approximately 8000 dispatches) was observed from the computational experiments. The study also revealed that the redeployment policies which are restricted within the same operational division could potentially result in a more promising response time performance. Furthermore, the best policy involved the combination of redeploying ambulances whenever they are released from service and that of relocating ambulances that are idle in bases.Conclusion: This study demonstrated the successful application of an approximate modelling framework based on ADP that leverages upon a detailed DES model of the Singapore's EMS system to generate approximate optimal dynamic redeployment plans. Various policies and scenarios relevant to the Singapore EMS system were evaluated. [ABSTRACT FROM AUTHOR]

Realistic mobility modeling is necessary for testing disaster management strategies as well as performance of disaster–resilient networks. Evacuation of the people from a disaster area depends on the environment and type of the hazard which cause certain changes in the pedestrian flows. Although most models focus on the building evacuations or city-scale evacuation planning, there is a need for a mobility model that captures the pedestrians’ movement behavior during evacuation from large and crowded disaster areas such as theme parks. In this paper, we propose a mobility model of the pedestrians in disaster areas. In our application scenario of theme parks, the main mission of the operators is the evacuation of the visitors and providing access to transportation vehicles such as ambulances. We use real maps to generate theme park models with obstacles, roads, and disaster events. We incorporate macro and micro mobility decisions of the visitors, considering their local knowledge and the social interactions among the visitors. We analyze the outcomes of the simulation of our theme park disaster (TP-D) mobility model with simulations of currently used models and real-world GPS traces. Moreover, using the proposed model as a baseline, we analyze the performance of an opportunistic network application. [ABSTRACT FROM AUTHOR]

The efficient management of ambulance routing for emergency requests is vital to save patients when a disaster response scenario occurs. In today’s road traffic, the transportation of patients from emergency points becomes more difficult in such tragic situation. We consider an urgent situation where a lot of hurts/patients require simultaneously an urgent medical care. Patients are either (1) slightly injured which can be assisted on the spot or (2) seriously injured which should be transferred to hospitals. In this paper, we aim to enhance the response-time performance of emergency medical service providers by handling the ambulance routing problem (ARP). The problem can be modeled as either the Open Vehicle Routing Problem (OVRP) or a Vehicle Routing Problem with Pickup and Delivery (VRPPD). We propose a cluster-first route-second algorithm based on the Petal algorithm and the particle swarm optimization (PSO) approach in order to handle efficiently the ARP. [ABSTRACT FROM AUTHOR]

Emergency Medical Services (EMS) is a demanding and hazardous industry. Because of the changing roles in the emergency response system, EMS workers are increasingly expected to provide treatment and care in addition to transport, which increases their task load and susceptibility to harm. This paper serves to outline the EMS field from the worker's perspective with the purpose of understanding their views on health, safety, and the work environment, and identify where gaps in worker well-being are exposed. Through direct observation, field studies, and formal interviews with EMS professionals, we discuss where reluctance lies in addressing safety issues and the current efforts to address them. A high prevalence of responses regarding the inadequacy of ambulance restraining systems was reported, as existing interventions do not take into account medic needs. [ABSTRACT FROM AUTHOR]

Research efforts on ambulance response times for Emergency Medical Services (EMS) calls have been made for decades, especially in developed countries, using different techniques and with varying objectives. In Brazil, a developing country, the scarce scientific production on this vital indicator prioritizes scenarios for EMS in cities with more than one million inhabitants. This shows the importance of extending research to the reality of small and medium-sized cities. This paper presents SAMU, the Brazilian EMS that follows the Franco-German emergency medicine model, compiling numbers related to service at the national level. The use of quantile regression allows the identification of the RT for the EMS and helps to explain the effects of factors at the system level, at the patient level, and specific factors on response time intervals of Southwest Paraná SAMU. This specific EMS, characterized as an inter-municipal consortium of prehospital services, is responsible for prehospital emergency care for an approximate population of 635,000 inhabitants in 42 small towns in the State of Paraná in southern Brazil. From the analysis of the records of 12,050 ambulance dispatches, it was possible to identify the average ambulance response time of 14 min and 25 s. The regression model was able to explain the influence of the independent variables at the system level (presumed severity of the emergency, ambulance dispatch time, and ambulance travel time), at the patient level (age, gender, and characteristic of the emergency) and specific variables of the emergency (day of the week and time of day) on the dependent variable response time over the quantiles, showing that the dispatch time, travel time, time of day, service to male patients and critical cases influence the ambulance response time. This work contributes to deepening the understanding of the management of EMS operations in a developing country, allows the comparison of the RT identified in relation to other countries, and identifies factors that impact the RT for other actors directly or indirectly involved. The practical implications are also presented, as well as how the study impacts the decision-making and management process of the EMS in the short, medium and long term. • In this study, the use of ambulance service was surveyed, and the RT was identified. • Using descriptive statistics, it was possible to determine the average RT of 14min 25s. • It also describe the EMS in terms of its structure, its internal and external processes and its technological limitations. • This work characterizes the Brazilian EMS, as well as the influences exerted by the Franco-German and Anglo-American models. [ABSTRACT FROM AUTHOR]

• Simulation optimization (SO) is used to improve the emergency healthcare chain performance. • The studied system includes an emergency medical service and an emergency department. • The proposed SO models simultaneously optimize resource planning and ambulance relocation. • The SO models optimize the weighted sum of multiple KPIs in a multiobjective approach. • The coordination between the different sub-systems improves the global performance. The aging population has a significant impact on the demand for emergency services worldwide. When the demand for these systems exceeds the available capacity, overcrowding occurs, leading to many negative consequences such as patient dissatisfaction, increased stress levels for caregivers, and financial loss for hospitals. Therefore, healthcare managers require decision support tools to rationalize their investment decisions and explore improvement opportunities throughout the emergency process. In this paper, a Discrete Event Simulation (DES) model is developed to capture the randomness and complexity of the entire emergency healthcare chain, including an Emergency Medical Service (EMS), an Emergency Department (ED), and an Observation Unit (OU). This tool was then embedded in a Simulation-Optimization (SO) approach that simultaneously optimizes resource planning decisions and ambulance relocation in the emergency process while exploring the potential benefits of coordination between different stakeholders in terms of joint decision making. Solutions of the SO approach were assessed by the DES model regarding a wide range of KPIs to support a multiobjective approach necessary to spotlight the decision maker choice. The results show that the coordination between the different sub-systems could remarkably improve the global performance of the system. [ABSTRACT FROM AUTHOR]

Aims: The current burden of cannabis-related presentations to emergency health services is largely unknown. This paper presents data collected over a 13-year period in metropolitan Melbourne, Australia as part of the Ambo Project, a unique surveillance system that analyses and codes paramedic records for drug-related trends and harms.Methods: Cannabis-related ambulance attendances involving 15-59 year olds in metropolitan Melbourne were analysed retrospectively from 2000 to 2013 (n=10,531). Trends and attendance characteristics were compared among cannabis only (CO)-, cannabis and alcohol (CA)- and cannabis with polydrug use (CP)-related attendances. Changes in alcohol and drug involvements in cannabis-related attendances were explored.Results: Rates of cannabis-related ambulance attendances increased significantly over the study period. Increasing rate of attendances per 100,000 population per year changed from 0.6 (2000-2010) to 5.5 (2010-2013). This sharp change was driven by CO- and CP-related attendances (rate of CA-related attendance increased steadily). The highest increasing rate (15.6) was for CO-related attendances among 15-29 years old males (2010-2013). Crystal methamphetamine became the most common illicit co-intoxicant amongst cannabis presentations in 2013.Conclusions: Relative to the total drug-related burden on ambulance services, cannabis-related presentations appear to be a small but significant and increasing problem. Significant changes in trends across other drug involvement and demographic subgroups suggest a possible shift in the cannabis using population and/or a change in using behaviours. Public health strategies should raise awareness of the increased risk posed by cannabis polydrug use and high attendance subpopulations should be determined. [ABSTRACT FROM AUTHOR]

The main objective of emergency medical assistance (EMA) services is to attend patients with sudden diseases at any possible location within an area of influence. This usually consists in providing “in situ” assistance and, if necessary, the transport of the patient to a medical center. The potential of such systems to reduce mortality is directly related to the travel times of ambulances to emergency patients. An efficient coordination of the ambulance fleet of an EMA service is crucial for reducing the average travel times. In this paper we propose mechanisms that dynamically improve the allocation of ambulances to patients as well as the redeployment of available ambulances in the region under consideration. We test these mechanisms in different experiments using historical data from the EMA service of the Autonomous Region of Madrid in Spain: SUMMA112 . The results empirically confirm that our proposal reduces the average response times of EMA services significantly. [ABSTRACT FROM AUTHOR]

Adequate spatial coverage by the emergency medical service and the ability to reach any location in the area of interest in the shortest possible time are crucial for the survival of patients with serious conditions. Knowledge of blind spots (i.e. sites that cannot be reached within the required time) represents key information for improving the service quality and may lead, e.g. to a relocation of bases or to other active interventions. Spatial coverage can be derived from experience based on historical data. Such an approach may be problematic if a larger area is being analysed, especially if data is not available for some parts of such areas or if no data is available. To eliminate such problems, we created a prediction model utilising the random forest ensemble learning method. The model is capable of predicting the travel time based on available historical data on ambulance movements (GPS) and the geometric and construction characteristics of individual road segments. We therefore collaborated with the regional public administration and emergency medical service authorities to deliver a time- and resource-efficient solution for emergency spatial planning practice. The outputs from the newly built model were subsequently validated against data from an empirical model currently used by the regional authorities. The results from both models were compared from the perspective of performance in various seasonal and time-of-day conditions. The prediction of travel times using the new model improved according to all the evaluated validation metrics. The importance and applicability of the foregoing model lies in the fact that it can be incorporated into the current emergency medical service management system in a simple manner in terms of data availability and the required computational resources. We conclude that the dynamic model presented in this paper represents an improvement relative to the reference data, and discuss the possibilities of further improving the proposed model. • Travel time coverage for emergency medical services was built and tested in a region-wide area. • Combining GPS data and road network attributes improved the travel time analysis. • The new dynamic model produced better results than the original reference model. [ABSTRACT FROM AUTHOR]

In the last decade, emergency department (ED) overcrowding has become a national crisis for the US healthcare system. Increasing mortality rates, decreasing quality of care, financial losses due to walkouts, and ambulance diversion are some of the consequences of ED overcrowding. Given the increasing demand in terms of ambulance utilization, being able to assign service requests to EDs efficiently, becomes a key function of the emergency medical services. This paper presents new ambulance allocation optimization models to reduce patients' total time to treatment, waiting times; therefore, ED overcrowding. Disparities and fairness are considered in the development of the mixed integer programming models. Under a set of assumptions, we apply our strategies to allocate 75 ambulance emergencies to 11 EDs in a specific county in Florida. Heterogeneous types of patients, demand characteristics, and geographical/facility information are considered in the models. Based on numerical experiments and the situation in Florida, we show that the optimization techniques can be utilized for large problems and result in up to 31% improvement of the current decentralized model. Further analysis reveals the negative or positive impact that the strategies have on each patient, giving new insights for future policy modifications. Bi-objective, single objective, and game theory optimization models are implemented in this study. • We study the national crisis of emergency department overcrowding. • We develop new strategies to allocate ambulance emergencies to reduce overcrowding. • We formulate considering fairness, system efficiency, and patient-level impacts. • We show that the minimization of system total time affects negatively on patients. • We show that one strategy has desired levels of efficiency and patient outcomes. [ABSTRACT FROM AUTHOR]

To achieve high performing emergency medical services (EMS), planning is of vital importance. EMS planners face several challenges when managing ambulance stations and the fleet of ambulances. In this paper, three strategic cases for EMS planners are presented together with potential solutions. In the first case, the effects of closing down a local emergency room (ER) are analyzed together with how adding an ambulance station and an ambulance to the area affected by the closing of the ER can be used to mitigate the negative consequences from the closing. The second case investigates a change in the organization of EMS. Currently, many non-urgent transport assignments are performed by ambulances which make them unavailable for more urgent calls. The potential for a more effective utilization of the ambulances is explored through transferring these assignments to designated transport vehicles. The third case is more technical and challenges the common practice regarding how time dependent demand is handled. Looking at the busiest hour or the average daily demand, is compared with taking time varying demand into account. The cases and solutions are studied using a recently developed strategic ambulance station location and ambulance allocation model for the Maximum Expected Performance Location Problem with Heterogeneous Regions (MEPLP-HR). The model has been extended to also include multiple time periods. This article demonstrates an innovative use of the model and how it can be applied to find and evaluate solutions to real cases within the field of strategic planning of EMS. The model is found to be a useful decision support tool when analyzing the cases and the expected performance of potential solutions. [ABSTRACT FROM AUTHOR]