Emergency Medical Service Location Research Articles

Decision support framework for tactical emergency medical service location planning

A well-designed emergency medical services (EMS) system is essential to provide the best possible quality of care to patients in emergencies. Exogenous effects, such as demographic change or extreme weather conditions, lead to a continuous increase in emergency calls. On the other hand, emergency vehicles and qualified personnel are limited. Consequently, there is a continuous need to improve the existing EMS systems in order to optimize the use of the available resources. To assist decision makers in this complex task, we provide a decision support framework at the tactical site planning level. First, we analyze the given historical data to find suitable periods that have similar structures in terms of the number of calls and the time of day. We follow this approach to account for fluctuating demand and time-dependent travel times. A new stochastic, time-dependent, mixed-integer extension of the maximum expected coverage location problem (MEXCLP) is developed that accounts for ambulance site interdependencies. We conduct computational experiments for an existing EMS network structure to optimally locate sites and allocate ambulances. Furthermore, we analyze the benefit of additional ambulances and flexible waiting sites. A discrete-event simulation (DES) is used to evaluate the model solutions and to provide further insights into the trade-off between quality and cost of care for the given model solutions. Results based on anonymized real-world EMS data indicate that flexible waiting sites provide considerable potential savings in the use of ambulances while maintaining a high level of coverage.

Directed Search for Non-Dominated Emergency Medical System Designs

This research paper is aimed at a specific group of emergency medical service location problems, which are solved to save people’s lives and reduce the rate of mortality and morbidity. Since searching for the optimal service center deployment is a big challenge, many operations researchers, programmers, and healthcare practitioners have been making a great effort to find effective solutions since the 1960s. Within this paper, we study such a system design problem in which two contradictory objectives are taken into account. Since the optimization of one criterion causes deterioration in the value of the other, a specific small finite set of solutions seems to be a sufficient output of the associated solving process for further decision-making. Therefore, we study here several heuristic approaches that enable us to approximate the original Pareto fronts of non-dominated system designs. In addition to the theoretical explanation, we provide the readers with the results of numerical experiments in order to evaluate the quality of the proposed algorithms. Based on the presented results, it can be stated that the suggested approach is able to produce a good approximation of the Pareto front of emergency medical service system designs in acceptable computational time, which is in orders shorter than the one required by the former exact method.

A multi-period emergency medical service location problem based on Wasserstein-metric approach using generalised benders decomposition method

This paper considers a multi-period location and sizing problem for an emergency medical service (EMS) system based on a distributionally robust optimisation (DRO) chance-constrained programming approach. The dynamic uncertain emergency medical requests are described in the ambiguity set, which is constructed based on Wasserstein-metric. The model of this problem focuses on minimising long-term operation costs. The chance constraints ensure the reliability of EMS system for the entire geographic areas. A reformulation of chance constraints is provided in Mixed Integer Linear Program form. For problem solution, a generalised Benders decomposition (GBD) implementation is proposed. A numerical simulation is conducted to illustrate the performance of two solution approaches in terms of computational convergence speed and optimality of the problem.

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location.

To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.

Emergency medical service location problem based on physical bounds using chance-constrained programming approach

This paper studies a location and sizing problem for an emergency medical service system using distributionally robust chance-constrained programming approach. The medical demands are uncertain and described in an ambiguity set, which is constructed based on Wasserstein-metric. The location problem is modelled through minimising the total expected costs, including construction costs of facilities, purchase of ambulances and maintenance costs. The chance constraints are introduced to guarantee the service level of geographical areas. For the chance constraints, three reformulations such as CVaR-approximation, the mixed integer linear programme exact reformulation and the physically bounded bilinear reformulation are studied. A numerical study is conducted to illustrate the computational performance and objective values.

Fairness or efficiency-Managing this conflict in emergency medical services location planning

Every patient should have equal access to fast and qualified assistance by emergency medical services (EMS). Most location planning models for EMS only consider efficiency criteria such as maximum coverage. Especially in metropolitan regions, the heterogeneous distribution of demand leads to unequal service availability: city centers receive higher coverage than suburban areas. To address this issue, we extend existing location planning models by two fairness criteria: the Rawlsian criterion, which maximizes the coverage of the least-covered demand area, and the Gini coefficient, which minimizes differences in coverage between demand areas. We consider both fairness criteria using the ϵ-constraint method and apply both criteria to the case of a German EMS provider. By generating the Pareto front, we analyze the trade-offs between efficiency and fairness. We find that managing the conflict between fairness and efficiency especially in a metropolitan region offers more beneficial solutions with respect to both criteria than generally assumed. We show that it is possible to greatly improve fairness by giving up only a small amount of coverage.

Probabilistic Envelope Constrained Multiperiod Stochastic Emergency Medical Services Location Model and Decomposition Scheme

This paper considers a multiperiod emergency medical services (EMS) location problem and introduces two two-stage stochastic programming formulations that account for uncertainty about emergency demand. Whereas the first model considers both a constraint on the probability of covering the realized emergency demand and minimizing the expected cost of doing so, the second one employs probabilistic envelope constraints that allow us to control the degradation of coverage under the more severe scenarios. These models give rise to large mixed-integer programs, which can be tackled directly or by using a conservative approximation scheme. For the former, we implement the branch-and-Benders-cut method, which improves significantly the solution time when compared with using both a recently proposed state-of-the art branch-and-bound algorithm and the CPLEX solver. Finally, a practical study is conducted using historical data from the Northern Ireland Ambulance Service and sheds some light on optimal EMS location configuration for this region and on necessary trade-offs that must be made between emergency demand coverage and expected cost. These insights are confirmed through an out-of-sample performance analysis.

Combining spatial information and optimization for locating emergency medical service stations: A case study for Lower Austria

ObjectivesEmergency medical services have been established in many countries all over the world. Good first care improves the outcome of patients in terms of hospital stay duration, chances of full recovery and of treatment costs. In this paper, we present an integrated approach combining spatial information and integer optimization for emergency medical service location planning. The research is motivated by a recent call for bids to restructure the location of emergency medical services in the Austrian federal state of Lower Austria by the local state government. MethodsOur framework allows for constraints on the places where an emergency care physician is stationed, accounting for the fact that – for economical reasons – it might not be feasible to arbitrarily place emergency care physicians. We use maximum coverage linear programs to get accurate solutions for the problem instances (depending on the maximum allowed number of emergency care physicians and the constraints of their placement). We optimize for the maximum number of covered residents given certain parameters. The travelling distances are calculated by means of a digital road graph. Moreover we analyze the coverage of the day population as there are significant shifts in the number of persons present at daytime. For every problem instance we have calculated the ten best solutions and examined the variance among them. For the demand point aggregation we have used a cell grid. ResultsUsing our method we can show that with less emergency care physicians more residents can be covered. This is highly applicable to low populated areas where the coverage becomes better. There is little variance from the best to the second best solution: There are only small changes (usually only one cell is shifted) between the best and the second best solution. The coverage of the day population – except for a few problem instances – is always better than the coverage of the residents (reflecting the fact that many residents commute to more densely populated areas). ConclusionsIn our study, we show that our solutions provide better coverage of residents with fewer emergency care physicians than the current status quo.

Estimating the performance of emergency medical service location models via discrete event simulation

We address the problem of evaluating deterministic EMS (Emergency Medical Service) location models via a simulation approach. For deterministic set covering location models, the performance of the model is typically determined by an objective function representing a certain type of coverage. After determining the location of EMS stations by deterministic models, we propose to conduct a simulation analysis to evaluate the performance by estimating the “real” coverage of the population that takes into consideration the unavailability of the busy ambulances. By using optimization tools, we find the location of ambulances for each model by solving the mathematical models and then we simulate each setting for two different policies under the same parameters. The overall performance of the models is firstly tested on Istanbul data and then on randomly generated data with different problem size, different layout and different arrival rates. We demonstrate that the performance estimated by the simulation based assessment can vary with respect to the problem characteristics and that the performance of a seemingly good deterministic model may not be that good as measured by the simulation approach.

Application Of Imperialist Competitive Algorithm To The Emergency Medical Services Location Problem

Minimization of arrival time at scenes plays an essential role to help injured people in emergency events. This can be undertaken through mathematical programming models, called emergency medical services location problem, and solved by conventional exact algorithms or by recent meta-heuristic methods as well. Meta-heuristic algorithms have recently been realized to be more efficient in the sense of computing times especially in large-scale cases. The emergency medical services location problem would be further complicated when the number of stations and/or emergency vehicles, as an important indicator of system costs, should be determined at the same time. In this paper, a newly introduced optimization method, Imperialist Competitive Algorithm (ICA), is used to solve the EMS location problem. The ICA mimics the human's socio-political evolution to solve continuous problems. In this paper, a discrete version of the ICA is sought to be adapted to solve the EMS location problem. The adapted ICA algorithm is then applied on two benchmark problems with four different demand scenarios as well as on the real transportation network of Mashhad City. Results of this algorithm are compared with those of other wellknow meta-heuristic algorithms (i.e. the genetic algorithm, the simulated annealing and the particle swarm optimization). These results indicate that the cpu time of the ICA is averagely less than that obtained from the other algorithms, and the number of required ambulances is not considerably different.

Emergency Medical Service Location Problem

This paper suggests emergency medical service vehicle (ambulance) algorithm when the emergency patient occurs in order to be sufficient the maximum permission time T of arrival about all sectors in one city that is divided in the various areas. This problem cannot be solved in polynomial times. One can obtains the solution using the integer programming. In this paper we suggest vertex set (or dominating set) algorithm and easily decide the location of ambulances. The core of the algorithm decides the location of ambulance is to the maximum degree vertex among the neighborhood of minimum degree vertex. For the 33 sectors Ostin city in Texas, we apply minutes. The traditional set cover algorithm with integer programming cannot obtains the solution in several T in 18 cases. But, this algorithm obtains solution for all of the 18 cases.

Integrating expected coverage and local reliability for emergency medical services location problems

Daskin's MEXCLP model [Daskin M. A maximum expected covering location model: formulation, properties, and heuristic solution. Transportation Science 1983;17:48–70] was one of the first efforts to capture the stochastic nature of emergency medical services (EMS) location problems within a mixed-integer formulation. With their subsequent introduction of MALP, ReVelle and Hogan [The maximum availability location problem. Transportation Science 1989;23:192–200] offered two key advances, local vehicle busyness estimates and the α-reliability objective. While these constructs have influenced many subsequent EMS location models, they have been subjected to relatively little empirical analysis. To address this, we introduce the LR-MEXCLP, a hybrid model combining the local busyness estimates of MALP with the maximum coverage objective of MEXCLP. We then solve a series of problems with all three models and employ simulation to estimate aggregate service levels. We find that LR-MEXCLP leads to modest but consistent service gains over both MALP and MEXCLP. These results support the merits of local busyness estimates, but they also suggest that the α-reliability objective may be inappropriate when seeking to maximize aggregate system response capabilities. More generally, our research underscores the utility of (a) linking modeling assumptions and goals with real-world application contexts, and (b) employing simulation or other techniques to validate theoretical results.

Integrated Approach for Emergency Medical Service Location and Assignment Problem

In an emergency medical service (EMS) system, the depot location and fleet assignment greatly affect the average response time, which is the main criterion for measuring system performance. Whereas the EMS depot location problem is a strategic problem, the fleet assignment problem is a tactical one. As such, the EMS depot location and fleet assignment problems are usually solved separately under some simplified assumptions. However, there is a potential for savings in both the average response times and the capital and operating costs if these problems can be solved simultaneously. A simulation model for EMS vehicle dispatching was developed. This model is calibrated with real-world data, and it is incorporated in a genetic algorithm to help solve the EMS depot location and fleet assignment problems simultaneously. Emergency types, their response priorities, and whether or not they require dispatching of multiple units are taken into consideration in the model. The average response time and the capital and operating costs are used as criteria for evaluation.