Modeling a pre-hospital emergency medical service using hybrid simulation and a machine learning approach

Olave-Rojas, David; Nickel, Stefan

doi:10.1016/j.simpat.2021.102302

Cited by 31 publications

(19 citation statements)

References 15 publications

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“…Our study is the first to compare different objectives for location of EMS stations in a large urban–rural area. This way it fills the gap in ambulance location literature since the research so far has been concentrating on urban rather than rural or mixed areas [ 34 ]. Modelling the EMS system for a heterogeneous urban–rural area is more challenging than it is for a homogeneous region.…”

Section: Discussionmentioning

confidence: 99%

Coverage versus response time objectives in ambulance location

et al. 2021

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Background This paper deals with the location of emergency medical stations where ambulances waiting to be dispatched are parked. The literature reports a lot of mathematical programming models used to optimize station locations. Most studies evaluate the models only analytically applying the same simplifying assumptions that were used in the modelling phase. In addition, they concentrate on systems operating one type of emergency units in homogeneous urban areas. The goal of our study is to identify which optimization criterion the emergency medical service (EMS) outcomes benefit from the most and which model should be used to design tiered systems in large urban–rural areas. Methods A bi-criteria mathematical programming model is proposed. The criteria include the accessibility of high-priority patients within a short time limit and average response time to all patients. This model is being compared to the p-median model with a single response time objective and to a hierarchical pq-median model that considers two different vehicle types. A detailed computer simulation model is used to evaluate the solutions. The methodology is verified in the conditions of the Slovak Republic using real historical data on 149,474 ambulance trips performed in 2015. Results All mathematical models improve EMS performance by relocating some stations compared to the current distribution. The best results are achieved by the hierarchical median-type model. The average response time is reduced by 58 s, the number of calls responded to within 15 min is increased by 5% and the number of high-priority calls responded to within 8 min by 6%. Conclusions The EMS systems operating in heterogeneous areas should be designed to minimize response times, and not to maximize the number of calls served within a given time limit.

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Section: Discussionmentioning

confidence: 99%

Coverage versus response time objectives in ambulance location

et al. 2021

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“…Additionally, in recent developments, other objectives have been proposed, such as the survival functions (Erkut et al [49]) or the concept of envy (Chanta et al [87]). Another common objective used in these kinds of problems is response time, which considers the time elapsed since the call is received until the arrival of the ambulance to the emergency site [82,91,117,142].…”

Section: Optimization Objectivesmentioning

confidence: 99%

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

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In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

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“…In the field of spatial analysis, geospatial interpolation and regression tools have been used with a focus on the spatial autocorrelation of urban spatial data [19][20][21][22]. In addition, a study [23] was conducted to analyze the spatiotemporal accessibility of emergency services.…”

Section: Travel Time Prediction Of Emergency Servicesmentioning

confidence: 99%

“…Deterministic or Stochastic Travel Time [7][8][9][10][11] Parametric or Nonparametric Methods [12][13][14][15][16][17][18] Geospatial Analysis [19][20][21][22][23] Table 1. Previous Studies on the Travel Time of Emergency Services…”

Section: Approach Previous Studiesmentioning

confidence: 99%

Prediction of Travel Time of Fire Service using Kriging and Weighted-sum Technique

2022

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The accessibility of city fire services is an important indicator for evaluating fire services and optimizing fire resource allocation. For firefighting, rescue, and first-aid activities, it is recommended that the time from fire vehicles leaving the garage to arrive at the scene is less than 5 minutes. Therefore, the travel time of fire services is one of the main concerns for many researchers. This study assumes that changing the urban environment affects the travel time of fire services. Therefore, in this study, weights were applied over the years to predict the travel time of fire service by using the kriging technique. As a result of the case study, temporal factors (elapsed year, term of travel time, and time spent) did not significantly affect travel time prediction accuracy using the kriging technique. As observed in previous studies, it is confirmed that the prediction accuracy is high because it is less affected by traffic-related factors at short travel distances. The results of this study contribute to the development of spatial analysis techniques to improve the accuracy of travel-time prediction.

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Modeling a pre-hospital emergency medical service using hybrid simulation and a machine learning approach

Cited by 31 publications

References 15 publications

Coverage versus response time objectives in ambulance location

Coverage versus response time objectives in ambulance location

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Prediction of Travel Time of Fire Service using Kriging and Weighted-sum Technique

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