Coverage, survivability or response time: A comparative study of performance statistics used in ambulance location models via simulation–optimization

Zaffar, Muhammad Adeel; Rajagopalan, Hari; Saydam, Cem; Mayorga, Marı́a E.; Sharer, Elizabeth

doi:10.1016/j.orhc.2016.08.001

Cited by 35 publications

(25 citation statements)

References 41 publications

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“…The use of algorithms and simulations allow scenarios to be developed and evaluated. Hence, it is possible to make changes in the characteristics of the scenarios, such as resources, variables and planning horizons, for example [26][27][28] . Non-significant methods for Factors contributing to these figures concern the heavy traffic of vehicles on public roads those cities, which hinders the circulation of ambulances and is an impediment to achieving adequate response times as prescribed by the WHO.…”

Section: ■ Resultsmentioning

confidence: 99%

Response time in the emergency services. Systematic review

et al. 2018

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The growth of the urban population raises concern about municipal public managers in the sense of providing emergency medical services (EMS) that are aligned with the needs of prehospital emergency medical care demanded by the population. The literature review aims at presenting the response time of emergency medical services in several parts of the world and discussing some factors that interfere in the result of this indicator such as GDP (Gross Domestic Product) percentage spent on health and life expectancy of countries. The study will also show that in some of the consulted articles, authors suggest to EMS recommendations for decreasing the response time using simulations, heuristics and metaheuristics. Response time is a basic indicator of emergency medical services, in such a way that researchers use the descriptive statistics to evaluate this parameter. Europe and the USA outstand in the publication of studies that present this information. Some articles use stochastic and mathematical methods to suggest models that simulate scenarios of response time reduction and suggest such proposals to the local EMS. Countries in which the response time was identified have a high index of human development and life expectancy between 74.7 and 83.7 years.

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Section: ■ Resultsmentioning

confidence: 99%

Response time in the emergency services. Systematic review

et al. 2018

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“…We concentrate on two objectives that are supposed to mostly influence the outcomes of emergency medical services, particularly the maximum coverage and the minimum average response time. Previous studies [ 15 , 35 , 36 ] suggested that the maximum coverage objective itself does not perform well. The response time related objectives such as the average response time [ 35 , 36 ] and maximum survivability [ 15 ], result in a better system performance than the maximum coverage objective.…”

Section: Discussionmentioning

confidence: 99%

“…Zaffar et al [ 15 ] recently evaluated three different deployment strategies by a trace-driven simulation model using Mecklenburg County (US) EMS data. The simulation model is not very realistic since it uses constant values for ambulances’ speed, on-scene and drop-off times.…”

Section: Introductionmentioning

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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“…Recently published research [16] evaluates three different deployment strategies by a trace-driven simulation model using Mecklenburg County (US) EMS data. The simulation model is not very realistic since it uses constant values for ambulances' speed, on-scene and drop-off times.…”

Section: Relevant Literaturementioning

confidence: 99%

Coverage versus response time objectives in ambulance location

Jánošíková

Jankovič

Kvet

et al. 2021

Preprint

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Background: This paper deals with the location of emergency medical stations in a large-scale area. Operations research tools are used to design a new infrastructure that reflects demographic changes.Methods: A bi-criteria mathematical programming model is proposed. The criteria include the accessibility of high-priority patients in a short time limit and response time of all patients. The model is compared with the p-median model with a single response time objective and with a hierarchical pq-median model that considers different vehicle types. A detailed computer simulation model is used to evaluate the solutions.Results: The methodology is verified in the conditions of the Slovak Republic using real historical data on ambulance trips. Considerable improvements regarding the average response time, coverage of population and coverage of high-risk patients can be achieved by redistributing current stations. In a large-scale area, the coverage objective does not work well. It does not outperform the response time objective that enables saving more lives. The best results are achieved by the hierarchical median-type model.Conclusions: The resulting distribution of ambulances significantly improves the accessibility of urgent health care to patients.

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Coverage, survivability or response time: A comparative study of performance statistics used in ambulance location models via simulation–optimization

Cited by 35 publications

References 41 publications

Response time in the emergency services. Systematic review

Response time in the emergency services. Systematic review

Coverage versus response time objectives in ambulance location

Coverage versus response time objectives in ambulance location

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