Coverage versus response time objectives in ambulance location

Abstract

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.

Ambulance Locations in a Tiered Emergency Medical System in a City

“…Since this model also minimizes the average response time, our results are compatible with the cited works. The average response time seems to be the most important indicator of EMS access, especially from the most critical patients' point of view, because it strongly affects the probability of survival for those patients [17,29,33].…”

“…The MCLP model considers the expected number of patients. The expected numbers b j1 and b j2 of high-priority and low-priority patients, respectively, can be calculated using the age structure of the population in the city, and the emergency rates for the particular age categories [29].…”

Optimally Allocating Ambulances in Delhi using Mutation based Shuffled Frog Leaping Algorithm

“…In addition, the background of many articles has validated response time as a significant factor for gaining better insight into the operational performance of EMS. Some works have demonstrated that the patient's mortality and recovery rates are highly influenced by the response time [11]- [14]. Using the same objective in this paper, the authors have considered response time as the prime attribute for estimating and improving the medical service provided by EMS.…”

Elements of simulated annealing in Pareto front search

“…The fair objective function value f2(y) formulated by ( 3) expresses the number of users, whose time distance from the nearest located service facility exceeds given limit D. This objective follows the request of certain level of fairness in access to the provided service [5,18,22,23].…”

“…From the scientific point of view, the public service system design problem belongs to the family of discrete network location problems, which have been studied and effectively solved by many authors [3,7,8,9,11,23,27,28]. As far as a concrete form of the problem is concerned, one of the most commonly used modelling concepts follows the weighted p-median problem [3,4,14,17,18,19,20,21].…”