Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation. (June 2018)

• Record Type: Journal Article
• Authors: van Barneveld, Thije; Jagtenberg, Caroline; Bhulai, Sandjai; van der Mei, Rob
• Is Part Of: Socio-economic planning sciences. Number 62(2018)
• Page Start: 129
• Page End: 142
• Publication Date: 2018-06
• Subjects: Ambulance redeployment -- Response times -- Workload -- Simulation; Planning -- Periodicals; Economic policy -- Periodicals; Social policy -- Periodicals; Planification -- Périodiques; Politique économique -- Périodiques; Politique sociale -- Périodiques; ECONOMIC PLANNING; SOCIAL PLANNING; DECISION-MAKING; 361
• DOI: 10.1016/j.seps.2017.11.001
• Languages: English
• ISSNs: 0038-0121

Abstract

Abstract: Providers of Emergency Medical Services (EMS) are typically concerned with keeping response times short. A powerful means to ensure this, is to dynamically redistribute the ambulances over the region, depending on the current state of the system. In this paper, we provide new insight into how to optimally (re)distribute ambulances. We study the impact of (1) the frequency of redeployment decision moments, (2) the inclusion of busy ambulances in the state description of the system, and (3) the performance criterion on the quality of the distribution strategy. In addition, we consider the influence of the EMS crew workload, such as (4) chain relocations and (5) time bounds, on the execution of an ambulance relocation. To this end, we use trace-driven simulations based on a real dataset from ambulance providers in the Netherlands. In doing so, we differentiate between rural and urban regions, which typically face different challenges when it comes to EMS. Our results show that: (1) taking the classical 0–1 performance criterion for assessing the fraction of late arrivals only differs slightly from related response time criteria for evaluating the performance as a function of the response time, (2) adding more relocation decision moments is highly beneficial, particularly for rural areas, (3) considering ambulances involved in dropping off patients available for newly coming incidents reduces relocation times only slightly, and (4) simulation experiments for assessing move-up policies are highly preferable to simple mathematical models. Highlights: We review two known methods to solve the ambulance relocation problem. We mix several features of these algorithms in order to study the performance of the resulting hybrid method. These features include the frequency of relocation decision moments and the performance criterion on the quality. Moreover, the effect of including busy ambulances in the state space description is tested. We use trace-driven simulations based on a real-life dataset of ambulance providers in the Netherlands to test the methods.