In life-threatening emergency situations, the ability of emergency medical service (EMS) providers to arrive at the emergency scene within a few minutes may make the difference between survival or death. To realize such extremely short response times at affordable cost, efficient planning of EMS systems is crucial. In this article we will discuss the Testing Interface For Ambulance Research (TIFAR) simulation tool that can be used by EMS managers and researchers to evaluate the effectiveness of different dispatch strategies. The accuracy of TIFAR is assessed by comparing the TIFAR-based performance indicators against a real EMS system in the Netherlands. The results show that TIFAR performs extremely well.

Governments deal with increasing health care demand and costs, while budgets are tightened. At the same time, ambulance providers are expected to deliver high-quality service at affordable cost. Maximum reliability and minimal availability models guarantee a minimal performance level at each demand point, in contrast to the majority of facility location and allocation methods that guarantee a minimal performance that is aggregated over the entire ambulance region. As a consequence, existing models generally lead to overstaffing, particularly in 'mixed' regions with both urban and rural areas, which leads to unnecessarily high costs. This paper addresses this problem. First, we introduce the concept of demand projection to give fundamental insight into why this overstaffing takes place. Next, we overcome the overstaffing by the so-called adjusted queuing (AQ) solution that provides generalizations of the existing models. We provide mathematical proofs for the correctness of the AQ solution. Finally, to assess the performance of the AQsolution we have performed extensive numerical experimentation, using real data from four ambulance regions in the Netherlands. The results show that in all cases the AQ-solution indeed leads to better ambulance care than the existing solutions, while reducing staffing cost.

In pre-hospital health care the call center plays an important role in the coordination of emergency medical services (EMS). An EMS call center handles inbound requests for EMS and dispatches an ambulance if necessary. The time needed for triage and dispatch is part of the total response time to the request, which, in turn, is an indicator for the quality of EMS. Calls entering an efficient EMS call center must have short waiting times, centralists should perform the triage efficiently and the dispatch of ambulances must be adequate and swift. This paper presents a detailed discrete event simulation model for EMS call centers. The model provides insight into the EMS call center processes and can be used to address strategic issues, such as capacity and workforce planning. We analyse results of the model that are based on real EMS call center data to illustrate the usefulness of the model.

In pre-hospital health care the call center plays an important role in the coordination of emergency medical services (EMS). An EMS call center handles inbound requests for EMS and dispatches an ambulance if necessary. The time needed for triage and dispatch is part of the total response time to the request, which, in turn, is a key performance indicator for the quality of EMS. Call center agents should perform the triage efficiently, so that entering calls have short waiting times, and the dispatch of ambulances must be adequate and swift to get a fast EMS response. This paper presents and compares three discrete event simulation models for EMS call centers: the first has two different call center agent classes between whom communication tasks are split, while the second has one class of call center agents that share all tasks. The third model is a combination of both. The models provide new insight into the EMS call center processes and can be used to address strategic issues, such as capacity and workforce planning. The analysis and simulations of urgent communication and decision processes in this paper are valuable to other emergency call centers 1 .

Abstract. In life-threatening emergency situations in which every second counts, the timely arrival of an ambulance can make the difference between survival and death. In practice, the response-time targets, defined as the maximum time between the moment an incoming emergency call is received the moment when onsite medical aid is provided, are often not met. A promising means to reduce late arrivals by ambulances is to proactively relocate ambulances to ensure good coverage by the available ambulances in real time. This paper evaluates two dynamic relocation policies that an ambulance service provider in the Netherlands modified for operational use and implemented in a software tool for realtime decision support. The policies were used in a pilot program within a dispatch center for 12 weeks. Based on the success of this pilot, our policies were adopted for ongoing use and permanent implementation. This paper describes the relocation methods, evaluates the pilot, provides statistics for efficiency improvements, and discusses the experiences of ambulance dispatchers and management.History: This paper was refereed. Funding: This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organization for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs. Keywords: ambulance • EMS • relocation • DAM • dynamic • management • operationsAmbulance service providers (ASPs) worldwide must implement policies to improve efficiency, such as budget cuts or performance improvement programs. They can obtain efficiencies via changes to medical equipment, staff training, and the logistic domain. In this paper, we focus on the latter. The goal is to allocate the "right resources at the right time at the right place," such that the probability of meeting their response-time targets, within given budget constraints, is optimized. The ambulance service provisioning process has several stages. When an emergency occurs, a dispatch center receives an emergency call, typically a 911 or 112 call (Stage 1). During this stage, an agent at the dispatch center performs triage (i.e., asks the caller a set of questions to assess the severity of the emergency). If the incident requires ambulance service, the agent immediately dispatches an ambulance-usually the closest available ambulance-to the scene of the emergency (Stage 2). The target response time, defined as the elapsed time between the moment that a call comes in and the moment that the ambulance arrives at the emergency scene, is country specific; in the Netherlands, the response-time target for highemergency calls is 15 minutes. After performing onsite medical treatment (Stage 3), the emergency personnel on the ambulance may transfer the patient to a hospital (Stage 4). Upon completion of the patient transfer, the ambulance is available for handling the next emergency.The traditional ambulance service provisioning paradigm is static and reactive. That is, each ambulance has a fixed base location (also referred ...