Joint Location and Dispatching Decisions for Emergency Medical Service Systems

Emergency Medical Service (EMS) systems are a service that provides acute care and transportation to a place for definitive care, to people experiencing a medical emergency. The ultimate goal of EMS systems is to save lives. The ability of EMS systems to do this effectively is impacted by several resource allocation decisions including location of servers (ambulances), districting of demand zones and dispatching rules for the servers. The location decision is strategic while the dispatching decision is operational. Those two decisions are usually made separately although both affect typical EMS performance measures. The service from an ambulance is usually time sensitive (patients generally want the ambulances to be available as soon as possible), and the demand for service is stochastic. Regulators also impose availability constraints, the most generally accepted being that 90\% of high priority calls (such as those related to cardiac arrest events) should be attended to within 8 minutes and 59 seconds. In the case of minimizing the mean response time as the only objective, previous works have shown that there are cases in which it might not be optimal to send the closest available server to achieve the minimum overall response time. Some researchers have proposed integrated models in which the two decisions are made sequentially. The main contribution of this work is precisely in developing the integration of location and dispatching decisions made simultaneously. Combining those decisions leads to complex optimization models in which even the formulation is not straightforward. In addition, given the stochastic nature of the EMS systems the models need to have a way to represent their probabilistic nature. Several researchers agree that the use of queuing theory elements in combination with location, districting and dispatching models is the best way to represent EMS systems. Often heuristic/approximate solution procedures have been proposed and used since the use of exact methods is only suitable for small instances. Performance indicators other than Response Time can be affected negatively when the dispatching rule is sending the closest server. For instance, there are previous works claiming that when the workload of the servers is taken into account, the nearest dispatching policy can cause workload imbalances. Therefore, researchers mentioned as a potential research direction to develop solution approaches in which location, districting and dispatching could be handled in parallel, due to the effect that all those decisions have on key performance measures for an EMS system. In this work the aim is precisely the development of an optimization framework for the joint problem of location and dispatching in the context of EMS systems. The optimization framework is based on meta heuristics. Fairness performance indicators are also considered, taking into account different points of view about the system, in addition to the standard efficiency criteria. Initially we cover general aspects related to EMS systems, including an overall description of main characteristics being modeled as well as an initial overview of related literature. We also include an overall description and literature review with focus on solution methodologies for real instances, of two related problems: the $p$-median problem and the maximal covering location problem (MCLP). Those two problems provide much of the basic structure upon which the main mathematical model integrating location and dispatching decisions is built later. Next we introduce the mathematical model (mixed-integer non-linear problem) which has embedded a queuing component describing the service nature of the system. Given the nature of the resulting model it was necessary to develop a solution algorithm. It was done based on Genetic Algorithms. We have found no benefit on using the joint approach regarding mean Response Time minimization or Expected Coverage maximization. We concluded that minimizing Response Time is a better approach than maximizing Expected Coverage, in terms of the trade-off between those two criteria. Once the optimization framework was developed we introduced fairness ideas to the location/allocation of servers for EMS systems. Unlike the case of Response Time, we found that the joint approach finds better solutions for the fairness criteria, both from the point of view of internal and external costumers. The importance of that result lies in the fact that people not only expect the service from ambulances to be quick, but also expect it to be fair, at least in the sense that any costumer in the system should have the same chances of receiving quick attention. From the point of view of service providers, balancing ambulance workloads is also desirable. Equity and efficiency criteria are often in conflict with each other, hence analyzing trade-offs is a first step to attempt balancing different points of view from different stakeholders. The initial modeling and solution approach solve the problem by using a heuristic method for the overall location/allocation decisions and an exact solution to the embedded queuing model. The problem of such an approach is that the embedded queuing model increases its size exponentially with relation to the number of ambulances in the system. Thus the approach is not practical for large scale real systems, say having 10+ ambulances. Therefore we addressed the scalability problem by introducing approximation procedures to solve the embedded queuing model. The approximation procedures are faster than the exact solution method for the embedded sub-problem. Previous works mentioned that the approximated solutions are only marginally apart from the exact solution (1 to 2\%). The mathematical model also changed allowing for several ambulances to be assigned to a single station, which is a typical characteristic of real world large scale EMS systems. To be able to solve bigger instances we also changed the solution procedure, using a Tabu Search based algorithm, with random initialization and dynamic size of the tabu list. The conclusions in terms of benefits of the joint approach are true for bigger systems, i.e. the joint approach allows for finding the best solutions from the point of view of several fairness criteria

Comparison of Emergency Medical Services Delivery Performance using Maximal Covering Location and Gradual Cover Location Problems

Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered

Efficiency and fairness in ambulance planning

Mei, R.D. van der [Promotor]Bhulai, S. [Promotor

Testing Prospects for Reliable Diatom Nanotechnology in Microgravity

The worldwide effort to grow nanotechnology, rather than use lithography, focuses on diatoms, single cell eukaryotic algae with ornate silica shells, which can be replaced by oxides and ceramics, or reduced to elemental silicon, to create complex nanostructures with compositions of industrial and electronics importance. Diatoms produce an enormous variety of structures, some of which are microtubule dependent and perhaps sensitive to microgravity. The NASA Single Loop for Cell Culture (SLCC) for culturing and observing microorganisms permits inexpensive, low labor in-space experiments. We propose to send up to the International Space Station diatom cultures of the three diatom species whose genomes are being sequenced, plus the giant diatoms of Antarctica (up to 2 mm diameter for a single cell) and the unique colonial diatom, Bacillaria paradoxa. Bacillaria cells move against each other in partial synchrony, like a sliding deck of cards, by a microfluidics mechanism. Will normal diatoms have aberrant pattern and shape or motility compared to ground controls? The generation time is typically one day, so that many generations may be examined from one flight. Rapid, directed evolution may be possible running the SLCC as a compustat. The shell shapes and patterns are preserved in hard silica, so that the progress of normal and aberrant morphogenesis may be followed by drying samples on a moving filter paper "diatom tape recorder". With a biodiversity of 100,000 distinct species, diatom nanotechnology may offer a compact and portable nanotechnology toolkit for exploration anywhere

The Minimum Expected Penalty Relocation Problem for the computation of compliance tables for ambulance vehicles

We study the ambulance relocation problem in which one tries to respond to possible future incidents quickly. For this purpose, we consider compliance table policies: a relocation strategy commonly used in practice. Each compliance table level indicates the desired waiting site locations for the available ambulances. To compute efficient compliance tables, we introduce the minimum expected penalty relocation problem (MEXPREP), which we formulate as an integer linear program. In this problem, one has the ability to control the number of waiting site relocations. Moreover, different performance measures related to response times, such as survival probabilities, can be incorporated. We show by simulation that the MEXPREP compliance tables outperform both the static policy and compliance tables obtained by the maximal expected coverage relocation problem (MECRP), which both serve as benchmarks. Besides, we perform a study on different relocation thresholds and on two different methods to assign available ambulances to desired waiting sites

Emergency medical services delivery performance based on real map

Performance of emergency medical services delivery is normally benchmarked via ambulance response time. Quick ambulance response can efficiently reduce the disability and mortality of emergency patients. Ambulance dispatch policy and location model both have a significant impact on the response time. A proper dispatch policy can determine the right ambulance for the incoming emergency call. Meanwhile, the use of location model can increase the ambulance coverage. Nevertheless, the applications of both dispatch policy and location model are yet to be seen in Malaysia ambulance services. There is also a lack of academic contributions focusing on a simulation study of emergency medical services in Malaysia, especially those using local geographic information. In this research, a simulation framework is presented to study the response time performance of a simulation model that consists of both the dispatch policy and location model. Several real-life dispatch policies were simulated in a real map by using actual geographic information to evaluate the efficiency of ambulance services in Johor Bahru. By using a suitable dispatch policy, the simulation results show an improvement in average response time for higher-priority call while the total covered calls have increased significantly with the application of maximal covering location problem. At nine ambulances, the achieved maximum coverage is 68% for using the location model compared to merely 45% prior to the implementation

Ambulance location for maximum survival

This article proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an emergency medical service (EMS) vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures—the expected number of survivors in case of cardiac arrests. The survival-maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival-maximizing models using data from the Edmonton EMS system.NSERCpre-prin

MODELING AND SIMULATION OF NOVEL MEDICAL RESPONSE SYSTEMS FOR OUT-OF-HOSPITAL CARDIAC ARREST

Sudden Cardiac Arrest (SCA) is the leading cause of death in the United States, resulting in 350,000 deaths annually. SCA survival requires immediate medical treatment with a defibrillatory shock and cardiopulmonary resuscitation. The fatality rate for out-of-hospital cardiac arrest is 90%, due in part to the reliance on Emergency Medical Services (EMS) to provide treatment. A substantial improvement in survival could be realized by applying early defibrillation to cardiac arrest victims. Automated External Defibrillators (AEDs) allow lay rescuers to provide early defibrillation, before the arrival of EMS. However, very few out-of-hospital cardiac arrests are currently treated with AEDs. Novel response concepts are being explored to reduce the time to defibrillation. These concepts include mobile citizen responders dispatched by a cell phone app to nearby cardiac arrest locations, and the use of drones to deliver AEDs to a cardiac arrest scene. A small number of pilot studies of these systems are currently in progress, however, the effectiveness of these systems remains largely unknown. This research presents a modeling and simulation approach to predict the effectiveness of various response concepts, with comparison to the existing standard of EMS response. The model uses a geospatial Monte Carlo sampling approach to simulate the random locations of a cardiac arrest within a geographical region, as well as both random and fixed origin locations of responding agents. The model predicts response time of EMS, mobile dispatched responders, or drone AED delivery, based on the distance travelled and the mode of transit, while accounting for additional system factors such as dispatch time, availability of equipment, and the reliability of the responders. Response times are translated to a likelihood of survival for each simulated case using a logistic regression model. Sensitivity analysis and response surface designed experiments were performed to characterize the important factors for response time predictions. Simulations of multiple types of systems in an example region are used to compare potential survival improvements. Finally, a cost analysis of the different systems is presented along with a decision analysis approach, which demonstrates how the method can be applied based on the needs and budgets of a municipality