DISTRICTING AND DISPATCHING POLICIES TO IMPROVE THE EFFICIENCY OF EMERGENCY MEDICAL SERVICE (EMS) SYSTEMS

The major focus of Emergency Medical Service (EMS) systems is to save lives and to minimize the effects of emergency health incidents. The efficiency of the EMS systems is a major public concern. Thus, over the past three decades a significant amount of research studies have been conducted to improve the performance of EMS systems. The purpose of this study is also to improve the performance of EMS system. The contribution of this research towards improving the performance of EMS systems is twofold. One area is to implement optimal or near optimal dispatching strategies for EMS systems and the other is to determine the response boundaries for EMS vehicles. Proposed dispatching strategies are implemented incorporating the degree of the urgency of the call. A Markov decision process (MDP) model is developed to obtain optimal dispatching strategies in less complex models. A heuristic algorithm is proposed to dispatch ambulances for more complex models. In this study, an integer programming formulation and a constructive heuristic are proposed to determine response areas or districts for each ambulance. Additionally, dispatching rules to dispatch paramedic units within districts and out of districts are examined. Simulation is used to evaluate the performance of the EMS system after introducing proposed dispatching policies. Performance is measured in terms of patients\u27 survival probability rather than measuring the response time thresholds, since survival probability reflects the patients\u27 outcome directly. Results are illustrated using real-data collected from Hanover county Virginia. Results show that proposed dispatching rules are valuable in increasing patients\u27 survivabilit

STRATEGIES TO IMPROVE THE EFFICIENCY OF EMERGENCY MEDICAL SERVICE (EMS) SYSTEMS UNDER MORE REALISTIC CONDITIONS

Emergency medical service (EMS) systems provide medical care to pre-hospital patients who need rapid response and transportation. This dissertation proposes a new realistic approach for EMS systems in two major focuses: multiple unit dispatching and relocation strategies. This work makes recommendations for multiple-unit dispatch to multiple call priorities based on simulation optimization and heuristics. The objective is to maximize the expected survival rate. Simulation models are proposed to determine the optimization. A heuristic algorithm is developed for large-scale problems. Numerical results show that dispatching while considering call priorities, rather than always dispatching the closest medical units, could improve the effectiveness of EMS systems. Additionally, we extend the model of multiple-unit dispatch to examine fairness between call priorities. We consider the potentially-life-threatening calls which could be upgraded to life-threatening. We formulate the fairness problem as an integer programming model solved using simulation optimization. Taking into account fairness between priorities improves the performance of EMS systems while still operating at high efficiency. As another focus, we consider dynamic relocation strategy using a nested-compliance table policy. For each state of the EMS systems, a decision must be made regarding exactly which ambulances will be allocated to which stations. We determine the optimal nested-compliance table in order to maximize the expected coverage, in the binary sense, as will be later discussed. We formulate the nested-compliance table model as an integer program, for which we approximate the steady-state probabilities of EMS system to use as parameters to our model. Simulation is used to investigate the performance of the model and to compare the results to a static policy based on the adjusted maximum expected covering location problem (AMEXCLP). Additionally, we extend the nested-compliance table model to consider an upper bound on relocation time. We analyze the decision regarding how to partition the service area into smaller sub-areas (districts) in which each sub-area operates independently under separate relocation strategies. We embed the nested-compliance table model into a tabu search heuristic algorithm. Iteration is used to search for a near-optimal solution. The performance of the tabu search heuristic and AMEXCLP are compared in terms of the realized expected coverage of EMS systems

Strategic Location and Dispatch Management of Assets in a Military Medical Evacuation Enterprise

This dissertation considers the importance of optimizing deployed military medical evacuation (MEDEVAC) systems and utilizes operations research techniques to develop models that allow military medical planners to analyze different strategies regarding the management of MEDEVAC assets in a deployed environment. For optimization models relating to selected subproblems of the MEDEVAC enterprise, the work herein leverages integer programming, multi-objective optimization, Markov decision processes, approximate dynamic programming, and machine learning, as appropriate, to identify relevant insights for aerial MEDEVAC operations

EV charging stations and RES-based DG: A centralized approach for smart integration in active distribution grids

Renewable Energy Sources based (RES-based) Dispersed Generation (DG) and Electrical Vehicles (EVs) charging systems diffusion is in progress in many Countries around the word. They have huge effects on the distribution grids planning and operation, particularly on MV and LV distribution grids. Many studies on their impact on the power systems are ongoing, proposing different approaches of managing. The present work deals with a real application case of integration of EVs charging stations with ES-based DG. The final task of the integration is to be able to assure the maximum utilization of the distribution grid to which both are connected, without any upgrading action, and in accordance with Distribution System Operators (DSOs) needs. The application of the proposed approach is related to an existent distribution system, owned by edistribuzione, the leading DSO in Italy. Diverse types of EVs supplying stations, with diverse diffusion scenarios, have been assumed for the case study; various Optimal Power Flow (OPF) models, based on diverse objective functions, reflecting DSO necessities, have been applied and tried. The obtained results demonstrate that a centralized management approach by the DSO, could assure the respect of operation limits of the system in the actual asset, delaying or avoiding upgrading engagements and charges

Using Markov Decision Processes with Heterogeneous Queueing Systems to Examine Military MEDEVAC Dispatching Policies

major focus of the Military Health System is to provide efficient and timely medical evacuation (MEDEVAC) to battlefield casualties. Medical planners are responsible for developing dispatching policies that dictate how aerial military MEDEVAC units are utilized during major combat operations. The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on the request\u27s classification (i.e., zone and precedence level) and the state of the MEDEVAC system. Rejected requests are rerouted to be serviced by other, non-medical military organizations in theater. Performance is measured in terms of casualty survivability rather than a response time threshold since survival probability more accurately represents casualty outcomes. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic baseline policies. Two computational experiments, a two-level, five-factor screening design and a subsequent three-level, three-factor full factorial design, are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system level performance measure. Results indicate that dispatching the closest available MEDEVAC unit is not always optimal and that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Moreover, an open question exists concerning the best exact solution approach for solving Markov decision problems due to recent advances in performance by commercial linear programming (LP) solvers. An analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the LP algorithms executed by CPLEX 12.6 in regards to computational effort. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems

A mathematical programming approach for dispatching and relocating EMS vehicles.

We consider the problem of dispatching and relocating EMS vehicles during a pandemic outbreak. In such a situation, the demand for EMS vehicles increases and in order to better utilize their capacity, the idea of serving more than one patient by an ambulance is introduced. Vehicles transporting high priority patients cannot serve any other patient, but those transporting low priority patients are allowed to be rerouted to serve a second patient. We have considered three separate problems in this research. In the first problem, an integrated model is developed for dispatching and relocating EMS vehicles, where dispatchers determine hospitals for patients. The second problem considers just relocating EMS vehicles. In the third problem only dispatching decisions are made where hospitals are pre-specified by patients not by dispatchers. In the first problem, the objective is to minimize the total travel distance and the penalty of not meeting specific constraints. In order to better utilize the capacity of ambulances, we allow each ambulance to serve a maximum of two patients. Considerations are given to features such as meeting the required response time window for patients, batching non-critical and critical patients when necessary, ensuring balanced coverage for all census tracts. Three models are proposed- two of them are linear integer programing and the other is a non-linear programing model. Numerical examples show that the linear models can be solved using general-purpose solvers efficiently for large sized problems, and thus it is suitable for use in a real time decision support system. In the second problem, the goal is to maximize the coverage for serving future calls in a required time window. A linear programming model is developed for this problem. The objective is to maximize the number of census tracts with single and double coverage, (each with their own weights) and to minimize the travel time for relocating. In order to tune the parameters in this objective function, an event based simulation model is developed to study the movement of vehicles and incidents (911 calls) through a city. The results show that the proposed model can effectively increase the system-wide coverage by EMS vehicles even if we assume that vehicles cannot respond to any incidents while traveling between stations. In addition, the results suggest that the proposed model outperforms one of the well-known real time repositioning models (Gendreau et al. (2001)). In the third problem, the objective is to minimize the total travel distance experienced by all EMS vehicles, while satisfying two types of time window constraints. One requires the EMS vehicle to arrive at the patients\u27 scene within a pre-specified time, the other requires the EMS vehicle to transport patients to their hospitals within a given time window. Similar to the first problem, each vehicle can transport maximum two patients. A mixed integer program (MIP) model is developed for the EMS dispatching problem. The problem is proved to be NP-hard, and a simulated annealing (SA) method is developed for its efficient solution. Additionally, to obtain lower bound, a column generation method is developed. Our numerical results show that the proposed SA provides high quality solutions whose objective is close to the obtained lower bound with much less CPU time. Thus, the SA method is suitable for implementation in a real-time decision support system

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

Emergency medical dispatching : protocols, experiences and priorities

Each year, millions of people call the emergency number with a wide variety of symptoms and various levels of illness severity. At its’ core, emergency medical dispatching encompasses the answering of these calls, the assessment of the need for medical assistance, the dispatch of a resource with an appropriate priority level and the provision of instructions assisting the caller. Consequently, emergency medical dispatching is important in ensuring patient safety as well as for ascertaining the best use of limited resources. However, research on different aspects of emergency medical dispatch remains limited. Therefore, this thesis’s overall aim was to provide new knowledge in relation to dispatch protocols and the assessment and prioritization of emergency medical calls. Further, to bring light onto emergency medical dispatchers’ (EMDs) experiences of managing such calls thereby creating an understanding and foundation for further development and strengthening of this first link in the chain of emergency care. The thesis builds upon four studies based on different populations: Study I: a simulation study with the aim to compare the accuracy, in terms of correct dispatch priority, between two dispatch protocols; the Swedish Index and RETTS-A. Expert consensus was used as reference standard. A total of 1,293 calls was included. For priority level, 349 (54%) calls were assessed correctly with Swedish Index and 309 (48%) with RETTS-A (p=0.012). Sensitivity for the highest priority was 82.6% (95% CI: 76.6-87.3) for Swedish Index and 54.0% (95% CI: 44.3-63.4%) for RETTS-A. Overtriage was 37.9% (34.2.-41.7%) in Swedish Index and 28.6% (25.2-32.2) in RETTS-A. The corresponding proportion of undertriage was 6.3% (4.7-8.5) and 23.4% (20,3-26.9) respectively. The results demonstrate that although the Swedish Index had a higher accuracy than RETTS-A, the overall accuracy for both dispatch protocols is low. Study II: a retrospective observational study based on registry data from four Swedish regions in 2015. The aim was to compare calls assessed by an EMD with and without the support of a registered nurse (RN) with respect to priority level, accuracy, and dispatch category. Ambulance personnel’s assessment was used as reference standard. A total of 25,025 calls were included. Dispatch priority was in concordance with the reference standard in 11,319 (50.7%) for EMD and in 481 (41.5%) for EMD+RN, (p<0.01). Overtriage was equal for both groups; 5904 (26,4%) for EMD, and 306 (26.4%) for EMD+RN, (p=0.25). Undertriage was 5122 (22.9%) for EMD and 371 (32.0%) for EMD+RN (p<0.01). Sensitivity for the most urgent priority was 54.6% for EMD, compared to 29.6% for EMD+RN (p<0.01), and specificity was 67.3% and 84.8% (p<0.01) respectively. Dispatch category was in concordance with reference standard in 13,785 (66.4%) EMD and 697 (62.2%) EMD+RN (p=0.01). The results demonstrated that a higher precision was not observed for calls assessed with RN-support. Study III: a qualitative interview study aiming at exploring EMDs experiences of managing emergency medical calls. One main category emerged from the inductive content analysis of 13 interviews: “to attentively manage a multifaceted, interactive task”. The main category was in turn composed of three categories: “to utilize creativity to gather information”, “continuously process and assess complex information” and “engage in the professional role”. Study IV: a retrospective observational registry study on all primary ambulance missions within the Stockholm Region Aug 2019 to Sept 2022. The aim was to identify the proportion of time critical patients, defined as patients receiving time critical interventions in the prehospital setting, having an ambulance dispatched as Priority 1. Further, to describe dispatch categories and emergency department (ED) diagnoses. Of 571 163 included missions, 92 975 (16.3%) were time critical. Of these, 75 504 (81.2%) had an ambulance dispatched as Priority 1, 16 967 (18.2%) as Priority 2, and 504 (0.5%) as Priority 3. When stratified according to dispatch priority, the ranking of the most common dispatch categories differed. ED-diagnosis were mostly symptom-related. The results demonstrate that most patients with time critical conditions receive an ambulance dispatched as Priority 1. Those who are not identified as in need of an ambulance dispatched as Priority 1, differ in regard to their presentation, and often present to the EMCC with unspecific symptoms. In conclusion, this thesis sheds light on different aspects of emergency medical dispatching in regard to emergency calls with a wide range of symptoms. Specifically, it contributes to the evaluation of dispatch protocols and highlights the need for further investigations in relation to the established, yet understudied, practice of emergency medical dispatching performed predominantly by EMDs with and without the support of RNs. Given their key role in managing this multifaceted interactive task, the results can be used to inform future development of protocols and interview techniques. The results further indicate the need for regular feedback, as part of clinical routine. Finally, the thesis enhances the understanding of the population of patients with time critical conditions and contributes to the understanding and future establishment of a definition of time critical conditions in the pre-hospital setting