Preparing travel time data can be a time-consuming process, which greatly limits the flexibility of transport simulation models. In the current paper, we present an approach to integrate a routing engine locally in an existing modeling framework, hence enabling to dynamically calculate travel times in the constructed emergency medical services (EMS) simulation models. This integration eliminates the need for the pre-calculation typically required to prepare travel time data. Using the extended framework, we developed an EMS simulation model for stroke patients, which we applied in a scenario study to southern Sweden. This allowed us to evaluate the potential benefits of using dynamic travel time calculations in prehospital stroke care. The experimental results, supported by comparisons with pre-calculated travel times, confirm the effectiveness of our approach in integrating dynamic travel time calculations into the framework. Moreover, the results of our evaluation indicate that including this functionality in simulation models can provide more realistic results. Finally, our approach for local implementation of dynamic travel time calculations is faster and less restricted compared to using online services.

Rapid treatment is crucial for minimizing the consequences of a stroke. However, logistical challenges and the complexity of accurate stroke diagnosis often impede timely and effective treatment. One way to reduce time to treatment is the use of so-called mobile stroke units (MSUs), which are specialized ambulances equipped to diagnose and treat stroke patients on site. The adequate planning and optimization of prehospital stroke transport policies involving MSUs can help reduce delays in accessing treatment. Mathematical optimization and simulation are useful approaches for optimizing and assessing different stroke transport policies without endangering patient’s health.

The aim of this thesis is to explore how optimization and simulation can improve the analysis and planning of prehospital stroke care. Specifically, optimization is used to determine optimal MSU placements, while simulation is applied to evaluate stroke transport policies, including those involving MSUs. To achieve this aim, the thesis is structured around four main objectives, in which we develop and analyze a number of different optimization and simulation models. First, the MSU placement problem is solved using an exhaustive search algorithm and formulated as a mixed-integer linear programming model to determine optimal MSU placements. The objective of solving this problem is to make a trade-off between efficiency and equity, ensuring maximum population coverage and equitable service across a region. Second, macro-level and micro- level simulation models are proposed to evaluate various stroke transport policies, including MSUs. Third, a simulation modeling framework is introduced to enable the construction of discrete event simulation models for emergency medical services (EMS) policy analysis, supporting flexible and adaptive simulations of real-world EMS operations. The framework incorporates various decision policies, such as emergency vehicle selection, dispatch type (single and co-dispatch) selection, and hospital selection, allowing for the evaluation of stroke transport policies across different stroke types. Lastly, dynamic travel time calculations and machine learning-based travel time estimations are integrated into the framework to enhance the flexibility and reliability of EMS simulations.

Through scenario studies conducted in Sweden’s Southern Healthcare Region, this research demonstrates how optimization and simulation can support effective stroke transport policy planning and improve decision-making in prehospital stroke care. The identified MSU placements, along with the evaluated dispatch policies, highlight significant potential for reducing the time to diagnosis and treatment for different types of strokes. Faster time to treatment not only enhances overall stroke care delivery but also improves patient outcomes by reducing stroke-related disabilities. The findings underscore the value of these approaches in guiding EMS policy design, ultimately contributing to better patient outcomes and reduced social impacts of stroke. The results of this thesis aim to assist public health authorities in making informed decisions to optimize prehospital stroke care.

Rapid treatment is crucial for minimizing the consequences of a stroke. However, logistical challenges and the complexity of accurate stroke diagnosis often impede timely and effective treatment. One way to reduce time to treatment is the use of so-called mobile stroke units (MSUs), which are specialized ambulances equipped to diagnose and treat stroke patients on site. The adequate planning and optimization of prehospital stroke transport policies involving MSUs can help reduce delays in accessing treatment. Mathematical optimization and simulation are useful approaches for optimizing and assessing different stroke transport policies without endangering patient’s health. The aim of this thesis is to explore how optimization and simulation can improve the analysis and planning of prehospital stroke care. Specifically, optimization is used to determine optimal MSU placements, while simulation is applied to evaluate stroke transport policies, including those involving MSUs. To achieve this aim, the thesis is structured around four main objectives, in which we develop and analyze a number of different optimization and simulation models. First, the MSU placement problem is solved using an exhaustive search algorithm and formulated as a mixed-integer linear programming model to determine optimal MSU placements. The objective of solving this problem is to make a trade-off between efficiency and equity, ensuring maximum population coverage and equitable service across a region. Second, macro-level and micro- level simulation models are proposed to evaluate various stroke transport policies, including MSUs. Third, a simulation modeling framework is introduced to enable the construction of discrete event simulation models for emergency medical services (EMS) policy analysis, supporting flexible and adaptive simulations of real-world EMS operations. The framework incorporates various decision policies, such as emergency vehicle selection, dispatch type (single and co-dispatch) selection, and hospital selection, allowing for the evaluation of stroke transport policies across different stroke types. Lastly, dynamic travel time calculations and machine learning-based travel time estimations are integrated into the framework to enhance the flexibility and reliability of EMS simulations. Through scenario studies conducted in Sweden’s Southern Healthcare Region, this research demonstrates how optimization and simulation can support effective stroke transport policy planning and improve decision-making in prehospital stroke care. The identified MSU placements, along with the evaluated dispatch policies, highlight significant potential for reducing the time to diagnosis and treatment for different types of strokes. Faster time to treatment not only enhances overall stroke care delivery but also improves patient outcomes by reducing stroke-related disabilities. The findings underscore the value of these approaches in guiding EMS policy design, ultimately contributing to better patient outcomes and reduced social impacts of stroke. The results of this thesis aim to assist public health authorities in making informed decisions to optimize prehospital stroke care.

Travel time estimation is an integral component of emergency medical services (EMS) simulations due to the need to calculate ambulance transport times for patients. We present a study where we integrated a machine learning (ML) based ambulance travel time estimation module into an EMS simulation modeling framework, aiming to explore the potential benefits of using ML-based travel time estimations in emergency simulations. To illustrate the effectiveness of the proposed approach, we used the framework to construct an EMS simulation model for stroke patients and applied it in a scenario study covering Skåne County, Sweden. The result of the simulation shows differences in ambulance driving times when using the ML-based module compared to existing routing engines designed for passenger cars. The observed differences emphasize the impacts of integrating ML-based estimations into EMS simulations.

A mobile stroke unit (MSU) is a specialized ambulance, enabling to shorten the time to diagnosis and treatment for stroke patients. In the current paper, we present a simulation-based approach to study the potential impacts of collaborative use of regular ambulances and MSUs in prehospital transportation for stroke patients, denoted as co-dispatching. We integrated a co-dispatch policy in an existing modeling framework for constructing emergency medical services simulation models. In a case study, we applied the extended framework to southern Sweden to evaluate the effectiveness of using the co-dispatch policy for different types of stroke. The results indicate reduced time to diagnosis and treatment for stroke patients when using the co-dispatch policy compared to the situation where either a regular ambulance or an MSU is assigned for a stroke incident.

A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.

Mobile Stroke Units (MSUs) are specialized ambulances that can diagnose and treat stroke patients; hence, reducing the time to treatment for stroke patients. Optimal placement of MSUs in a geographic region enables to maximize access to treatment for stroke patients. We contribute a mathematical model to optimally place MSUs in a geographic region. The objective function of the model takes the tradeoff perspective, balancing between the efficiency and equity perspectives for the MSU placement. Solving the optimization problem enables to optimize the placement of MSUs for the chosen tradeoff between the efficiency and equity perspectives. We applied the model to the Blekinge and Kronoberg counties of Sweden to illustrate the applicability of our model. The experimental findings show both the correctness of the suggested model and the benefits of placing MSUs in the considered regions.

Constructing simulation models can be a complex and time-consuming task, in particular if the models are constructed from scratch or if a general-purpose simulation modeling tool is used. In this paper, we propose a model construction framework, which aims to simplify the process of constructing discrete event simulation models for emergency medical service (EMS) policy analysis. The main building blocks used in the framework are a set of general activities that can be used to represent different EMS care chains modeled as flowcharts. The framework allows to build models only by specifying input data, including demographic and statistical data, and providing a care chain of activities and decisions. In a case study, we evaluated the framework by using it to construct a model for the simulation of the EMS activities related to acute stroke. Our evaluation shows that the predefined activities included in the framework are sufficient to build a simulation model for the rather complex case of acute stroke.

A mobile stroke unit (MSU) is a special type of ambulance, where stroke patients can be diagnosed and provided intravenous treatment, hence allowing to cut down the time to treatment for stroke patients. We present a discrete event simulation (DES) model to study the potential benefits of using MSUs in the southern health care region of Sweden (SHR). We included the activities and actions used in the SHR for stroke patient transportation as events in the DES model, and we generated a synthetic set of stroke patients as input for the simulation model. In a scenario study, we compared two scenarios, including three MSUs each, with the current situation, having only regular ambulances. We also performed a sensitivity analysis to further evaluate the presented DES model. For both MSU scenarios, our simulation results indicate that the average time to treatment is expected to decrease for the whole region and for each municipality of SHR. For example, the average time to treatment in the SHR is reduced from 1.31h in the baseline scenario to 1.20h and 1.23h for the two MSU scenarios. In addition, the share of stroke patients who are expected to receive treatment within one hour is increased by a factor of about 3 for both MSU scenarios.

Immediate treatment is of extreme importance for stroke patients. However, providing fast enough treatment for stroke patients is far from trivial, mainly due to logistical challenges and difficulties in diagnosing the correct stroke type. One way to reduce the time to treatment is to use so-called Mobile Stroke Units (MSUs), which allows to diagnose and provide treatment for stroke patients already at the patient scene. A well-designed stroke transport policy is vital to improve the access to treatment for stroke patients. Simulation and mathematical optimization are useful approaches for assessing and optimizing stroke transport policies, without endangering the health of the patients.

The main purpose of this thesis is to contribute to improving the situation for stroke patients and to reducing the social impacts of stroke. The aim is to study how to use simulation and optimization to achieve improved analysis and planning of prehospital stroke care. In particular, we focus on assessing the potential use of MSUs in a geographic area. In this thesis, optimization is used to identify the optimal locations of MSUs, and simulation is used to assess different stroke transport policies, including MSU locations. The results of this thesis aim to support public health authorities when making decisions in the prehospital stroke care domain.

In order to fulfill the aim of this thesis, we develop and analyze a number of different simulation and optimization models. First, we propose a macro-level simulation model, an average time to treatment estimation model, used to estimate the expected time to treatment for different parts of a geographic region. Using the proposed model, we generate two different MSU scenarios to explore the potential benefits of employing MSUs in Sweden’s southern healthcare region (SHR).  

Second, we present an optimization model to identify the best placement of MSUs while making a trade-off between the efficiency and equity perspectives, providing maximum population coverage and equal service for all patients, respectively. The trade-off function used in the model makes use of the concepts of weighted average time to treatment to model efficiency and the time difference between the expected time to treatment for different geographical areas to model equity. In a scenario study applied in the SHR, we evaluate our optimization model by comparing the current situation with three MSU scenarios, including 1, 2, and 3 MSUs.

Third, we present a micro-level discrete event simulation model to assess stroke transport policies, including MSUs, allowing us to model the behaviors of individual entities, such as patients and emergency vehicles, over time. We generate a synthetic set of stroke patients using a Poisson distribution, used as input in a scenario study.

Finally, we present a modeling framework with reusable components, which aims to facilitate the construction of discrete event simulation models in the emergency medical services domain. The framework consists of a number of generic activities, which can be used to represent healthcare chains modeled in the form of flowcharts. As the framework includes activities and policies modeled on the general level, the framework can be used to create models only by providing input data and a care chain specification. We evaluate the framework by using it to build a model for simulating EMS activities related to the complex case of acute stroke.

A mobile stroke unit (MSU) is an ambulance, where stroke patients can be diagnosed and treated. Recently, placement of MSUs has been studied focusing on either maximum population coverage or equal service for all patients, termed efficiency and equity, respectively. In this study, we propose an unconstrained optimization model for the placement of MSUs, designed to introduce a tradeoff between efficiency and equity. The tradeoff is based on the concepts of weighted average time to treatment and the time difference between the expected time to treatment for different geographical areas. We conduct a case-study for Sweden’s Southern Health care Region (SHR), generating three scenarios (MSU1, MSU2, and MSU3) including 1, 2, and 3 MSUs, respectively. We show that our proposed optimization model can tune the tradeoff between the efficiency and equity perspectives for the MSU(s) allocation. This enables a high level of equal service for most inhabitants, as well as reducing the time to treatment for most inhabitants of a geographic region. In particular, placing three MSUs in the SHR with the proposed tradeoff, the share of inhabitants who are expected to receive treatment within an hour potentially improved by about a factor of 14 in our model.