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.

A Mobile stroke unit (MSU) is a type of ambulance deployed to promote the rapid delivery of stroke care. We present a computational study using a time to treatment estimation model to analyze the potential benefits of using MSUs in Sweden's Southern Health Care Region (SHR). In particular, we developed two scenarios (MSU1 and MSU2) each including three MSUs, which we compared with a baseline scenario containing only regular ambulances. For each MSU scenario, we assessed how much the expected time to treatment is estimated to decrease for the whole region and each subregion of SHR, and how the population is expected to benefit from the deployment of MSUs. For example, the average time to treatment in SHR was decreased with 20,4 and 15,6 minutes, respectively, in the two MSU scenarios. Moreover, our computational results show that the locations of the MSUs significantly influence what benefits can be expected. While MSU1 is expected to improve the situation for a higher share of the population, MSU2 is expected to have a higher impact on the patients who currently have the longest time to treatment.