Stroke remains one of the leading causes of death and disability globally, underscoring the importance of timely and effective prehospital care to improve patient outcomes. This thesis aims to use artificial intelligence’s power to enhance prehospital stroke care. To accomplish this, we study challenges in prehospital stroke care by focusing on three interrelated research challenges: Mobile stroke unit (MSU) allocation, ambulance travel time estimation, and improving travel time calculations within emergency medical service (EMS) simulation. We develop and analyze different optimization and machine learning (ML) methods to achieve improved analysis and planning of prehospital stroke care. In particular, we propose methods to solve the MSU allocation problem, which aims to identify the optimal locations for a fixed number of MSUs at the existing ambulance station locations within a geographic region. Moreover, we develop a machine learning-based regression method for ambulance travel time estimation. Next, we apply our pre-trained ML-based regression method to improve ambulance travel time estimation within an EMS simulation framework.

For the MSU allocation problem, we first propose a mathematical model, which we apply to identify the optimal MSU locations in the Blekinge and Kronoberg counties of Sweden. The experimental findings show both the correctness of the suggested model and the benefits of placing MSUs in the considered regions. Second, we propose a Genetic algorithm (GA) method with an efficient encoding scheme for the input data, representing the number of MSUs and potential sites. Additionally, we develop custom selection, crossover, and mutation operators tailored to the specific 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. Third, we propose the enhanced genetic algorithm with clustering (EGAC). By leveraging clustering, the EGAC provides diverse and comprehensive coverage, avoiding the pitfalls of starting with closely located and potentially less optimal solutions, thereby effectively steering and accelerating its convergence towards the optimal MSU placements. Our experimental results show that the EGAC significantly outperforms the traditional genetic algorithm, which does not make use of cluster-based starting solutions, by achieving remarkably faster convergence towards the optimal solution for different numbers of MSUs to allocate. We illustrate the performance of the EGAC through qualitative and quantitative analyses.

For ambulance travel time estimation, we propose an ML-based regression method for estimating ambulance travel times. Ambulance travel time estimations play a pivotal role in ensuring timely and efficient emergency medical care by predicting the time needed by an ambulance to reach a specific location. Overlooking factors such as local traffic situations, day of the week, hour of the day, or the weather may create a risk of inaccurately estimating the ambulance travel times, which might lead to longer emergency response times, potentially impacting patient outcomes. We propose a machine learning approach to accurately estimate ambulance travel times, particularly using regression models and real-world spatiotemporal data from the Skåne region, Sweden. Our method includes data preprocessing and feature engineering, with a focus on variables significantly correlated with travel time. Through a comprehensive exploratory data analysis, we highlight the main characteristics, patterns, and underlying trends of the considered ambulance data set. We present an extensive empirical analysis comparing the performance of different machine learning models across different ambulance travel trips and feature sets, revealing insights into the importance of each feature in improving the estimation accuracy.

Another focus of this thesis is the use of our ML-based regression method to improve the ambulance travel time estimation within EMS simulation. To illustrate the effectiveness of the proposed regression modeling, we utilize a modeling construction 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 am- bulance 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.

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.