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.