Smart simulation and modelling for complex cancer systems

Clinical pathways are an effective and effcient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This research project was funded by KESS2 in collaboration with a company partner - Velindre Cancer Centre (VCC). This thesis develops effcient and sustainable methods for pathway mapping, modelling and improving, within the context of developing a state-of-the-art decision support tool. A particular focus on lung cancer is considered for method construction and investigations. The clinical pathways are mapped through representing each pathway as a string of letters. This enabled the development of the modifed Needleman-Wunsch metric, to allow for consideration of both data and medical expert information, for the use with k-medoids clustering. The key contribution of automating the simulation build and necessary input parameters, is developed. Models can be constructed for four routing procedures, namely Raw Pathways, Full Transitions, Cluster Transitions and Process Medoids, that explore progressively less complex and varied interpretations of the clinical pathways. Improvements can then be investigated for aligning capacity and demand. Combining this amounted to the development of Sim.Pro.Flow, an open access decision support tool, that contains all methods discussed in this thesis. The generalised approach allows for these methods, and Sim.Pro.Flow, to be suitably exible for application with process data from both healthcare and other industries