Browsing by Author "Vilkkumaa, Eeva, Assist. Prof., Aalto University School of Business, Department of Information and Service Management, Finland"

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Model-based approaches to decision making in healthcare delivery
(2024) Dillon, Mary
School of Business | Doctoral dissertation (article-based)
Healthcare systems worldwide face escalating pressures from aging populations, advancements in pharmaceuticals and technologies, strained services, and economic constraints. Hence, robust decision-making processes are imperative to maximise population health. Mathematical modelling has proven to be a valuable tool for addressing such healthcare challenges. Recent experiences, exemplified by the COVID-19 pandemic, have demonstrated the effectiveness of mathematical modelling in decision-making in healthcare delivery. This thesis contributes to the advancement of model-based decision-making in healthcare with a focus on practical applicability. It leverages two healthcare domains, colorectal cancer screening and the blood supply chain, to illustrate the benefit of model-based approaches in improving costeffectiveness and resource utilisation in public healthcare delivery. One avenue for informed decision-making aimed at achieving an equitable, efficient, and high-quality healthcare system is health technology assessment; a process that employs analytical methods to evaluate the value of healthcare technologies or interventions throughout their life cycle. In this thesis, the long-term evaluation of appropriate modal of colorectal cancer screening practices and resource allocation is considered through cost-effectiveness analyses. Second, given that healthcare systems are inherently fraught with uncertainty, there exists a necessity for day-to-day decisions that remain robust in the face of the unknown. This thesis employs mathematical optimisation models to address decision-making under uncertainty, particularly within the management of blood inventories. Optimisation entails the selection of the decision alternatives to maximise a specified objective. Stochastic programming is utilised to incorporate uncertain blood demand into models that define optimal blood inventory policies. Optimisation is a powerful tool when decisions made today must remain valid into the future. In conclusion, this thesis underscores the role of model-based approaches in healthcare decisionmaking. By applying these approaches in the contexts of colorectal cancer screening and the blood supply chain, this research contributes to enhancing the efficiency, cost-effectiveness, and overall quality of public healthcare delivery.