Model-based approaches to decision making in healthcare delivery

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School of Business | Doctoral thesis (article-based) | Defence date: 2024-05-24
Degree programme
46 + app. 72
Aalto University publication series DOCTORAL THESES, 89/2024
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.
Supervising professor
Vilkkumaa, Eeva, Assist. Prof., Aalto University School of Business, Department of Information and Service Management, Finland
Thesis advisor
Flander, Louisa, Dr. Principal Fellow, University of Melbourne School of Population and Global Health, Australia
optimisation, stochastic programming, health economics, decision support
Other note
  • [Publication 1]: Dillon, M., Flander, L., Buchanan, D.D., Macrae, F.A., Emery, J.D., Winship, I.M., Boussioutas, A., Giles, G.G., Hopper, J.L., Jenkins, M.A. and Ait Ouakrim, D. Family history-based colorectal cancer screening in Australia: A modelling study of the costs, benefits, and harms of different participation scenarios. PLoS medicine, 15(8), p.e1002630, 2018.
    DOI: 10.1371/journal.pmed.1002630 View at publisher
  • [Publication 2]: Dillon, M. Planning for the next pandemic: The Finnish colorectal cancer screening programme. In European Decision Science Institute Conference, Dublin, 14 pages, May 2022.
  • [Publication 3]: Dillon, M., Oliveira, F. and Abbasi, B. A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics, 187, 27-41, 2017.
    DOI: 10.1016/j.ijpe.2017.02.006 View at publisher
  • [Publication 4]: Dillon, M., Vauhkonen, I., Arvas, M., Ihalainen, J., Vilkkumaa, E., Oliveira, F. Supporting platelet inventory management decisions: What is the effect of extending platelets’ shelf life. European Journal of Operational Research, 310(2), 640–654, 2023.
    DOI: 10.1016/j.ejor.2023.03.007 View at publisher