Oncological treatments are especially challenging due to the high degree of therapy personalisation that calls for the on-demand preparation of drugs and accurate timing in the distribution and delivery process. The suitability of the patient’s condition to carry on with the ongoing therapy is monitored periodically and bootstraps the whole preparation of a new iteration of the treatment. This makes pharmaceutical supply chain management very difficult to optimise since it needs to be responsive to sudden change while efficient in terms of costs and usage of resources. In this paper, we identify the main challenges of the domain drawing from the available related works and the experience maturated in collaboration with a public cancer care and research institute. We then propose how Digital Twins could be used as a way to engineer a system to support the accurate tracking of such a complex reality to monitor effectiveness and efficiency, as well as collect data and support decision-making using predictions and simulations to optimise the overall process.

A Digital Twins Approach for Oncologic Pharmaceutical Supply Chain

Montagna, Sara;
2024

Abstract

Oncological treatments are especially challenging due to the high degree of therapy personalisation that calls for the on-demand preparation of drugs and accurate timing in the distribution and delivery process. The suitability of the patient’s condition to carry on with the ongoing therapy is monitored periodically and bootstraps the whole preparation of a new iteration of the treatment. This makes pharmaceutical supply chain management very difficult to optimise since it needs to be responsive to sudden change while efficient in terms of costs and usage of resources. In this paper, we identify the main challenges of the domain drawing from the available related works and the experience maturated in collaboration with a public cancer care and research institute. We then propose how Digital Twins could be used as a way to engineer a system to support the accurate tracking of such a complex reality to monitor effectiveness and efficiency, as well as collect data and support decision-making using predictions and simulations to optimise the overall process.
2024
979-8-3503-0436-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11576/2736291
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