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Resource optimisation for cancer pathways with aggregate diagnostic demand: a perishable inventory approach

Arruda, Edilson, Harper, Paul, England, Tracey, Gartner, Daniel, Aspland, Emma, Ourique, Fabrico and Crosby, Tom 2020. Resource optimisation for cancer pathways with aggregate diagnostic demand: a perishable inventory approach. IMA Journal of Management Mathematics 10.1093/imaman/dpaa014
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Abstract

This work proposes a novel framework for planning the capacity of diagnostic tests in cancer pathways that considers the aggregate demand of referrals from multiple cancer specialties (sites). The framework includes an analytic tool that recursively assesses the overall daily demand for each diagnostic test and considers general distributions for both the incoming cancer referrals and the number of required specific tests for any given patient. By disaggregating the problem with respect to each diagnostic test, we are able to model the system as a perishable inventory problem that can be solved by means of generalized G/D/C queuing models, where the capacity C is allowed to vary and can be seen as a random variable that is adjusted according to prescribed performance measures. The approach aims to provide public health and cancer services with recommendations to align capacity and demand for cancer diagnostic tests effectively and efficiently. Our case study illustrates the applicability of our methods on lung cancer referrals from UK’s National Health Service.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Mathematics
Data Innovation Research Institute (DIURI)
Publisher: Oxford University Press (OUP): IMA
ISSN: 1471-678X
Funders: Cancer Research UK
Date of First Compliant Deposit: 21 May 2020
Date of Acceptance: 17 May 2020
Last Modified: 31 Jul 2020 15:51
URI: http://orca.cf.ac.uk/id/eprint/131784

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