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

Resource optimization for cancer pathways with aggregate diagnostic demand: a perishable inventory approach
Resource optimization for cancer pathways with aggregate diagnostic demand: a perishable inventory approach
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.
1471-678X
1
Fernandes De Arruda, Edilson
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
England, Tracey
6ad98e2d-7e4a-46ce-bb06-48af113aa8f8
Gartner, Daniel
fbe94ad1-bea5-441c-89aa-c5327e450f4b
Aspland, Emma
3320c1f8-2e36-4b7c-87b4-59026cce9806
Paul Harper
Fabricio de Oliveira Ourique
Tom Crossby
Fernandes De Arruda, Edilson
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
England, Tracey
6ad98e2d-7e4a-46ce-bb06-48af113aa8f8
Gartner, Daniel
fbe94ad1-bea5-441c-89aa-c5327e450f4b
Aspland, Emma
3320c1f8-2e36-4b7c-87b4-59026cce9806

Fernandes De Arruda, Edilson, England, Tracey, Gartner, Daniel and Aspland, Emma , Paul Harper, Fabricio de Oliveira Ourique and Tom Crossby (2020) Resource optimization for cancer pathways with aggregate diagnostic demand: a perishable inventory approach. IMA Journal of Management Mathematics, 1. (doi:10.1093/imaman/dpaa014).

Record type: Article

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.

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More information

Accepted/In Press date: 17 May 2020
e-pub ahead of print date: 30 June 2020

Identifiers

Local EPrints ID: 444116
URI: http://eprints.soton.ac.uk/id/eprint/444116
ISSN: 1471-678X
PURE UUID: 92a19473-fd64-4765-ab6e-1dcd1f62ab49
ORCID for Edilson Fernandes De Arruda: ORCID iD orcid.org/0000-0002-9835-352X

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Date deposited: 25 Sep 2020 16:36
Last modified: 28 Apr 2022 02:31

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Contributors

Author: Tracey England
Author: Daniel Gartner
Author: Emma Aspland
Corporate Author: Paul Harper
Corporate Author: Fabricio de Oliveira Ourique
Corporate Author: Tom Crossby

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