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. 2020 Jun 30;32(2):221-236.
doi: 10.1093/imaman/dpaa014. eCollection 2021 Apr.

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

Edilson F Arruda  1   2 , Paul Harper  1 , Tracey England  1 , Daniel Gartner  1 , Emma Aspland  1 , Fabrício O Ourique  3 , Tom Crosby  4
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Resource optimization for cancer pathways with aggregate diagnostic demand: a perishable inventory approach

Edilson F Arruda et al. IMA J Manag Math. .

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 [Formula: see text] 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.

Keywords: capacity planning; healthcare modelling; inventory control; queuing systems.

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Figures

<sc>Fig.</sc> 1.
Fig. 1.. Incoming distribution of lung cancer suspicions.
<sc>Fig.</sc> 2.
Fig. 2.. Distribution of CT requests for lung cancer referrals.
<sc>Fig.</sc> 3.
Fig. 3.. Cumulative distribution of CT requests for lung cancer referrals.
<sc>Fig.</sc> 4.
Fig. 4.. Steady state probabilities for the number of pending tests.
<sc>Fig.</sc> 5.
Fig. 5.. Waiting time thresholds and probabilities.
See this image and copyright information in PMC

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