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Modelling lung cancer diagnostic pathways using discrete event simulation

Modelling lung cancer diagnostic pathways using discrete event simulation
Modelling lung cancer diagnostic pathways using discrete event simulation
The United Kingdom has one of the poorest lung cancer survival rates in Europe. In this study, and as part of a wider programme of research to help design and evaluate a single lung cancer pathway (SCP) for Wales, existing diagnostic pathways and processes have been mapped and then modelled with a discrete event simulation for several major Welsh cancer care centres. The validated models have been used to provide key performance indicators and to examine different diagnostic testing strategies and capacities. Under the current diagnostic pathways, the mean time to treatment was 72 days for surgery patients, 56 days for chemotherapy patients, and 61 days for radiotherapy patients. Our research demonstrated that by ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 11 days from the current lung cancer pathway resulting in a 21% increase of patients receiving treatment within the Welsh Government set target of 62 days. Ensuring that patients begin their treatment within 21 days of diagnosis sees almost all patients comply with the 62-days. This case study helps to demonstrate how discrete event simulation, coupled with detailed statistical analysis and collaboration with clinicians, provides a useful decision support tool which can be used to examine possible improvements to cancer diagnostic pathways, where time to diagnosis is critical to improved patient outcomes.
Diagnostic pathway, cancer, discrete event simulation, performance indicators
1747-7778
England, Tracey
8f99b32a-1670-4e20-b6c6-30ae96940ca2
Harper, Paul
ca5e539f-4d8c-483c-a3f1-2beedeaacd49
Crosby, Tom
d641cb6d-efc6-45ae-b083-a21c599a032c
Gartner, Daniel
fbe94ad1-bea5-441c-89aa-c5327e450f4b
Arruda, Edilson F.
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Foley, Kieran
ba5b6b0c-35de-4b9f-8bcf-aafd42603504
Williamson, Ian
12381296-edbf-4ac5-969b-dcb559c22f27
England, Tracey
8f99b32a-1670-4e20-b6c6-30ae96940ca2
Harper, Paul
ca5e539f-4d8c-483c-a3f1-2beedeaacd49
Crosby, Tom
d641cb6d-efc6-45ae-b083-a21c599a032c
Gartner, Daniel
fbe94ad1-bea5-441c-89aa-c5327e450f4b
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Foley, Kieran
ba5b6b0c-35de-4b9f-8bcf-aafd42603504
Williamson, Ian
12381296-edbf-4ac5-969b-dcb559c22f27

England, Tracey, Harper, Paul, Crosby, Tom, Gartner, Daniel, Arruda, Edilson F., Foley, Kieran and Williamson, Ian (2021) Modelling lung cancer diagnostic pathways using discrete event simulation. Journal of Simulation. (doi:10.1080/17477778.2021.1956866). (In Press)

Record type: Article

Abstract

The United Kingdom has one of the poorest lung cancer survival rates in Europe. In this study, and as part of a wider programme of research to help design and evaluate a single lung cancer pathway (SCP) for Wales, existing diagnostic pathways and processes have been mapped and then modelled with a discrete event simulation for several major Welsh cancer care centres. The validated models have been used to provide key performance indicators and to examine different diagnostic testing strategies and capacities. Under the current diagnostic pathways, the mean time to treatment was 72 days for surgery patients, 56 days for chemotherapy patients, and 61 days for radiotherapy patients. Our research demonstrated that by ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 11 days from the current lung cancer pathway resulting in a 21% increase of patients receiving treatment within the Welsh Government set target of 62 days. Ensuring that patients begin their treatment within 21 days of diagnosis sees almost all patients comply with the 62-days. This case study helps to demonstrate how discrete event simulation, coupled with detailed statistical analysis and collaboration with clinicians, provides a useful decision support tool which can be used to examine possible improvements to cancer diagnostic pathways, where time to diagnosis is critical to improved patient outcomes.

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More information

Accepted/In Press date: 12 July 2021
Keywords: Diagnostic pathway, cancer, discrete event simulation, performance indicators

Identifiers

Local EPrints ID: 450424
URI: http://eprints.soton.ac.uk/id/eprint/450424
ISSN: 1747-7778
PURE UUID: 024fa694-d82f-4630-99e9-9c13ed47e510
ORCID for Edilson F. Arruda: ORCID iD orcid.org/0000-0002-9835-352X

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Date deposited: 28 Jul 2021 16:30
Last modified: 28 Apr 2022 02:31

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Contributors

Author: Tracey England
Author: Paul Harper
Author: Tom Crosby
Author: Daniel Gartner
Author: Kieran Foley
Author: Ian Williamson

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