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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, 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. The validated model...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901409/ https://www.ncbi.nlm.nih.gov/pubmed/36760877 http://dx.doi.org/10.1080/17477778.2021.1956866 |
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author | England, Tracey Harper, Paul Crosby, Tom Gartner, Daniel Arruda, Edilson F. Foley, Kieran Williamson, Ian |
author_facet | England, Tracey Harper, Paul Crosby, Tom Gartner, Daniel Arruda, Edilson F. Foley, Kieran Williamson, Ian |
author_sort | England, Tracey |
collection | PubMed |
description | The United Kingdom has one of the poorest lung cancer survival rates in Europe. In this study, 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. The validated models have been used to provide key performance indicators and to examine different diagnostic testing strategies. 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 in patients receiving treatment within the Welsh Government set target of 62 days. |
format | Online Article Text |
id | pubmed-9901409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-99014092023-02-07 Modelling lung cancer diagnostic pathways using discrete event simulation England, Tracey Harper, Paul Crosby, Tom Gartner, Daniel Arruda, Edilson F. Foley, Kieran Williamson, Ian J Simul Reseach Article The United Kingdom has one of the poorest lung cancer survival rates in Europe. In this study, 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. The validated models have been used to provide key performance indicators and to examine different diagnostic testing strategies. 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 in patients receiving treatment within the Welsh Government set target of 62 days. Taylor & Francis 2021-08-02 /pmc/articles/PMC9901409/ /pubmed/36760877 http://dx.doi.org/10.1080/17477778.2021.1956866 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Reseach Article England, Tracey Harper, Paul Crosby, Tom Gartner, Daniel Arruda, Edilson F. Foley, Kieran Williamson, Ian Modelling lung cancer diagnostic pathways using discrete event simulation |
title | Modelling lung cancer diagnostic pathways using discrete event simulation |
title_full | Modelling lung cancer diagnostic pathways using discrete event simulation |
title_fullStr | Modelling lung cancer diagnostic pathways using discrete event simulation |
title_full_unstemmed | Modelling lung cancer diagnostic pathways using discrete event simulation |
title_short | Modelling lung cancer diagnostic pathways using discrete event simulation |
title_sort | modelling lung cancer diagnostic pathways using discrete event simulation |
topic | Reseach Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901409/ https://www.ncbi.nlm.nih.gov/pubmed/36760877 http://dx.doi.org/10.1080/17477778.2021.1956866 |
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