Cooke, EA;
Smith, NAS;
Thomas, SA;
Ruston, C;
Hothi, S;
Hughes, D;
(2022)
An integrated discrete event simulation and particle swarm optimisation model for optimising efficiency of cancer diagnosis pathways.
Healthcare Analytics
, 2
, Article 100082. 10.1016/j.health.2022.100082.
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Abstract
The National Health Service (NHS) constitution sets out minimum standards for rights of access of patients to NHS services. The ‘Faster Diagnosis Standard’ (FDS) states that 75% of patients should be told whether they have a diagnosis of cancer or not within 28 days of an urgent GP referral. Timely diagnosis and treatment lead to improved outcomes for cancer patients, however, compliance with these standards has recently been challenged, particularly in the context of operational pressures and resource constraints relating to the COVID-19 pandemic. In order to minimise diagnostic delays, the National Physical Laboratory in collaboration with the Royal Free London (RFL) NHS Foundation Trust address this problem by treating it as a formal resource optimisation, aiming to minimise the number of patients who breach the FDS. We use discrete event simulation and particle swarm optimisation to identify areas for improving the efficiency of cancer diagnosis at the RFL. We highlight capacity-demand mismatches in the current cancer diagnosis pathways at the RFL, including imaging and endoscopy investigations. This is due to the volume of patients requiring these investigations to meet the 28-day FDS target. We find that increasing resources in one area alone does not fully solve the problem. By looking at the system as a whole we identify areas for improvement which will have system-wide impact even though individually they do not necessarily seem significant. The outcomes and impact of this project have the potential to make a valuable impact on shaping future hospital activity.
| Type: | Article |
|---|---|
| Title: | An integrated discrete event simulation and particle swarm optimisation model for optimising efficiency of cancer diagnosis pathways |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.health.2022.100082 |
| Publisher version: | https://doi.org/10.1016/j.health.2022.100082 |
| Language: | English |
| Additional information: | © 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Diagnostics analytics, Particle swarm optimisation, Cancer diagnosis, Efficiency optimisation, Hospital operations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Haematology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10165787 |
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