Process modelling of NHS cardiovascular waiting lists in response to the COVID-19 pandemic
Process modelling of NHS cardiovascular waiting lists in response to the COVID-19 pandemic
Objective: to model the referral, diagnostic and treatment pathway for cardiovascular disease (CVD) in the English National Health Service (NHS) to provide commissioners and managers with a methodology to optimise patient flow and reduce waiting lists.
Study: design A systems dynamics approach modelling the CVD healthcare system in England. The model is designed to capture current and predict future states of waiting lists.
Setting: routinely collected, publicly available data streams of primary and secondary care, sourced from NHS Digital, NHS England, the Office of National Statistics and StatsWales.
Data collection and extraction methods: the data used to train and validate the model were routinely collected and publicly available data. It was extracted and implemented in the model using the PySD package in python.
Results: NHS cardiovascular waiting lists in England have increased by over 40% compared with pre- COVID-19 levels. The rise in waiting lists was primarily due to restrictions in referrals from primary care, creating a bottleneck postpandemic. Predictive models show increasing point capacities within the system may paradoxically worsen downstream flow. While there is no simple rate-limiting step, the intervention that would most improve patient flow would be to increase consultant outpatient appointments.
Conclusions: the increase in NHS CVD waiting lists in England can be captured using a systems dynamics approach, as can the future state of waiting lists in the presence of further shocks/interventions. It is important for those planning services to use such a systems-oriented approach because the feed-forward and feedback nature of patient flow through referral, diagnostics and treatment leads to counterintuitive effects of interventions designed to reduce waiting lists.
cardiology, cardiac surgery, health policy
e065622
Catsis, Salvador
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Champneys, Alan R.
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Hoyle, Rebecca
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Currie, Christine
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Enright, Jessica
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Cheema, Katherine
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Woodall, Mike
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Angelini, Gianni
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Nadarajah, Ramesh
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Gale, Chris P.
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Gibbison, Ben
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19 July 2023
Catsis, Salvador
ed264df1-01e1-458c-808b-6da931937292
Champneys, Alan R.
47a636dc-28d2-4d57-ad12-578d34661284
Hoyle, Rebecca
e980d6a8-b750-491b-be13-84d695f8b8a1
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Enright, Jessica
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Cheema, Katherine
a4dee48c-95cb-43eb-85c2-b4964d78b674
Woodall, Mike
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Angelini, Gianni
36437e97-0cf6-4385-af7e-c583d89c782d
Nadarajah, Ramesh
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Gale, Chris P.
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Gibbison, Ben
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Catsis, Salvador, Champneys, Alan R., Hoyle, Rebecca, Currie, Christine, Enright, Jessica, Cheema, Katherine, Woodall, Mike, Angelini, Gianni, Nadarajah, Ramesh, Gale, Chris P. and Gibbison, Ben
(2023)
Process modelling of NHS cardiovascular waiting lists in response to the COVID-19 pandemic.
BMJ Open, 13 (7), , [e065622].
(doi:10.1136/bmjopen-2022-065622).
Abstract
Objective: to model the referral, diagnostic and treatment pathway for cardiovascular disease (CVD) in the English National Health Service (NHS) to provide commissioners and managers with a methodology to optimise patient flow and reduce waiting lists.
Study: design A systems dynamics approach modelling the CVD healthcare system in England. The model is designed to capture current and predict future states of waiting lists.
Setting: routinely collected, publicly available data streams of primary and secondary care, sourced from NHS Digital, NHS England, the Office of National Statistics and StatsWales.
Data collection and extraction methods: the data used to train and validate the model were routinely collected and publicly available data. It was extracted and implemented in the model using the PySD package in python.
Results: NHS cardiovascular waiting lists in England have increased by over 40% compared with pre- COVID-19 levels. The rise in waiting lists was primarily due to restrictions in referrals from primary care, creating a bottleneck postpandemic. Predictive models show increasing point capacities within the system may paradoxically worsen downstream flow. While there is no simple rate-limiting step, the intervention that would most improve patient flow would be to increase consultant outpatient appointments.
Conclusions: the increase in NHS CVD waiting lists in England can be captured using a systems dynamics approach, as can the future state of waiting lists in the presence of further shocks/interventions. It is important for those planning services to use such a systems-oriented approach because the feed-forward and feedback nature of patient flow through referral, diagnostics and treatment leads to counterintuitive effects of interventions designed to reduce waiting lists.
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e065622.full
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More information
Accepted/In Press date: 4 July 2023
e-pub ahead of print date: 19 July 2023
Published date: 19 July 2023
Additional Information:
Funding Information:
This study was funded by the British Heart Foundation, University Hospitals Bristol and Weston Charity and the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol.
Publisher Copyright:
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.
Keywords:
cardiology, cardiac surgery, health policy
Identifiers
Local EPrints ID: 480016
URI: http://eprints.soton.ac.uk/id/eprint/480016
ISSN: 2044-6055
PURE UUID: 76206ce0-baf5-4f5b-b71a-a874f9a08566
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Date deposited: 01 Aug 2023 16:32
Last modified: 06 Jun 2024 01:48
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Contributors
Author:
Salvador Catsis
Author:
Alan R. Champneys
Author:
Jessica Enright
Author:
Katherine Cheema
Author:
Mike Woodall
Author:
Gianni Angelini
Author:
Ramesh Nadarajah
Author:
Chris P. Gale
Author:
Ben Gibbison
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