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Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England
The recovery of elective waiting lists represents a major challenge and priority for the health services of many countries. In England’s National Health Service (NHS), the waiting list has increased by 45% in the two years since the COVID-19 pandemic was declared in March 2020. Long waits associate...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer US
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540081/ https://www.ncbi.nlm.nih.gov/pubmed/36205827 http://dx.doi.org/10.1007/s10729-022-09615-2 |
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author | Wood, Richard M |
author_facet | Wood, Richard M |
author_sort | Wood, Richard M |
collection | PubMed |
description | The recovery of elective waiting lists represents a major challenge and priority for the health services of many countries. In England’s National Health Service (NHS), the waiting list has increased by 45% in the two years since the COVID-19 pandemic was declared in March 2020. Long waits associate with worse patient outcomes and can deepen inequalities and lead to additional demands on healthcare resources. Modelling the waiting list can be valuable for both estimating future trajectories and considering alternative capacity allocation strategies. However, there is a deficit within the current literature of scalable solutions that can provide managers and clinicians with hospital and specialty level projections on a routine basis. In this paper, a model representing the key dynamics of the waiting list problem is presented alongside its differential equation based solution. Versatility of the model is demonstrated through its calibration to routine publicly available NHS data. The model has since been used to produce regular monthly projections of the waiting list for every hospital trust and specialty in England. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at10.1007/s10729-022-09615-2. |
format | Online Article Text |
id | pubmed-9540081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-95400812022-10-11 Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England Wood, Richard M Health Care Manag Sci Current Opinion The recovery of elective waiting lists represents a major challenge and priority for the health services of many countries. In England’s National Health Service (NHS), the waiting list has increased by 45% in the two years since the COVID-19 pandemic was declared in March 2020. Long waits associate with worse patient outcomes and can deepen inequalities and lead to additional demands on healthcare resources. Modelling the waiting list can be valuable for both estimating future trajectories and considering alternative capacity allocation strategies. However, there is a deficit within the current literature of scalable solutions that can provide managers and clinicians with hospital and specialty level projections on a routine basis. In this paper, a model representing the key dynamics of the waiting list problem is presented alongside its differential equation based solution. Versatility of the model is demonstrated through its calibration to routine publicly available NHS data. The model has since been used to produce regular monthly projections of the waiting list for every hospital trust and specialty in England. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at10.1007/s10729-022-09615-2. Springer US 2022-10-07 2022 /pmc/articles/PMC9540081/ /pubmed/36205827 http://dx.doi.org/10.1007/s10729-022-09615-2 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Current Opinion Wood, Richard M Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England |
title | Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England |
title_full | Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England |
title_fullStr | Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England |
title_full_unstemmed | Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England |
title_short | Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England |
title_sort | supporting covid-19 elective recovery through scalable wait list modelling: specialty-level application to all hospitals in england |
topic | Current Opinion |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540081/ https://www.ncbi.nlm.nih.gov/pubmed/36205827 http://dx.doi.org/10.1007/s10729-022-09615-2 |
work_keys_str_mv | AT woodrichardm supportingcovid19electiverecoverythroughscalablewaitlistmodellingspecialtylevelapplicationtoallhospitalsinengland |