Cargando…

New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study

OBJECTIVES: The suspension of elective surgery during the COVID-19 pandemic is unprecedented and has resulted in record volumes of patients waiting for operations. Novel approaches that maximise capacity and efficiency of surgical care are urgently required. This study applies Markov multiscale comm...

Descripción completa

Detalles Bibliográficos
Autores principales: Clarke, Jonathan, Murray, Alice, Markar, Sheraz Rehan, Barahona, Mauricio, Kinross, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7783383/
https://www.ncbi.nlm.nih.gov/pubmed/33130573
http://dx.doi.org/10.1136/bmjopen-2020-042392
_version_ 1783632101589909504
author Clarke, Jonathan
Murray, Alice
Markar, Sheraz Rehan
Barahona, Mauricio
Kinross, James
author_facet Clarke, Jonathan
Murray, Alice
Markar, Sheraz Rehan
Barahona, Mauricio
Kinross, James
author_sort Clarke, Jonathan
collection PubMed
description OBJECTIVES: The suspension of elective surgery during the COVID-19 pandemic is unprecedented and has resulted in record volumes of patients waiting for operations. Novel approaches that maximise capacity and efficiency of surgical care are urgently required. This study applies Markov multiscale community detection (MMCD), an unsupervised graph-based clustering framework, to identify new surgical care models based on pooled waiting-lists delivered across an expanded network of surgical providers. DESIGN: Retrospective observational study using Hospital Episode Statistics. SETTING: Public and private hospitals providing surgical care to National Health Service (NHS) patients in England. PARTICIPANTS: All adult patients resident in England undergoing NHS-funded planned surgical procedures between 1 April 2017 and 31 March 2018. MAIN OUTCOME MEASURES: The identification of the most common planned surgical procedures in England (high-volume procedures (HVP)) and proportion of low, medium and high-risk patients undergoing each HVP. The mapping of hospitals providing surgical care onto optimised groupings based on patient usage data. RESULTS: A total of 7 811 891 planned operations were identified in 4 284 925 adults during the 1-year period of our study. The 28 most common surgical procedures accounted for a combined 3 907 474 operations (50.0% of the total). 2 412 613 (61.7%) of these most common procedures involved ‘low risk’ patients. Patients travelled an average of 11.3 km for these procedures. Based on the data, MMCD partitioned England into 45, 16 and 7 mutually exclusive and collectively exhaustive natural surgical communities of increasing coarseness. The coarser partitions into 16 and seven surgical communities were shown to be associated with balanced supply and demand for surgical care within communities. CONCLUSIONS: Pooled waiting-lists for low-risk elective procedures and patients across integrated, expanded natural surgical community networks have the potential to increase efficiency by innovatively flexing existing supply to better match demand.
format Online
Article
Text
id pubmed-7783383
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-77833832021-01-07 New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study Clarke, Jonathan Murray, Alice Markar, Sheraz Rehan Barahona, Mauricio Kinross, James BMJ Open Health Policy OBJECTIVES: The suspension of elective surgery during the COVID-19 pandemic is unprecedented and has resulted in record volumes of patients waiting for operations. Novel approaches that maximise capacity and efficiency of surgical care are urgently required. This study applies Markov multiscale community detection (MMCD), an unsupervised graph-based clustering framework, to identify new surgical care models based on pooled waiting-lists delivered across an expanded network of surgical providers. DESIGN: Retrospective observational study using Hospital Episode Statistics. SETTING: Public and private hospitals providing surgical care to National Health Service (NHS) patients in England. PARTICIPANTS: All adult patients resident in England undergoing NHS-funded planned surgical procedures between 1 April 2017 and 31 March 2018. MAIN OUTCOME MEASURES: The identification of the most common planned surgical procedures in England (high-volume procedures (HVP)) and proportion of low, medium and high-risk patients undergoing each HVP. The mapping of hospitals providing surgical care onto optimised groupings based on patient usage data. RESULTS: A total of 7 811 891 planned operations were identified in 4 284 925 adults during the 1-year period of our study. The 28 most common surgical procedures accounted for a combined 3 907 474 operations (50.0% of the total). 2 412 613 (61.7%) of these most common procedures involved ‘low risk’ patients. Patients travelled an average of 11.3 km for these procedures. Based on the data, MMCD partitioned England into 45, 16 and 7 mutually exclusive and collectively exhaustive natural surgical communities of increasing coarseness. The coarser partitions into 16 and seven surgical communities were shown to be associated with balanced supply and demand for surgical care within communities. CONCLUSIONS: Pooled waiting-lists for low-risk elective procedures and patients across integrated, expanded natural surgical community networks have the potential to increase efficiency by innovatively flexing existing supply to better match demand. BMJ Publishing Group 2020-10-31 /pmc/articles/PMC7783383/ /pubmed/33130573 http://dx.doi.org/10.1136/bmjopen-2020-042392 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Health Policy
Clarke, Jonathan
Murray, Alice
Markar, Sheraz Rehan
Barahona, Mauricio
Kinross, James
New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study
title New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study
title_full New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study
title_fullStr New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study
title_full_unstemmed New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study
title_short New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study
title_sort new geographic model of care to manage the post-covid-19 elective surgery aftershock in england: a retrospective observational study
topic Health Policy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7783383/
https://www.ncbi.nlm.nih.gov/pubmed/33130573
http://dx.doi.org/10.1136/bmjopen-2020-042392
work_keys_str_mv AT clarkejonathan newgeographicmodelofcaretomanagethepostcovid19electivesurgeryaftershockinenglandaretrospectiveobservationalstudy
AT murrayalice newgeographicmodelofcaretomanagethepostcovid19electivesurgeryaftershockinenglandaretrospectiveobservationalstudy
AT markarsherazrehan newgeographicmodelofcaretomanagethepostcovid19electivesurgeryaftershockinenglandaretrospectiveobservationalstudy
AT barahonamauricio newgeographicmodelofcaretomanagethepostcovid19electivesurgeryaftershockinenglandaretrospectiveobservationalstudy
AT kinrossjames newgeographicmodelofcaretomanagethepostcovid19electivesurgeryaftershockinenglandaretrospectiveobservationalstudy
AT newgeographicmodelofcaretomanagethepostcovid19electivesurgeryaftershockinenglandaretrospectiveobservationalstudy