A new geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study
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Published version
Author(s)
Clarke, Jonathan
Murray, Alice
Markar, sheraz
Barahona, mauricio
Kinross, james
Type
Journal Article
Abstract
Objectives
The suspension of elective surgery during the COVID 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 1st April 2017 and 31st 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 one-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 7 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.
The suspension of elective surgery during the COVID 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 1st April 2017 and 31st 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 one-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 7 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.
Date Issued
2020-10-31
Date Acceptance
2020-09-30
Citation
BMJ Open, 2020, 10 (10), pp.1-9
ISSN
2044-6055
Publisher
BMJ Journals
Start Page
1
End Page
9
Journal / Book Title
BMJ Open
Volume
10
Issue
10
Copyright Statement
© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. 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/.
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/.
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Wellcome Trust
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Identifier
https://bmjopen.bmj.com/content/10/10/e042392.info
Grant Number
EP/N014529/1
UNS81609 - 215938/Z/19/Z
RDB04 79560
RD207
Subjects
Science & Technology
Life Sciences & Biomedicine
Medicine, General & Internal
General & Internal Medicine
surgery
health policy
organisation of health services
public health
WAITING-LISTS
CHOICE
MARKET
TIMES
health policy
organisation of health services
public health
surgery
Adult
Betacoronavirus
COVID-19
Community Networks
Coronavirus Infections
Efficiency, Organizational
Elective Surgical Procedures
England
Health Services Accessibility
Humans
Intersectoral Collaboration
Markov Chains
Models, Organizational
Pandemics
Pneumonia, Viral
Retrospective Studies
Risk Assessment
SARS-CoV-2
State Medicine
Waiting Lists
PanSurg Collaborative
Humans
Pneumonia, Viral
Coronavirus Infections
Markov Chains
Risk Assessment
Retrospective Studies
Models, Organizational
Community Networks
Adult
State Medicine
Waiting Lists
Efficiency, Organizational
Health Services Accessibility
England
Pandemics
Elective Surgical Procedures
Intersectoral Collaboration
Betacoronavirus
COVID-19
SARS-CoV-2
1103 Clinical Sciences
1117 Public Health and Health Services
1199 Other Medical and Health Sciences
Publication Status
Published
Date Publish Online
2020-10-31