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


Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
31 10 2020
Historique:
entrez: 1 11 2020
pubmed: 2 11 2020
medline: 20 11 2020
Statut: epublish

Résumé

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. Retrospective observational study using Hospital Episode Statistics. Public and private hospitals providing surgical care to National Health Service (NHS) patients in England. All adult patients resident in England undergoing NHS-funded planned surgical procedures between 1 April 2017 and 31 March 2018. 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. 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. 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.

Identifiants

pubmed: 33130573
pii: bmjopen-2020-042392
doi: 10.1136/bmjopen-2020-042392
pmc: PMC7783383
doi:

Types de publication

Journal Article Observational Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e042392

Informations de copyright

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: JK reports consultancy for Verb robotics/Ethicon and is a shareholder of OneWelbeck Day Surgery.

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Auteurs

Jonathan Clarke (J)

Department of Mathematics, Imperial College of Science, Technology and Medicine, London, UK j.clarke@imperial.ac.uk.

Alice Murray (A)

Department of Surgery and Cancer, Imperial College of Science, Technology and Medicine, London, UK.

Sheraz Rehan Markar (SR)

Department of Surgery and Cancer, Imperial College of Science, Technology and Medicine, London, UK.

Mauricio Barahona (M)

Department of Mathematics, Imperial College of Science, Technology and Medicine, London, UK.

James Kinross (J)

Department of Surgery and Cancer, Imperial College of Science, Technology and Medicine, London, UK.

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Classifications MeSH