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10.1136/bmjopen-2020-042392 http://scihub22266oqcxt.onion/10.1136/bmjopen-2020-042392 33130573!7783383!33130573     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33130573&cmd=llinks): Failed to open stream: HTTP request failed! 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New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study |pdf=|usr=}}{{33130573}} gab.com Text New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study JO: BMJ Open http://www.kidney.de/pget.php?&tw=1&pmid=33130573 #FOAvip 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. Twit Text FOAVip New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study ????BMJ Open http://www.kidney.de/pget.php?&tw=1&pmid=33130573 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33130573 #FOAvip English Wikipedia <ref>{{Cite pmid|33130573}}</ref> New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study #MMPMID33130573 Clarke J; Murray A; Markar SR; Barahona M; Kinross J BMJ Open 2020[Oct]; 10 (10): e042392 PMID33130573show ga 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. |*Markov Chains[MESH] |*Models, Organizational[MESH] |*Pandemics[MESH] |*Waiting Lists[MESH] |Adult[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Community Networks/organization & administration[MESH] |Coronavirus Infections/epidemiology[MESH] |Efficiency, Organizational[MESH] |Elective Surgical Procedures/classification/*statistics & numerical data[MESH] |England/epidemiology[MESH] |Health Services Accessibility[MESH] |Humans[MESH] |Intersectoral Collaboration[MESH] |Pneumonia, Viral/epidemiology[MESH] |Retrospective Studies[MESH] |Risk Assessment[MESH] |SARS-CoV-2[MESH]New geographic model of care to manage the post-COVID-19 elective surg(epmc).pdf DeepDyve Pubget Overpricing