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10.1007/s10729-020-09511-7 http://scihub22266oqcxt.onion/10.1007/s10729-020-09511-7 C7341703!7341703!32642878     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Health+Care+Manag+Sci 2020 ; 23 (3): 315-24 Nephropedia Template TP {{tp|p=32642878|t=2020. COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care |pdf=|usr=}}{{32642878}} gab.com Text COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care JO: Health Care Manag Sci http://www.kidney.de/pget.php?&tw=1&pmid=32642878 #FOAvip Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these ?capacity-dependent? deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional ?capacity-independent? deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.Electronic supplementary material: The online version of this article (10.1007/s10729-020-09511-7) contains supplementary material, which is available to authorized users. Twit Text FOAVip COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care ????Health Care Manag Sci http://www.kidney.de/pget.php?&tw=1&pmid=32642878 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32642878 #FOAvip English Wikipedia <ref>{{Cite pmid|32642878}}</ref> COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care #MMPMID32642878 Wood RM; McWilliams CJ; Thomas MJ; Bourdeaux CP; Vasilakis C Health Care Manag Sci 2020[]; 23 (3): 315-24 PMID32642878show ga Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these ?capacity-dependent? deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional ?capacity-independent? deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.Electronic supplementary material: The online version of this article (10.1007/s10729-020-09511-7) contains supplementary material, which is available to authorized users. äCOVID-19 scenario modelling for the mitigation of capacity-dependent d(epmc).pdf DeepDyve Pubget Overpricing