Literature DB >> 25267582

Mathematical modelling of patient flows to predict critical care capacity required following the merger of two district general hospitals into one.

J Williams1, S Dumont, J Parry-Jones, I Komenda, J Griffiths, V Knight.   

Abstract

There is both medical and political drive to centralise secondary services in larger hospitals throughout the National Health Service. High-volume services in some areas of care have been shown to achieve better outcomes and efficiencies arising from economies of scale. We sought to produce a mathematical model using the historical critical care demand in two District General Hospitals to determine objectively the requisite critical care capacity in a newly built hospital. We also sought to determine how well the new single unit would be able to meet changes in demand. The intention is that the model should be generic and transferable for those looking to merge and rationalise services on to one site. One of the advantages of mathematical modelling is the ability to interrogate the model to investigate any number of different scenarios; some of these are presented.
© 2014 The Association of Anaesthetists of Great Britain and Ireland.

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Year:  2014        PMID: 25267582     DOI: 10.1111/anae.12839

Source DB:  PubMed          Journal:  Anaesthesia        ISSN: 0003-2409            Impact factor:   6.955


  3 in total

1.  Using probabilistic patient flow modelling helps generate individualised intensive care unit operational predictions and improved understanding of current organisational behaviours.

Authors:  George Hadjipavlou; Jill Titchell; Christina Heath; Richard Siviter; Hilary Madder
Journal:  J Intensive Care Soc       Date:  2019-09-05

2.  Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges.

Authors:  Elizabeth Williams; Tamas Szakmany; Izabela Spernaes; Babu Muthuswamy; Penny Holborn
Journal:  Crit Care Explor       Date:  2020-09-14

3.  Recursive neural networks in hospital bed occupancy forecasting.

Authors:  Ekaterina Kutafina; Istvan Bechtold; Klaus Kabino; Stephan M Jonas
Journal:  BMC Med Inform Decis Mak       Date:  2019-03-07       Impact factor: 2.796

  3 in total

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