Mathematical modelling and simulation for planning critical care capacity
Mathematical modelling and simulation for planning critical care capacity
Using average number of patients expected in a year, average length of stay and a target occupancy level to calculate the number of critical care beds needed is mathematically incorrect because of nonlinearity and variability in the factors that control length of stay. For a target occupancy in excess of 80%, this simple calculation will typically underestimate the number of beds required.
More seriously, it provides no quantitative guidance information about other aspects of critical care demand such as the numbers of emergency patients transferred, deferral rates for elective patients and overall utilisation. The combination of appropriately analysing raw data and detailed mathematical modelling provides a much better method for estimating numbers of beds required. We describe this modelling approach together with evidence of its performance.
intensive care: organisation and administration, trends, utilisation
320-327
Costa, A.X.
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Ridely, S.A.
2759ad1e-2aba-4036-b179-db5920cef0c4
Shahani, A.K.
01f30d3f-6d62-4c19-8a3e-e4d223559dc7
Harper, P.R.
e9853fed-d08b-4041-8d1e-c170fb1949f7
De Senna, V.
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Nielsen, M.S.
b1d9bdb4-7251-49fb-8b98-9b87e1d5bce5
2003
Costa, A.X.
3db9ec2e-4a0c-4310-b9e1-d0c1cb21e66f
Ridely, S.A.
2759ad1e-2aba-4036-b179-db5920cef0c4
Shahani, A.K.
01f30d3f-6d62-4c19-8a3e-e4d223559dc7
Harper, P.R.
e9853fed-d08b-4041-8d1e-c170fb1949f7
De Senna, V.
97754892-ac68-4e10-bacc-499275c22b39
Nielsen, M.S.
b1d9bdb4-7251-49fb-8b98-9b87e1d5bce5
Costa, A.X., Ridely, S.A., Shahani, A.K., Harper, P.R., De Senna, V. and Nielsen, M.S.
(2003)
Mathematical modelling and simulation for planning critical care capacity.
Anaesthesia, 58 (4), .
(doi:10.1046/j.1365-2044.2003.03042.x).
Abstract
Using average number of patients expected in a year, average length of stay and a target occupancy level to calculate the number of critical care beds needed is mathematically incorrect because of nonlinearity and variability in the factors that control length of stay. For a target occupancy in excess of 80%, this simple calculation will typically underestimate the number of beds required.
More seriously, it provides no quantitative guidance information about other aspects of critical care demand such as the numbers of emergency patients transferred, deferral rates for elective patients and overall utilisation. The combination of appropriately analysing raw data and detailed mathematical modelling provides a much better method for estimating numbers of beds required. We describe this modelling approach together with evidence of its performance.
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Published date: 2003
Keywords:
intensive care: organisation and administration, trends, utilisation
Organisations:
Operational Research
Identifiers
Local EPrints ID: 29692
URI: http://eprints.soton.ac.uk/id/eprint/29692
ISSN: 0003-2409
PURE UUID: 773fc407-c29b-48d8-9544-a59dc0bc2a11
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Date deposited: 12 May 2006
Last modified: 15 Mar 2024 07:34
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Contributors
Author:
A.X. Costa
Author:
S.A. Ridely
Author:
A.K. Shahani
Author:
P.R. Harper
Author:
V. De Senna
Author:
M.S. Nielsen
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