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.
Costa
A X
AX
Faculty of Mathematical Studies, University of Southampton, Southampton SO17 1BJ, UK.
Ridley
S A
SA
Shahani
A K
AK
Harper
P R
PR
De Senna
V
V
Nielsen
M S
MS
D001509:Bed Occupancy / Q000706:statistics & numerical data; D003198:Computer Simulation; D003422:Critical Care / Q000458:organization & administration*; D003661:Decision Support Techniques; D004739:England; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data; D006752:Hospital Planning / Q000379:methods*; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data; D008962:Models, Theoretical; D010934:Planning Techniques; D016526:Workload / Q000706:statistics & numerical data