title: Measuring and modelling surgical bed usage.
abstract: Surgical departments treat two groups of inpatients--the simple and the complex--consequently a single average fails to describe the use being made of the occupied beds. Using decision support techniques, we show why indicators such as the average length, the average occupancy and the average admissions mislead. Furthermore, by analysing the fluctuating pattern of weekly admissions we show how weekends and the Christmas holiday periods impact on bed usage. Next, we demonstrate that flow process models can be used to describe how the in-patient workload concerns two groups of patients. On an average day, 71.4% of the beds contained patients who will have an average (exponential) stay of 4.8 days, and the other beds, 28.6%, contain patients who will have an average (exponential) stay of 22.8 days. The article concludes by demonstrating the short and long-term impact on daily admissions of a 10% change in four different parameters of the model. The data used come from a surgical department in Adelaide, as UK data sets report finished consultant episodes rather than completed in-patient spells.
authors.affiliation: Department of Geriatric Medicine, St George's Hospital Medical School, London, UK. p.millard@sghms.ac.uk
authors.forename: P H
authors.initials: PH
authors.lastname: Millard
authors.forename: M
authors.initials: M
authors.lastname: Mackay
authors.forename: C
authors.initials: C
authors.lastname: Vasilakis
authors.forename: G
authors.initials: G
authors.lastname: Christodoulou
mesh_terms: D001509:Bed Occupancy / Q000706:statistics & numerical data*; D003661:Decision Support Techniques; D013502:General Surgery / Q000458:organization & administration / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay; D010351:Patient Discharge / Q000706:statistics & numerical data; D013021:South Australia; D016526:Workload