Austin Health (AH) currently operates inpatient Continuing Care services across two satellite campuses, and faces the problem of calculating the optimal number of beds needed in its subacute wards. It is inefficient if a patient needs to wait in an acute ward for a bed in a subacute ward, just as is operating too many beds in the subacute ward to always meet demand. In recent years the demand for acute beds has increased, creating pressure for faster patient movements and more admissions at times of high demand, with the consequent need to determine best-performing bed configurations. Additional constraints, related mostly to availability of medical resources, were of concern to AH staff and were considered when developing the model. The subacute bed allocation problem is significant because it cannot be formulated in closed form using simple probability distributions, but demands the use of actual variable data on admissions and separations to ensure a reliable result. The solution approach we used to tackle the problem is based on the combined use of the cross-entropy method for optimisation. It uses the simulation of subacute ward occupation and demand using a parametric bootstrap to generate data to solve this problem. We used a simulation model to represent the six wards under study and the dynamic relationships that describe this system. To obtain the optimal bed configurations, we use the cross-entropy method for optimisation. This is a modern optimisation method whose working principle is based on the fact that cross-entropy divergence can be used as a measure of closeness between two sampling distributions. Optimisation by cross-entropy estimates a sequence of parametric sampling distributions that converges to a distribution with probability mass concentrated in a region of near-optimal solutions. We used a parametric bootstrapping approach to generate the admission data that is used as an input to the optimiser. The expected result is a slight increase in the number of existing beds. We justify the effectiveness of the proposed approach for determining the optimal number of beds on the grounds that the actual results and the general behaviour of the optimisation software in its current version match the intuition of hospital staff on the behaviour of the system.
|Title of host publication
|MODSIM2015, 21st International Congress on Modelling and Simulation
|Tony Weber, Malcolm J. McPhee, Robert S. Anderssen
|Modelling and Simulation Society of Australia and New Zealand (MSSANZ)
|Number of pages
|Published - 2015
|International Congress on Modelling and Simulation 2015: Partnering with industry and the community for innovation and impact through modelling - Gold Coast Convention and Exhibition Centre, Broadbeach, Australia
Duration: 29 Nov 2015 → 4 Dec 2015
Conference number: 21st
|International Congress on Modelling and Simulation 2015
|29/11/15 → 4/12/15
|The 21st International Congress on Modelling and Simulation (MODSIM2015) was held at the Gold Coast Convention and Exhibition Centre, Broadbeach, Queensland, Australia from Sunday 29 November to Friday 4 December 2015.
It was held jointly with the 23rd National Conference of the Australian Society for Operations Research and the DSTO led Defence Operations Research Symposium (DORS 2015).
The theme for this event was Partnering with industry and the community for innovation and impact through modelling.
Papers from these proceedings should be cited using this format:
Walmsley, B.J., Oddy, V.H., Gudex, B.W., Mayer, D.G. and McPhee, M.J. (2015). Transformation of the BeefSpecs fat calculator: Addressing eating quality and production efficiency with on-farm decision making. In Weber, T., McPhee, M.J. and Anderssen, R.S. (eds) MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2015, pp. 490–496. ISBN: 978-0-9872143-5-5. www.mssanz.org.au/modsim2015/B4/walmsley.pdf
- Cross-entropy optimisation
- Hospital capacity planning
- Parametric bootstrap
- Subacute bed allocation