A Mixed Integer Programming Model to Analyse and Optimise Patient Flow in a Surgical Suite.

Demand for healthcare services is growing rapidly in Australia and across the world, and rising healthcare expenditure is increasing pressure on sustainability of government-funded healthcare systems. In Australia, elective surgery waiting lists are growing and hospitals are struggling with a capacity shortage. To keep up with the rising demand, we need to be more efficient in delivering surgical services. Although it is appropriate to model emergency patient flow as a queuing network, elective patient flow in a surgical suite is quite different. For example, the hospital management can adjust the elective patient arrival process to get a better throughput, or they can decide to not to operate on any elective surgery patient over weekends. A simulation model can be used as an alternative to a queuing model, however, it is difficult to make optimal decisions using a simulation model. In this paper, we develop a mixed integer programming (MIP) model to analyse and optimise elective surgery patient flow in a surgical suite. We will use the model to develop stochastically optimal surgery schedules for various length of stay scenarios. We will also analyse the effect of varying the penalty factor, an input parameter to control cancellations, on the occupancy level and the cancellation rate. Finally, we develop an optimal master surgery schedule to maximise the utilisation level while keeping the cancellation rate within acceptable limits.


University of Melborne, June 2017.