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Forecasting & Scheduling

Overcrowding in hospitals has been labelled an "international crisis", and significantly impacts the quality of care delivered and the quality of the patient experience. This research focuses on improving the management of hospital resources through applied prediction and scheduling tools.

Australia has one of the highest per capita spending on healthcare of the OECD industrialized nations at around 10% of GDP.  States are being 'overwhelmed' by rising health costs.  Without productivity improvements, i.e. doing more with fewer resources, the ability of states to provide the services they currently provide will be significantly strained.  Despite unprecedented levels of spending, preventable medical errors abound, uncoordinated care continues to frustrate patients and providers, and healthcare costs continue to rise.

Our acute public hospitals are plagued by similar problems: slow throughput times, long perceived waiting periods, limited surge capacity, technology integration challenges, efficiency bottlenecks, antiquated treatment spaces, lack of proper integration with community, and limited capacity to respond to catastrophic events.

Hospital executives and department directors are balancing a range of competing priorities on a daily basis including increased patient acuity, staffing shortages, delayed throughput, system bottlenecks, advances in technology and data management systems, emergency preparedness and facility maintenance.  A radical re-think of the current organisation and configuration of hospitals is required to ensure that the required efficiency savings can be achieved without compromising quality.

Our work in this area supports acute hospitals by applying evidence-driven strategies to support improved health outcomes.  For example, hospital overcrowding and timing of discharge are commonly linked to sub-optimal patient flow, poor quality of care, and unnecessary mortality.  Consequently hospital services subscribe to theoretical targets for occupancy levels and discharge times.  A better understanding of how occupancy levels and discharge times influence patient flow parameters, and more precise targets based on these, derived through modelling and simulation, would improve capacity management strategies and care outcomes.

We have several research activities in this area which are expected to enable hospitals to better manage their resources and hence reduce waiting times for patients:

  • Planning and Optimisation - predicting Emergency Department and inpatient bed demand and assisting in bed management and elective surgery decisions.
  • Patient Flow Analytics - forming an evidence base around increasing the knowledge of acute hospital capacity management and inpatient bed requirements to meet community demands.
  • Readmission Risk Prediction – developing a mechanism to "flag" chronic disease patients who have a high probability of subsequent emergency admissions so targeted interventions can be provided to reduce hospital admissions and improve health outcomes for patients with chronic conditions.

Last Updated on Monday, 10 October 2011 13:40

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