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1.  Using prediction to improve elective surgery scheduling 
The Australasian Medical Journal  2013;6(5):287-289.
Background
An ageing population and higher rates of chronic disease increase the demand on health services. The Australian Institute of Health and Welfare reports a 3.6% per year increase in total elective surgery admissions over the past four years.1 The newly introduced National Elective Surgery Target (NEST) stresses the need for efficiency and necessitates the development of improved planning and scheduling systems in hospitals.
Aims
To provide an overview of the challenges of elective surgery scheduling and develop a prediction based methodology to drive optimal management of scheduling processes.
Method
Our proposed two stage methodology initially employs historic utilisation data and current waiting list information to manage case mix distribution. A novel algorithm uses current and past perioperative information to accurately predict surgery duration. A NEST-compliance guided optimisation algorithm is then used to drive allocation of patients to the theatre schedule.
Results
It is expected that the resulting improvement in scheduling processes will lead to more efficient use of surgical suites, higher productivity, and lower labour costs, and ultimately improve patient outcomes.
Conclusion
Accurate prediction of workload and surgery duration, retrospective and current waitlist as well as perioperative information, and NEST-compliance driven allocation of patients are employed by our proposed methodology in order to deliver further improvement to hospital operating facilities.
doi:10.4066/AMJ.2013.1652
PMCID: PMC3674420  PMID: 23745150
Surgery scheduling; Predictive optimisation; Waiting list
2.  Artificial intelligence in health – the three big challenges 
The Australasian Medical Journal  2013;6(5):315-317.
doi:10.4066/AMJ.2013.1758
PMCID: PMC3674424  PMID: 23745154
3.  Advances in artificial intelligence research in health 
The Australasian Medical Journal  2012;5(9):475-477.
doi:10.4066/AMJ.2012.1352
PMCID: PMC3477775  PMID: 23115580
4.  A causal model for fluctuating sugar levels in diabetes patients 
The Australasian Medical Journal  2012;5(9):497-502.
Background
Causal models of physiological systems can be immensely useful in medicine as they may be used for both diagnostic and therapeutic reasoning.
Aim
In this paper we investigate how an agent may use the theory of belief change to rectify simple causal models of changing blood sugar levels in diabetes patients.
Method
We employ the semantic approach to belief change together with a popular measure of distance called Dalal distance between different state descriptions in order to implement a simple application that simulates the effectiveness of the proposed method in helping an agent rectify a simple causal model.
Results
Our simulation results show that distance-based belief change can help in improving the agent’s causal knowledge. However, under the current implementation there is no guarantee that the agent will learn the complete model and the agent may at times get stuck in local optima.
Conclusion
Distance-based belief change can help in refining simple causal models such as the example in this paper. Future work will include larger state-action spaces, better distance measures and strategies for choosing actions.
doi:10.4066/AMJ.2012.1392
PMCID: PMC3477778  PMID: 23115584
Belief Change; Belief Update; Belief Revision; Causal Models; Glucose Metabolism; Diabetes

Results 1-4 (4)