Adaptive Health Resource Networks

A focus of the ASN team's activity is in the study and development of Non-Clinical Health Decision Support Systems that in the future will form the backbone of Adaptive Health Resource Networks. We carry out whole of system research in the area of health service operational efficiency. The work involves research into patient flow modelling, health network modelling, and demand modelling and management, with the specific aim of developing decision support tool for planning and scheduling the resource allocations needed to best meet current and future patient needs for health services.

The Australian health system attempts to provide a level of service that meets community health needs within the limits imposed by Government budgets. Meeting this challenge requires the ability to optimise operational performance across hospitals, regional health networks and state health networks. Some of the factors involved in optimising performance at a whole of system include identifying demand via epidemiological forecasting, determining the spatial and temporal allocation of resources to meet this demand and scheduling the provision of these resources to best meet the demand.

Very little work has been done within Australian Health departments on this type of systems modelling and there is a great opportunity to make a significant contribution to improving operational performance at the different levels noted above using a team comprising mathematical modellers, clinicians and health professionals. The nature of the health system suggests that contributions from all these groups are needed to ensure that new methods are actually implemented in the system.

A major goal of the project is to form strategic alliances with regional integrated health service networks and to use them to provide new tools, based on sound mathematical analysis, for use in managing and improving resource allocations in the health system. These decision systems support management in areas such as: the allocation of beds to medical programs, the scheduling of operating theatres, the assignment of patients to beds in wards, and the flow of patients between facilities, in a close to optimal manner.

The particular mathematical challenge is to develop fast stable network predictive, assignment and scheduling algorithms that can seamlessly deal with the different degrees of uncertain behaviour at the different system levels and to provide planners at each level with tools for evaluating the best configurations for meeting patient needs.

For further information, please contact David Sier.

Page last updated February 13, 2009 02:09 PM.
ASN sub-site maintained by Simon Dunstall.