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Optimizing clinical and public health decision making

In clinical and public health settings, precise knowledge must be combined with increasingly large volumes of data to make rapid decisions in order to treat individuals and protect populations. Current information systems provide data, but offer little guidance in making optimal decisions in these environments. Where decision-support exists in these systems, it tends to be deterministic or rule-based, relying on cross-sectional information. Longitudinal and spatial statistical models and mathematical models of disease and epidemic progression have the capacity to combine complex knowledge with large volumes of data in real-time, but researchers have yet to successfully translate these basic advances into systems that can support decision-making in clinical and public health. The Centre will enable collaboration between researchers in biostatistics (Hanley), computer science (Precup) and medical informatics (Buckeridge, Tamblyn) with clinicians and information systems developers to develop, evaluate and commercialize software with embedded statistical and mathematical models for detection of cases and outbreaks of disease and for decision-support.