CHESS will investigate, develop, and evaluate new algorithms and models to support the acquisition to exploitation lifecycle for “data” in Connected Health applications.
Data must be sourced and captured at all points in the care pathway from all players and relevant extrinsic elements.
This data, often “big data”, must be aggregated and integrated with a view to its ultimate delivery in a valuable format to either the patient, a healthcare professional, a policy-maker, an informal care-giver, or a health system manager.
CHESS will consider the tasks and projects that represent the current needs for data in Connected Health.
There are key issues that arise at the different stages in the Connected Health data lifecycle, and CHESS will investigate novel methods for data acquisition such as non-invasive measurement of parameters traditionally requiring invasive or intrusive techniques.
CHESS will also investigate new algorithms for more accurate processing and better understanding of Connected Health data, such as large volume or “big” data techniques.
CHESS will use the findings of the research to assist in the development of public health policies by gaining better understanding around the implications of data.