About the Project
Over the last few decades, advanced technologies have greatly improved the diagnosis and management of health conditions in a variety of ways. One important consequence of improved technology has been that the collection and analysis of a large amount of diverse health related information has become possible. Much of this data would otherwise have remained unused, despite the extensive cost associated with its collection. If organised and analysed appropriately, could assist in monitoring the use of, and need for, health services. By integrating multiple types of data, technology can facilitate the production of meaningful outcomes for policy-makers and service designers. Epidoros V2 can be a spatial data repository that allows the organised and meaningful collation of information from multiple data sources. It can allow s the simultaneous analysis of multiple datasets to examine service usage, demand, gaps and needs. The data framework is presented as a matrix and the cells of the matrix are populated information gathered from open data sources, protected administrative information sought from data custodians, new survey data gathered from service users and unstructured public and personal datasets (e.g., social media, mobile apps and wearable sensors). All the data used will be cleaned, de-identified and stored in a centralized server, spatially geocoded, linked with other datasets where possible and aggregated at multiple levels to ensure privacy.
This matrix will allow the integration of multiple datasets, all linked to a common spatial reference point. Additional datasets can be added as they become available and the data repository can be securely shared with the wider research and service planning community for data mining.
A range of tools are made available to those who wish to use the dataset, including geographical information systems tools, process mining and modelling programs. Overall aim is to ensure that this data and the findings from various analyses can be used to inform system changes and can become embedded into the existing system as a core ongoing dataset that can track trajectories over time.
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