The Veterinary and Animal Research Data Commons (VARDC) project builds on the success of VetCompass Australia; a collaboration between all seven Australian vet schools, which collates electronic patient records from veterinary practices nationally and aggregates clinical data for researchers to interrogate.
The Veterinary and Animal Research Data Commons project will transform VetCompass Australia to create a platform that is a single point-of-access to multiple, related systems and data types. It will facilitate collaboration with Australia’s leading three veterinary pathology providers to ingest pathology reports and work with the Australian Imaging Service to host images alongside clinical data.
As a world-leading veterinary database, it has advantages over current human public health initiatives in that there are fewer privacy concerns for animals than for humans. This facilitates capacity for development of geospatial disease surveillance and text mining projects that boost human health outcomes.
Who is this project for?
- Veterinary research organisations
- Researchers, lecturers and students
- Professionals working within the veterinary environment, from clinicians to pathology providers
- Industry professionals (eg vaccine manufacturers)
- People with companion animals
- Policy-makers and government
What does this project enable?
The VARDC platform will deliver a framework that shows researchers how to design sample-based and big-data studies and provide optimised access to the data. For the first time, pathology providers and clinical researchers will have a comprehensive data set that includes full clinical histories to assist in setting points of reference and standard intervals, assessing survival data and treatment interventions. By improving the accuracy of their research, disease surveillance will be improved as will health outcomes for pets and improved diagnostic efficiency (economic and social benefit to owners). By capturing search terms and categorising identified pathology data in successive studies we will lay the groundwork for searches enhanced by machine learning.