Advanced Analytics and AI Resource Hub

The Challenge

Stage 1 of our Advanced Analytics in Healthcare activity revealed a fragmented landscape for advanced analytics and AI in health research, with limited availability of coordinated infrastructure, tools, and support. It identified the need for nationally scaled solutions that are accessible, reusable, and aligned with ethical and governance requirements. Key gaps included: 

• absence of shared repositories for analytics tools
• lack of training resources tailored to the research context
• challenges in accessing data environments that support responsible AI development. 

This project responds directly to these gaps by implementing the frameworks, use cases and recommendations developed through Stage 1.

The Response

Stage 1 of our Advanced Analytics in Healthcare activity brought together national expertise through co-design workshops and the Pathfinder Project to develop a shared framework for advanced analytics in healthcare. It identified key infrastructure, tools, and governance needs to support the responsible use of AI in health research.

The Advanced Analytics and AI Resource Hub moves from planning into active implementation, transforming a shared national vision into a practical, reusable platform. The project is evaluating, curating and recommending a suite of tools, data and socio-technical assets to support real-world health research.

Led by QUT’s Centre for Data Science, the project will deliver across 7 work packages. These include:

• establishing coordination structures
• developing repositories for tools and data
• supporting trusted access to analytics environments
• embedding ethical and governance frameworks
• delivering training tailored to researchers.

The infrastructure will be deployed via the ARDC Nectar Research Cloud, ensuring national-scale accessibility, scalability, and alignment with the ARDC’s reference architecture.

We envision the full realisation of an Advanced Analytics and AI Resource Hub as part of the People Research Data Commons stack, as outlined in the reference architecture. This includes designing a functional, user-friendly, and widely adopted infrastructure that meets the needs of the research community. The stack should deliver necessary and sufficient capabilities to support advanced analytics, with provision for continuous monitoring, reporting, and maintenance so resources remain relevant and fit for purpose for beneficiaries over time.

Who Will Benefit

The Advanced Analytics and AI Resource Hub will support a broad community of researchers and professionals working in health and data science.

• The Hub will benefit healthcare domain researchers beginning to adopt advanced analytics, and computational health researchers such as epidemiologists and data science researchers developing new analytical methods for health contexts. The Hub will provide access to tools, training, and trusted infrastructure designed to lower barriers and accelerate responsible, scalable research.
• Early-career researchers across health and allied health disciplines will benefit from foundational resources and skills development opportunities. 
• Data and analytics infrastructure managers will gain access to reusable, standards-aligned components that support integration and long-term sustainability.

The Partners

The ARDC is delivering this project in partnership with 3 co-investment partners:

• Queensland University of Technology (QUT), which, through its Centre for Data Science, leads the project and brings national expertise in data science, digital health, and research infrastructure
• University of Technology Sydney (UTS), which is contributing strengths in applied analytics, AI, and technology-driven innovation for societal impact
• Curtin University, which is providing expertise in digital health and data infrastructure, and supporting the development of scalable, research-ready solutions.

Together, the partners are working to deliver a nationally coordinated platform that supports the responsible and practical use of AI in health research.

This project will build a national Advanced Analytics and AI Resource Hub that supports health researchers.

Target Outcomes

Key outputs of this project will include:

• evaluation of a suite of AI and analytics tools designed for health research use cases
• curated data assets and resources made accessible through trusted environments
• a set of governance and ethical frameworks to support responsible AI application
• researcher training materials and guidance to build national capability
• reusable technical components aligned with the ARDC’s reference architecture
• a sustainable model for ongoing coordination and national delivery.

In an upcoming project, these outputs will be deployed via the ARDC Nectar Research Cloud and made available for reuse by institutions, research projects, and infrastructure providers across the sector.