Health and medical research organisations as well as health data custodians (e.g. state-based health services) are under increasing demand to facilitate academic-government collaboration across institutions while meeting stringent privacy, governance, and security requirements.

Yet, building and operating Trusted Research Environments (TREs), also known as Secure Access Environments, independently can be costly, resource-intensive, and difficult to scale.

This is not an individual researcher issue to be solved by competitive grants, but rather an enterprise challenge to be resolved by CIOs.

In this session, QCIF Digital Research is inviting organisations to explore a co-investment consortium model for KeyPoint, a secure, ISO 27001-certified Trusted Research Environment designed to support collaboration on sensitive data across hospitals, universities, government agencies, and research institutes.

KeyPoint’s utility has been proven with complete saturation of the existing compute resources by current research projects, noting its deployment is within a restricted region of the Nectar Research Cloud (Australian Research Data Commons).

Under a consortium investment model of early adopters, we look to develop a containerised version of KeyPoint that can be deployed and self-managed in alternative computing environments, whether for universities, research institutes, or government agencies.

Therefore, organisations can access enterprise-grade TRE capabilities while reducing duplication, lowering costs, and accelerating the adoption of secure research infrastructure across the sector, which in turn will catalyse AI-enabled health innovation in precision medicine and advanced diagnostics.

In this session, we will present the opportunity to partake in a consortium to develop a containerised version of KeyPoint that can be adopted at your respective home organisation, whether it utilises on-premises or cloud computing environments.

In turn, this will allow researchers and data custodians to:

Collaborate without moving sensitive data between institutions;
Keep data secure and under local governance;
With future federated TREs, generate insights from distributed datasets; and
Exploit developing privacy-preserving AI and machine learning through federated machine learning.

Whether your organisation is collaborating across hospitals, universities, and research institutes, or seeking a secure environment for data sharing and analysis within your own institution, KeyPoint provides the secure infrastructure, tools, and governance needed to support data-driven research without compromising privacy, security, or control.

Presenters:

Sach Jayasinghe, Director and CEO, QCIF Digital Research & Chair, Academic Translational Research Network Committee, Australasian Institute for Digital Health
Stephen Bird, Chief Technology Officer & Principal Technologist (Advanced Computing)

About QCIF Digital Research

QCIF Digital Research is a national not-for-profit organisation that partners with academia, government, and industry to accelerate research and innovation through advanced digital infrastructure, data platforms, and specialist expertise.

Our team of more than 40 specialists supports projects across data science, AI and machine learning, bioinformatics, statistics, software engineering, cloud and high-performance computing, sensitive data management, Trusted Research Environments, and more.

Working as an extension of our clients' teams, we provide the people, skills, and technology needed to solve complex challenges and deliver impactful outcomes.

For more information about QCIF Digital Research, visit our website and explore our training opportunities and current projects.

Have questions? You can contact us through our form or send us an email at enquiries@qcif.edu.au.

To keep up-to-date on the latest digital research news and events from QCIF Digital Research, subscribe to QCIF News.

Please note that this event may be recorded and published by the QCIF Digital Research.

Contact the host of An Enterprise Approach to Handling Sensitive Data to Accelerate AI-driven Innovation in Healthcare

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