Our team specialises in working with organisations to bring digital health innovations into clinical settings. Our collaborators range from start-ups and scale-ups to enterprises, health services, and government agencies.
Our research platform comprises translational scientists, research methods expertise, and our state-of-the-art Validitron clinical simulation lab and digital sandbox.
We work with collaborators to de-risk the integration of promising digital technologies into routine clinical practice – helping you build the evidence, confidence, and clarity needed to move forward.
We can help you:
- Move from first concepts to scale-ready solutions.
- Demonstrate the clinical efficacy of your product.
- Get clarity on compliance and regulatory pathways.
- Successfully navigate your most pressing usability challenges.
– Professor Justin Yeung, Founder, PredicTx |  |
How we can help
Evidence-driven problem validation and market access
Our process is all about efficiently mapping local clinical problems, wants, and workflows. You'll walk away with clear, locally relevant proof points to enter and grow in Australian and APAC markets.
Clinical usability and proofing
Whether you have a prototype, MVP or are fully launched, if clinicians can’t use your solution easily, it won’t scale. We bring lean but rigorous usability testing in realistic workflows to uncover friction, safety risks and “won’t use” moments early. The output is a concrete fix list, plus clinically credible evidence that your product is usable, acceptable, and ready for adoption.
Consumer and patient insights
Steer product and go-to-market decisions with early signals on what drives user trust, engagement, and drop-off — without burning budget on full-scale validation too early. Our leading consumer involvement and equity approach allows us to validate user journeys, messaging, and product features.
AI adoption and integration
To make sure your AI tool actually gets used, it needs to safely fit into real systems, roles, and workflows. Our AI human factors implementation frameworks identify where your tool helps, slows things down, and introduces new risks. We then provide a practical adoption plan addressing integration patterns, change management, training and safety guardrails.
Impact stories
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Improving patient experience of telehealth with a consultation summary tool
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PredicTx: AI-powered chemotherapy precision dosing for colorectal cancer
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Impact Evaluation of a Digital Coordination Centre in a tertiary hospital
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Evaluation of AI-enabled transcription for hospital use through simulation testing
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Optimisation of an opportunistic mental health screening care model for general practice
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An AI-powered Learning Health System platform for Hospital Surveillance of invasive fungal infections
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Funding mechanisms for preventative health apps in primary care
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Simulation-primed co-design and workflow validation of a digitised Medical Attendance Statement
Why de-risking matters
De-risking is critical to avoid failure in digital health. These case studies highlight real-world failures globally and examine the critical gaps that allowed them to happen.
⚠︎ Deployed worldwide. Validated nowhere.
Epic's sepsis prediction algorithm missed two in every three cases. Pre-implementation validation would have caught it.
Read more⚠︎ Every user got the same diagnosis.
At least one Alzheimer's screening app returned a positive result regardless of what the user entered. Clinical oversight at the design stage would have caught it.
Read more⚠︎ Top rated. Clinically wrong.
The Instant Blood Pressure app was a bestseller — and it systematically underreported elevated readings. Independent validation would have caught it.
Read moreDelivery partners
