A clinical decision-support layer over 3.1M anonymised patient records.
We built a decision-support layer that helps Helia's consultants reach diagnoses faster and with more consistency — and we built it under the regulatory and audit constraints of a Class IIa medical device.
Helia operates fifteen diagnostic imaging centres across the United Kingdom. The clinical leadership had identified a measurable variance in time-to-diagnosis between centres — a variance that mapped to consultant tenure, not to case complexity. They wanted to close that gap without removing clinical judgement from the loop. The solution had to be evidence-aware, audit-ready, and certified for clinical use.
Decision-support tools in healthcare are easy to demo and very hard to deploy. The system had to satisfy MHRA classification, integrate with three different RIS/PACS configurations, respect the GMC's good medical practice guidance on AI assistance, never over-state confidence, and — most importantly — be something working consultants would actually open at 02:00 on a Saturday. We had to build for the night shift, not the boardroom.
How we went about it.
“I do not want a tool that argues with me. I want a tool that hands me the right page of evidence at the right moment. That is what they built.”
What it delivered.
If the shape of Helia Diagnostics’s problem rhymes with one of yours, the most useful conversation is rarely an email exchange. We will sit with two of your operators for an hour and tell you whether we can help, whether someone else can help better, or whether the problem is not yet ready to be solved. That conversation is on us.