Turning Local Codes into a Common Language

If you’ve ever tried to match up information from two different hospital systems, you know the problem. The same clinical detail might be labelled in completely different ways. It works fine inside each hospital. But the moment you try to share it, the meaning can get lost.

This is exactly the kind of challenge Ontoserver was built to solve. Inside the Dedalus ecosystem, it quietly gets to work, taking those local codes and linking them to global standards like SNOMED CT and RxNorm. No endless spreadsheets, no guesswork. The system does the mapping, and then medical terminology experts check it before it’s put to use.

On the Trial for Care platform, the results have been hard to ignore. Mapping rates have climbed by 42 per cent. The hours spent on manual coding have dropped. And the datasets that come out at the other end are far more consistent and far easier to work with.

For researchers, that means less time fixing data and more time using it, building patient cohorts, running queries, or contributing to studies that need clean, standardised information.

And this is just one example. In the full article, you’ll find how a mix of automation, clever technical upgrades and a tool for exploring concepts is helping Dedalus move healthcare a step closer to true interoperability.