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A healthcare data practice

Healthcare data, platforms, and integrations in practice

DEXPRA works on healthcare data where it actually breaks: between systems. We build interoperability, data platforms, and clinical AI for healthcare organisations, and what we learn doing it goes here in the open, along with the tools we built to do it.

Integrated health data architecture, in four stages left to right. The clinical environment supplies the data: EHR, PACS, LIS and scanners. Those feed an interoperability and standards translation layer holding FHIR for APIs, profiles and terminology; HL7 v2 for legacy and current messaging; DICOM and DICOMweb for imaging data; and IHE for scheduled workflows and MADO. The standards dictate the rules of conversation between systems. Standardised clinical data then passes into the healthcare data platform at the core, which holds the architecture — canonical models, APIs and eventing — governance covering identity and data lineage, and the platform as the evidence package for training data and traceability. The platform provides governed data to applied clinical AI: generative AI using LLMs, RAG and agentic workflows; imaging AI deployments; and evaluation design and FDA clearance. Applied AI requires six handoffs back through the standards, and returns AI insights into clinical workflow and charting. A final edge runs from clinical workflow all the way back to IHE, so AI output re-enters through the same standards layer the data came out of.

Where this comes from

Surgical robotics, medical imaging, and clinical data platforms, built inside device manufacturers, imaging vendors, and life-sciences organisations. Standards committees, cleared products, and the integrations that come after the press release — everything here comes out of that, with the constraints still attached.

About DEXPRA

Working on one of these problems?

Tell us what you’re working on in interoperability, data platforms, or clinical AI, and we’ll tell you what we think.

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