DT OMOP DataHub helps organisations transform raw clinical data into standardised OMOP datasets through a repeatable, traceable pipeline designed for real-world healthcare data complexity.
Our structured pipeline supports each stage of the clinical data transformation process — from understanding your source data to delivering a validated, traceable OMOP Common Data Model output.
✅ Versioning & reproducibility — pipeline runs are designed to be auditable, traceable and rebuildable.
Discuss your data landscapeDT OMOP DataHub is designed for the realities of clinical data — messy, heterogeneous, and high-stakes. Each capability supports a structured path from raw data to OMOP.
For complex clinical patterns and free-text that rules alone cannot handle, our LLM fallback generates structured candidate output — validated and staged before entering formal mapping.
LLM + Rules-firstQuality checks are built into each pipeline stage — from schema profiling through post-load sanity checks — helping ensure OMOP output meets defined accuracy standards.
Stage-by-stage QADesigned to work with the source data forms encountered in real healthcare projects — database exports, flat files, EHR-derived data, laboratory and measurement data, free-text clinical content, and hybrid source environments.
Multi-formatPipeline runs capture source snapshots, preprocessing configs, mapping specs and vocabulary versions — designed to support traceable, reproducible rebuilds.
Full audit trailStructured exception backlogs, regression kits and diff reports help clinical data teams review, resolve and learn from edge cases — supporting continuous improvement over time.
Human-in-loopOMOP-structured output provides a standardised foundation for ML-ready data preparation, analytics, and further downstream use — within the broader OHDSI ecosystem and potential interoperability directions.
ML · Analytics · OHDSIGeneric ETL tools were not designed for the complexity of healthcare data. DT OMOP DataHub addresses the real-world challenges of EHR exports, free-text notes, long-tail clinical patterns, and evolving OMOP vocabularies with a structured, traceable approach.
A defined 4-step pipeline replaces ad-hoc ETL with a consistent, scalable process from source data to OMOP.
Multi-layer QA — from data profiling through post-load checks — helps ensure mapping quality and provides traceable evidence.
Through each engagement, DT refines reusable pipeline components — preprocessing engines, validation frameworks, and orchestration layers — while delivering client-specific mappings and configurations.
Audit trails, versioning, and QA evidence generation support traceability and accountability throughout the pipeline.
DT OMOP DataHub is designed to support teams that need structured, standardised clinical data for research, analytics, or interoperability.
Reduce the time spent preparing clinical data for analysis. DT OMOP DataHub provides a structured path to OMOP-formatted datasets so your team can focus on research.
Transform EHR data into OMOP to support participation in federated research networks, population health analytics, and downstream interoperability initiatives.
Move from OMOP data to ML-ready datasets — build reproducible feature matrices on standardised clinical concepts for model training, evaluation, and clinical AI applications.
Learn how DT OMOP DataHub can support your healthcare data standardisation needs. We are happy to walk through the pipeline, discuss your data landscape, or explore a potential engagement.
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