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DyMap is a middleware application to match datasets including terminologies, ontologies, vocabularies and other data values.  It's a simple to use, no-code solution that any end-user can use to easily map database values for system integration and semantic interoperability. You just need to select the two datasets, click on ‘start working’, verify mappings between different concepts, make corrections and push to master. 

DyMap also performs remapping routines in its projects, analysing changes between releases of the corresponding datasets and proposing changes to impacted mappings. This also applies to large scale ontologies and clinical terminologies such as SNOMED CT, dm+d, ICD and others, and for clinical environments.  

For clinical environments, especially hospitals and care facilities, mappings can be expressed via FHIR for downstream use by different hospital applications. NHS UK staff can align to the clinical terminologies in the UK's NHS Terminology Server using our application, and maintain the local mappings within DyMap.

Convinced? Just click on the button below to book a demo! 


  • Interoperability tool for data matching and semantic interoperability.

  • Link or load data from all other systems and databases (API or file upload).

  • Automated data matching to quickly combine different data sources.

  • ​Rapidly matches records, concepts and codes between sources, systems and databases.

  • Actively manages mappings between ontologies, terminologies, vocabularies etc. whether formal or local.

  • Maintains mappings even when source data or ontologies/vocabularies evolve, with changes flagged.

  • Scalable, open architecture design. APIs allow for integration with other systems and sources: 

    • API to expresses data in FHIR format for downstream medical applications.

    • API for user authentication, and 

    • API to access system functions (data ingestion, auto-matching, publishing and mapping maintenance). 


  • Generic interoperability tool for use across multiple sectors (health, public administration, defence, industry etc.).

  • Solves difficult-to-manage changes to clinical terminologies and other formal ontologies. Changes are automatically identified and queued to the user.

  • Simple enough to be used by non-IT personnel on an ongoing basis with minimal training.

  • Operational units can maintain local content and terminology mappings locally while integrating with the wider IT infrastructure.

  • Extends life of legacy systems that cannot upgraded by providing a 'semantic layer' that incorporates the legacy data into the current terminology environment.

  • Eliminates ad hoc workarounds by IT staff for Extract-Transform-Load (ETL) functions between different applications and databases and avoids reliance on hard coded mappings that introduce technical debt.

  • Harmonises data naturally at source, providing clean data downstream (to other applications, data warehouse etc.) to support advanced analytics.

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