Happy Pharma, Healthcare, Academic, and Government customers
mapped patients records
OMOP ETL conversions and refreshes completed
Pluggable ETL Data Adapters
OMOP CDM ETL / RWD Data Harmonization
OMOP ETL Acceleration Kit (Argo) and Data Quality Tools
(Statius and DQD)
- 30+ pluggable data adapters, including rich custom OMOP mappings
- All major Claims, EHR, Registry, SDTM data sets - Truven MarketScan™, Optum Claims™, Optum MarketClarity™, FlatIron™, Premier™, MDV™ , JMDC™, Symphony™, SDTM, Epic™, Allscripts™, Cerner™, CPRD™, TriNetX™, UK BioBank™ and more.
- Spark-based, scalable, cross-platform OMOP CDM ETL toolkit (Argo ™ )
- 99.99% defect prevention and OMOP test coverage
OMOP Vocabularies / Ontology
- Curation and maintenance of the OMOP Standardized Vocabularies
- Creation of OMOP ETL project-related vocabularies (custom mappings)
- Custom Vocabulary curation and integration service
OMOP Data Integration with non-OMOP Data
- The clinical trial, genomics, imaging, unstructured and semi-structured
Technology Partnership and Expertise
- Microsoft Azure, Google Cloud Platform, Amazon Web Services, Snowflake, Databricks,
- Cloudera
- DevOps / InfoSec
Contact us
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Whether your observational health data comes from electronic health records, payor or claims data or even from registries and clinical trials, our team of professionals in structural data analysis, medical terminology and transformation logic development can convert it to a target database of the highest quality. The team works closely with renowned experts in the field and makes sure that the results satisfy all conventions and rules so that your organization can make use of a complete and accurate representation of your source data in the OMOP Common Data Model.
The OMOP Common Data Model allows for the systematic analysis of disparate observational databases. The concept behind this approach is to transform data contained within those databases into a common format (data model) as well as a common representation (terminologies, vocabularies, coding schemes), and then perform systematic analyses using a library of standard analytic routines that have been written based on the common format.
Create ETL Specifications
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Perform source data quality assessment
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Analyze source vocabularies
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Perform table to table, field to field mappings
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Describe business rules and transformations
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Filter invalid data
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Create Unit Tests
Perform Vocabulary mappings
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Identify sources vocabularies
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Create custom vocabularies and mappings
Implement OMOP CDM ETL Code
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Develop ETL code
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Execute and populate OMOP CDM
Perform Quality Control Testing and Release
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Non-functional and functional automated and manual testing data quality test (1,000+ tests)
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Extensive User Acceptance testing
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Release into production
Either for local vocabularies that need translation into OMOP standardized vocabularies or for entire new public vocabularies that need to be processed and made available to the OHDSI community, our team of medical ontology experts can provide solutions. The team is serving the OHDSI community maintaining all standardized vocabularies available from Athena and their relationships and can also help you in building your own vocabularies, either local or public.