OMOP/OHDSITHE SOLUTION
OMOP (Observational Medical Outcomes Partnership) is a public-private collaboration, chaired by the FDA, which designed the OMOP Common Data Model (CDM), a standardized model for storing Real World Data (RWD), developed to facilitate the generation of scientific evidence through large-scale observational studies.
OHDSI (Observational Health Data Sciences and Informatics) is an international interdisciplinary collaboration that originated in 2014 whose purpose is to update the OMOP-CDM and develop open source IT tools to support OMOP-CDM such as Athena, Atlas, Achilles, Usagi, White Rabbit and Rabbit in a Hat (https://www.ohdsi.org/).
With hundreds of researchers from 30 countries and clinical databases for over 800 million patients worldwide, OHDSI enables a community of researchers to collaboratively generate observational studies to drive better health care decisions and care.
OUR SERVICES
We provide consulting on electronic data collection systems for non-profit clinical studies and disease registries based on
- Harmonization of a data source with international standard terminology aimed at integration into OMOP
- Installation of OHDSI tools for: characterization of source data, exploration and analysis of harmonized data and data quality control
- Support for the design of observational studies through OHDSI tools (e.g. ATLAS)
- Training for the use of OHDSI tools for data exploration and analysis
- Ordinary maintenance (system updates, architecture monitoring, etc.)
- Support for the automatic compilation of eCRFs (e.g. REDCap) with data from local information systems through integration with OMOP
OUR REFERENCES
BIOMERIS is among the first companies in Europe and the first company in Italy to have obtained certification from the IMI-EHDEN (European Health Data & Evidence Network) project for mapping clinical data to the Common Data Model OMOP.
The BIOMERIS team also has more than ten years of experience in data integration projects and construction of infrastructures for the reuse of clinical data for research purposes.
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