Large clinical datasets are an essential resource for biomedical research as they can provide data on millions of patients, which allows for greater strength and reliability in studies. The valuable data found within these existing real-world, large-scale clinical datasets may reduce the need for some traditional intentional trials. However, accessing large clinical datasets can be challenging due to associated costs, license restrictions, and other barriers.

To address these challenges, the National Library of Medicine launched a new Center for Clinical Observational Investigations in 2023. Initially, NLM will curate a list of nationally and internationally available clinical datasets. Then, by using informatics, data science, and statistical analysis, NLM will create and make dataset profiles available to include key information such as participant counts, demographics, diseases, and other characteristics important to research. The Center will also aim to employ a consistent approach to organize the data to foster standardization across the datasets and reduce ambiguity, improve reliability of research, and lower barriers to the use of data.

OMOP

The stated clinical domains, visit contexts and individual concepts used in the CCOI dataset profiles were generated via data structured in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The OMOP CDM is a data standard, designed to standardize the structure and content of observational data. For more information on the OMOP CDM please visit OMOP Common Data Model (ohdsi.github.io). For more information on individual OMOP concepts please visit Athena, the OMOP vocabulary library searchable by domains (i.e., observations, conditions, procedures, drugs, devices, and measurements).