Harnessing the power of OMOP for clinical research and beyond

Large-scale clinical research endeavors are a challenge – but are necessary to produce actionable clinical data. Learn how OMOP can help.

Life-saving medical research depends on reliable data, which usually comes from rigorous experiments conducted in the laboratory. However, to be truly impactful, researchers also need to analyze data from the actual world of patients and health systems. This type of data – known as real-world or observational data – exists in the electronic health record (EHR).

Yet using this data is not without its challenges. As such, the Observational Health Data Sciences and Informatics (OHDSI), is leading the way in fostering collaborative research to help accelerate the translation of clinical data into meaningful insights.

Unlocking the EHR for observational research

But observational studies need a large amount of patient information to produce statistically significant data. As such, getting a large and diverse enough dataset often requires collaboration across institutions, or even countries. And each group must use the same methods to analyze their data – a significant challenge when so many players are in the game.

One solution is to use standardized data formats and queries so that collaborators can analyze their data in the same way. The OHDSI program was created to help researchers achieve this goal by providing these standardized tools – along with a common analytical environment in which to use them.

But before researchers can use these tools, they will need to load their EHR data into OHDSI’s common data model (CDM). CDMs structure data in a way that explicitly defines the semantic meaning and relationships of each data element. OHDSI’s CDM grew out of the Observational Medical Outcomes Partnership (OMOP), an open-community standard that structures clinical data coded using standard reference terminology.

OMOP has tools that allow multiple data sets to use the same standardized methods, definitions, and references by explicitly defining the content of the data. This allows researchers to represent distinct clinical concepts from their home systems that are then fully defined in reference tables. In turn, they ensure data is appropriately stored for easy querying while still maintaining linkages back to the original source data.

Bringing the potential of OMOP to clinical care

The potential of OMOP extends to other stakeholders in the healthcare ecosystem. Its emphasis on collaborative inquiry makes it ideal for organizations – such as large provider networks, health information exchanges, and public health agencies – who need patient data from large, diverse datasets.

As such, OHDSI is keenly aware of this potential and is currently working to adapt OMOP for additional use cases like quality reporting; health information exchange; and point of care decision support. One notable advancement includes the imminent incorporation of Fast Healthcare Interoperability Resources (FHIR) standards, which will make extract, load, transform (ELT) processes much easier for researchers and clinicians alike. Healthcare organizations may consider leveraging OHDSI and OMOP to further their analytical capacity and deliver high-quality patient care.

For more about the importance of defining the meanings and relationships between data points, take a look at our guide to ontologies.

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