The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) serves as a global standard for observational health data. Its fundamental purpose involves transforming diverse healthcare data into a consistent, usable format for research. This enables large-scale analysis across various healthcare datasets, creating a common language for health information.
The Challenge of Health Data
Working with real-world healthcare data is difficult due to varied collection methods. Data originates from numerous sources, including electronic health records (EHRs), claims databases, and patient registries. These sources often use different coding systems and terminologies across institutions and countries. This lack of standardization creates data silos, hindering collaborative research and comprehensive analysis. For example, the same medical event might be recorded with different codes in separate databases, making it challenging to aggregate information across systems.
Anatomy of OMOP CDM
The OMOP CDM functions as a relational database schema, providing a blueprint for how health data should be structured. It consists of a predefined set of tables, each designed to capture a specific domain of clinical information in a standardized manner. For instance, the `PERSON` table holds demographic details, while `CONDITION_OCCURRENCE` records diagnoses, and `DRUG_EXPOSURE` tracks medication administration.
Other tables like `MEASUREMENT`, `PROCEDURE_OCCURRENCE`, and `OBSERVATION` store laboratory results, medical procedures, and clinical observations, respectively. Each of these tables includes a `person_id` field, which acts as a de-identified unique identifier to link various clinical events to a specific patient. This consistent linking across tables supports a patient-centric view of the data.
A core component of the OMOP CDM is its reliance on standardized vocabularies. These vocabularies, such as SNOMED CT for clinical terms, RxNorm for medications, and LOINC for laboratory tests, map disparate local codes to universal concepts. This mapping allows data originating from different source systems, which might use varying coding schemes (e.g., ICD-9, ICD-10, CPT4), to be directly compared and analyzed.
The OMOP standardized vocabularies are available free of charge for use with the CDM. They are regularly updated, typically twice a year, to align with the latest terminology standards. This ensures the model remains current with evolving medical knowledge and coding practices.
Beyond the tables and vocabularies, OMOP CDM also incorporates standardized conventions. These are rules and guidelines for data mapping and the Extract, Transform, Load (ETL) processes. These conventions ensure consistency in how data is represented and interpreted during conversion from raw source data into the CDM format. For instance, conventions dictate how to handle missing data, such as requiring an end date for drug exposure records, even if the source system does not explicitly provide it. This structured approach, encompassing tables, standardized vocabularies, and clear conventions, collectively enables the harmonization of diverse healthcare datasets into a uniform, analyzable format.
What OMOP CDM Achieves
The standardization offered by OMOP CDM enables large-scale observational research by facilitating studies across multiple institutions and diverse patient populations. This leads to more robust and generalizable findings, as researchers can combine and analyze data from various sources that have been transformed into a common format. For example, studies on drug safety or disease progression can leverage vast amounts of real-world patient data.
The model accelerates real-world evidence (RWE) generation by supporting the analysis of routine clinical practice data. RWE is derived from real-world data sources like EHRs and claims, providing insights into drug effectiveness, safety, and disease progression in actual clinical settings, rather than controlled trial environments. This is particularly valuable for understanding how treatments perform outside of highly controlled research studies.
OMOP CDM’s standardized data is instrumental in supporting machine learning and artificial intelligence (AI) applications in healthcare. Interoperable data makes it easier to gather and integrate information from diverse sources, which is fundamental for developing and validating predictive models. Tools like ATLAS can define patient cohorts for training these models, allowing health data scientists to focus more on model development.
The OMOP CDM fosters collaborative research networks by allowing researchers worldwide to share and analyze data consistently. This common framework promotes data sharing and comparison across organizations, enabling multinational studies. The ability to conduct federated analyses, where data remains local but is analyzed systematically, enhances reproducibility and patient confidentiality.
Adoption and Ongoing Development
The OMOP CDM has seen widespread global adoption by academic institutions, pharmaceutical companies, and government agencies. Its use is increasing, particularly in observational patient data research and network studies. This growing adoption reflects the recognized benefits of standardizing health data for research and analytical purposes.
The Observational Health Data Sciences and Informatics (OHDSI) community plays a central role in the continuous evolution and maintenance of the OMOP CDM. OHDSI is an open-science community that works collaboratively to generate evidence from health data. It develops and maintains the CDM, standard vocabularies, and a suite of open-source software tools that support the entire process from data ingestion to large-scale federated data analysis. OHDSI’s open-source initiative ensures transparency and fosters a collaborative environment for ongoing development and the establishment of best practices. This community-driven approach means the model continually adapts to new analytical use cases and evolving healthcare data needs.