Observational Medical Outcomes Partnership (OMOP)

Interoperability of healthcare data is essential to a Learning Healthcare System.

Historically, healthcare organizations and individual investigators have assembled a wealth of datasets, coding algorithms, phenotype definitions, and data dictionaries for a myriad of different purposes without any coordination or standardization. This has resulted in huge inefficiencies that impede the translation of evidence-based practices into routine care. Thus, any attempt to combine or compare two or more of these datasets still requires enormous effort. If every healthcare system uses a different name for a data field, then evaluating and comparing system-wide performance becomes impossible.

The purpose of a Common Data Model (CDM) is to standardize the format and content of observational data so that standardized applications, tools and methods can be applied across different datasets. Use of a Common Data Model integrates healthcare records across healthcare organizations so that these data resources can be queried to answer important questions quickly and efficiently.

The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) has been adopted by the Health Data Sciences and Informatics (OHDSI) collaborative, a multi-stakeholder, inter-disciplinary effort to create open-source solutions that bring out the value of observational health data through large-scale analytics. The purpose of a CDM is to standardize the format and content of observational data so that common software applications, tools and methods can easily be applied across datasets from multiple healthcare organizations.

OMOP Implementations

CHIME is supporting investigators who wish to use national OMOP data from the VA and/or local OMOP data from UCSF for clinical research projects. Through the pSCANNER project, data from all 5 University of California medical centers and the Veterans Health Administration (>150 medical centers) have been transformed into the OMOP common data model (version 4.0). Standardized structured query language (SQL) queries are shared in a common open-source repository and updated regularly. All data documentation is freely available online.