The OMOP Common Data Model in Australian Primary Care Data: Building a Quality Research Ready Harmonised Dataset
Authors: Ward Roger , Hallinan Christine Mary , Ormiston-Smith David , Chidgey Christine , Boyle Dougie 
Published: March 2023

Abstract

Background
The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a common data model to enable analysis alongside other comparable health metric datasets. This can be achieved using a model such as, the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). The OMOP-CDM is a data schema that uses a standardised vocabulary for the systematic analysis of multiple distinct observational databases. The concept behind OMOP is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances independent institutional research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and ‘validation’ analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in a primary care data repository.
Methods
We used structured query language (SQL) to construct, extract, transform, and load scripts into a database to convert the data into the OMOP common data model. The volume of distinct free text terms from each unique EMR presented a mapping challenge. Up to 10% of the source terms had an exact text match to the Snomed CT, RxNorm and LOINC standard vocabularies. As part of the manual mapping process for terms that did not have an exact match, an a priori decision rule provided a cut off value for terms that occurred with a low frequency. Based on this frequency threshold, over 95% of the unmapped terms were mapped manually. To assess the data quality of the resultant OMOP dataset we applied the OHDSI data quality dashboard.
Results
Across three primary care EMR systems we converted data on 2.3 million active patients to version 5.4 of the OMOP common data model. The Data Quality Dashboard was used to check data Plausibility, Conformance and Completeness. In all 3,570 checks were performed, each one organized into the Kahn framework. For each check the result was compared to a threshold whereby a FAIL is any percentage of violating rows falling above a predetermined value. The overall pass rate of the primary care OMOP database described here was 97%.
Conclusion
Given the OMOP CDM’s wide scale international usage, support, and training available, it is an opportune way to standardise data for collaborative use. Furthermore, it is easy to share analysis packages between research groups. This allows the rapid and repeatable comparison of data between groups and countries. There is a full suite of open-source tools available to support the Common Data Model. For instance, the OHDSI Data Quality Dashboard proved especially useful in examining the quality of our data. The simplicity of the common data model and the standards-based approach makes it an easy model to adopt and integrate into existing data acquisition and processing procedures.