Translating the Observational Medical Outcomes Partnership – Common Data Model (OMOP-CDM) Electronic Health Records to an OWL Ontology

Abstract

The heterogeneity of electronic health records model is a major problem: it is necessary to gather data from various models for clinical research, but also for clinical decision support. The Observational Medical Outcomes Partnership – Common Data Model (OMOP-CDM) has emerged as a standard model for structuring health records populated from various other sources. This model is proposed as a relational database schema. However, in the field of decision support, formal ontologies are commonly used. In this paper, we propose a translation of OMOP-CDM into an ontology, and we explore the utility of the semantic web for structuring EHR in a clinical decision support perspective, and the use of the SPARQL language for querying health records. The resulting ontology is available online.

Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM

“… 1–4 However, this potential is often not realised due to the inherent complexity of EMR databases—that comprise thousands of data elements across thousands of proprietary tables—where vast amount of data needs to be transformed, cleaned and restructured to make it ‘fit’ for ‘secondary use’. 5 For highly powered collaborative research, where large volumes of EMR data are combined, use is further constrained by the heterogeneity of each institution’s EMR schema 6 ; concern over data sharing and privacy breaches and lack of clarity over governance and consent. 7 …”

“…[1][2][3][4] However, this potential is often not realised due to the inherent complexity of EMR databases-that comprise thousands of data elements across thousands of proprietary tables-where vast amount of data needs to be transformed, cleaned and restructured to make it 'fit' for 'secondary use'. 5 For highly powered collaborative research, where large volumes of EMR data are combined, use is further constrained by the heterogeneity of each institution's EMR schema 6 ; concern over data sharing and privacy breaches and lack of clarity over governance and consent. 7 The Observational Health Data Sciences and Informatics (OHDSI) consortium 8 is addressing these challenges through the transformation of each EMR database into the open-source OMOP-CDM, where EMR data elements are translated into the OMOP-CDM using standardised terminologies such as SNOMED-CT, 9 LOINC 10 or RxNORM.…”