A Semantic Transformation Methodology for the Secondary Use of Observational Healthcare Data in Postmarketing Safety Studies

Abstract

Background: Utilization of the available observational healthcare datasets is key to complement and strengthen the postmarketing safety studies. Use of common data models (CDM) is the predominant approach in order to enable large scale systematic analyses on disparate data models and vocabularies. Current CDM transformation practices depend on proprietarily developed Extract—Transform—Load (ETL) procedures, which require knowledge both on the semantics and technical characteristics of the source datasets and target CDM.Purpose: In this study, our aim is to develop a modular but coordinated transformation approach in order to separate semantic and technical steps of transformation processes, which do not have a strict separation in traditional ETL approaches. Such an approach would discretize the operations to extract data from source electronic health record systems, alignment of the source, and target models on the semantic level and the operations to populate target common data repositories.Approach: In order to separate the activities that are required to transform heterogeneous data sources to a target CDM, we introduce a semantic transformation approach composed of three steps: (1) transformation of source datasets to Resource Description Framework (RDF) format, (2) application of semantic conversion rules to get the data as instances of ontological model of the target CDM, and (3) population of repositories, which comply with the specifications of the CDM, by processing the RDF instances from step 2. The proposed approach has been implemented on real healthcare settings where Observational Medical Outcomes Partnership (OMOP) CDM has been chosen as the common data model and a comprehensive comparative analysis between the native and transformed data has been conducted.Results: Health records of ~1 million patients have been successfully transformed to an OMOP CDM based database from the source database. Descriptive statistics obtained from the source and target databases present analogous and consistent results.Discussion and Conclusion: Our method goes beyond the traditional ETL approaches by being more declarative and rigorous. Declarative because the use of RDF based mapping rules makes each mapping more transparent and understandable to humans while retaining logic-based computability. Rigorous because the mappings would be based on computer readable semantics which are amenable to validation through logic-based inference methods.

A literature review of current technologies on health data integration for patient-centered health management

“…For the purpose of collaborative utilization of health data with heterogeneous data models, Common Data Model (CDM) transformation is one of the trending approaches. For example, the semantic CDM transformation introduced by Pacaci et al 68 transformed health records data from heterogeneous sources to an CDM-based database in a three-step process, which includes transforming source data sets to RDF documents, applying semantic conversion rules to make the data as instances of the CDM’s ontological model and populating the repositories by processing the RDF instances data. It avoids developing many custom analytical methods to the data with the heterogeneous data models and underlying vocabularies when utilizing health data from different sources.…”

A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study

“…In particular, the use of a data model–independent formalism to store data enables the implementation of one-to-many mappings to any target data model. For example, existing work has already proposed the transformation of RDF resources into customized relational data models [ 36 ] or standard common data models, such as i2b2 [ 37 , 38 ] and OMOP [ 39 ]. This approach addresses the complexity of the current many-to-many mappings and will enable the sharing of data with any community, provided that the mapping is done while keeping the data unchanged.…”

Interoperability of electronic health records using Semantic Knowledge Graphs. A use case applied at the UTPL University Hospital

“…On the other hand, [11] presents a semantic transformation approach with three steps: 1) transformation of medical data from heterogeneous sources to RDF, 2) application of semantic conversion rules to obtain data as instances for the CDM (Common Data Models) ontological model, and 3) population of the repositories, which meet the CDM specifications, by means of processing the RDF instances generated in step 2. The proposed approach has been applied in real health care environments where the Observational Medical Outcomes Partnership (OMOP) has been chosen as the common data model.…”