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.

Keywords: common data model; healthcare datasets; pharmacovigilance; postmarketing safety study; semantic transformation.

Figure 1

Overview of the semantic transformation methodology—population of a CDM repository from disparate EHR datasets through semantic mapping rules.

Figure 2

Visualization of OMOP ontology constructs.

Figure 3

(A) A sample semantic conversion rule for person gender and birthdate, (B) A sample filtering rule to check existence of gender and year of birth.

Figure 4

(A) Small portion of a patient data that covers the gender and the birthdate. (B) A sample unit test for semantic transformation rules.

Figure 5

Specifying eligibility criteria in TAS.

Figure 6

Chronograph visualizing the temporal pattern between a prescription of a drug and the occurrence of a medical event.

Figure 7

Implementation of the proposed framework in real-world settings.

Figure 8

Demographic summary of AMI and nifedipine cohorts in original TUD and populated OMOP database.

Figure 9

Proportion of Top 50 medications to whole exposures in TUD and transformed OMOP databases.

Figure 10

Proportion of Top 50 conditions to whole occurrences in TUD and transformed OMOP databases.