PT  - JOURNAL ARTICLE
AU  - Mark D. Danese
AU  - Marc Halperin
AU  - Jennifer Duryea
AU  - Ryan Duryea
TI  - The Generalized Data Model for Clinical Research
AID  - 10.1101/194597
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 194597
4099  - http://biorxiv.org/content/early/2017/11/10/194597.short
4100  - http://biorxiv.org/content/early/2017/11/10/194597.full
AB  - Background Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the efficiency of research. Transforming and loading data into these models is a labor-intensive process that can alter the original semantics of the original data. Therefore, we created a data model with a hierarchical structure that simplifies the transformation process and minimizes data alteration.Methods There were two design goals in constructing the tables and table relationships for the Generalized Data Model (GDM). The first was to use a single table for storing clinical codes in their original vocabularies to retain the original semantic representation of the data. The second was to retain hierarchical information present in the original data while retaining provenance.The model was tested by transforming synthetic Medicare data; Surveillance, Epidemiology, and End Results data linked to Medicare claims; and electronic health records from the Clinical Practice Research Datalink. We also tested a subsequent transformation from the GDM into the Sentinel data model.Results The resulting data model contains 19 tables, with the Clinical Codes, Contexts, and Collections tables serving as the core of the model, and containing most of the clinical, provenance, and hierarchical information. In addition, a Mapping table allows users to apply an arbitrarily complex set of relationships among vocabulary elements to facilitate automated analyses.Conclusions The GDM offers researchers a simple process for loading data, clear data provenance, and a clear path for users to transform their data into other data models. The GDM is designed to retain hierarchical relationships among data elements as well as the original semantic representation of the data, ensuring consistency in study building as part of a complete data pipeline for researchers.