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The Generalized Data Model for clinical research.

Mark D Danese1, Marc Halperin2, Jennifer Duryea2

  • 1Outcomes Insights, Inc., 2801 Townsgate Road, Suite 330, Westlake Village, CA, 91361, USA. mark@outins.com.

BMC Medical Informatics and Decision Making
|June 26, 2019
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This summary is machine-generated.

Researchers developed a new hierarchical data model to simplify healthcare data transformation, preserve original data semantics, and maintain data provenance for reproducible research.

Keywords:
Claims dataData modelElectronic health records

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Area of Science:

  • Biomedical Informatics
  • Health Data Management
  • Clinical Research Data Standards

Background:

  • Healthcare data resides in diverse schemas, hindering reliable and reproducible research.
  • Existing data models require labor-intensive transformations that can alter data semantics.
  • A novel hierarchical data model was developed to address these challenges.

Purpose of the Study:

  • To create a data model that simplifies data transformation processes.
  • To minimize alteration of original data semantics during transformation.
  • To retain hierarchical information and data provenance.

Main Methods:

  • Developed the Generalized Data Model (GDM) with a focus on preserving original clinical code vocabularies and hierarchical structures.
  • Tested the GDM by transforming synthetic Medicare data, Surveillance, Epidemiology, and End Results (SEER) data linked to Medicare claims, and electronic health records.
  • Evaluated the GDM's ability to transform data into the Sentinel data model.

Main Results:

  • The GDM comprises 19 tables, with Clinical Codes, Contexts, and Collections tables as the core.
  • The model effectively retains clinical, provenance, and hierarchical information.
  • A Mapping table facilitates automated analyses by defining relationships among vocabulary elements.

Conclusions:

  • The GDM simplifies data transformation, ensures clear data provenance, and supports interoperability with other data models.
  • The model preserves hierarchical relationships and original data semantics for consistent protocol implementation.
  • The GDM serves as a complete data pipeline component for researchers, enhancing data consistency and reproducibility.