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Data model considerations for clinical effectiveness researchers.

Michael G Kahn1, Deborah Batson, Lisa M Schilling

  • 1Department of Pediatrics, Division of Pediatric Epidemiology, University of Colorado, Denver, CO, USA. michael.kahn@ucdenver.edu

Medical Care
|June 14, 2012
PubMed
Summary
This summary is machine-generated.

Selecting the right data model is crucial for healthcare research infrastructure. The SAFTINet project evaluated models for Comparative Effectiveness Research (CER), choosing the Observation Medical Outcomes Partnership (OMOP) common data model.

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Area of Science:

  • Health Informatics
  • Data Science in Healthcare
  • Comparative Effectiveness Research (CER)

Background:

  • Growing adoption of electronic health records necessitates robust informatics infrastructure.
  • Integration of clinical and administrative data is key for healthcare effectiveness.
  • The Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) project aimed to build a scalable, distributed network for CER.

Purpose of the Study:

  • To evaluate the suitability of various data models for Comparative Effectiveness Research (CER).
  • To identify a common data model that balances complexity and usability for disparate data sources.
  • To inform the development of a robust informatics infrastructure for healthcare research.

Main Methods:

  • The SAFTINet project served as a case study for data model evaluation.
  • A sample use case involving asthma patient cohort identification was developed.
  • Data models were examined against technical and investigator requirements.

Main Results:

  • Several data models were explored to meet the needs of CER investigators.
  • The SAFTINet team prioritized requirements to select the most suitable data model.
  • The Observation Medical Outcomes Partnership (OMOP) common data model was ultimately chosen by SAFTINet.

Conclusions:

  • The selection of a data model depends on a prioritization of requirements and project-specific factors.
  • Multiple data models can be valid options for healthcare research infrastructure.
  • The chosen data model should be conducive to expansion for future research needs.