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Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership.

F FitzHenry1, F S Resnic2, S L Robbins2

  • 1Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Department of Biomedical Informatics; Vanderbilt University, Nashville , TN.

Applied Clinical Informatics
|October 9, 2015
PubMed
Summary

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This summary is machine-generated.

Transforming health data to a common data model (CDM) like OMOP CDM is resource-intensive but enables efficient, multi-site comparative effectiveness research by standardizing cohort identification and analysis.

Area of Science:

  • Health Informatics
  • Comparative Effectiveness Research
  • Data Standardization

Background:

  • Large-scale distributed comparative effectiveness analyses require common data models (CDM) across health systems.
  • Several CDMs exist, including Mini-Sentinel and the Observational Medical Outcomes Partnership (OMOP) CDM.

Purpose of the Study:

  • To describe the challenges and opportunities of using the OMOP CDM for a study-specific case.
  • To present three comparative effectiveness use cases developed from the OMOP CDM.

Main Methods:

  • Two health system databases were transformed into the OMOP CDM using provided crosswalks.
  • Cohorts for three comparative effectiveness use cases were developed from the transformed CDMs.
  • Data included administrative/billing, demographic, order history, medication, and laboratory information.
Keywords:
Common data modelbig datacomparative effectiveness

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Main Results:

  • Differences in record counts per person month were observed between civilian and federal datasets.
  • Data extraction methods for medications (orders vs. pharmacy fills) impacted counts.
  • The federal system exhibited a higher prevalence of conditions across all three use cases.
  • Manual coding was necessary for certain data types during the CDM transformation.

Conclusions:

  • Data transformation to the OMOP CDM was time-consuming and resource-intensive.
  • Manual data coding presented limitations during the conversion process.
  • Once converted, the OMOP CDM facilitated consistent cohort identification and analysis across sites, minimizing cross-site effort.