title: Feasibility and utility of applications of the common data model to multiple, disparate observational health databases.
abstract: OBJECTIVE
  To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research.
  
  
  METHODS
  Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results.
  
  
  RESULTS
  Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour.
  
  
  CONCLUSIONS
  The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases.
  
  
  CONCLUSIONS
  Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases.
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA evoss3@its.jnj.com.
authors: Erica A
authors: EA
authors: Voss
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Rupa
authors: R
authors: Makadia
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Amy
authors: A
authors: Matcho
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Qianli
authors: Q
authors: Ma
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Chris
authors: C
authors: Knoll
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Martijn
authors: M
authors: Schuemie
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Frank J
authors: FJ
authors: DeFalco
authors: Medical Informatics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Ajit
authors: A
authors: Londhe
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Vivienne
authors: V
authors: Zhu
authors: Epidemiology Analytics, Janssen Research & Development, Titusville, New Jersey, USA.
authors: Patrick B
authors: PB
authors: Ryan
mesh_terms: D016208:Databases, Factual / Q000592:standards*; D005240:Feasibility Studies; D006302:Health Services Research; D006801:Humans; D064887:Observational Studies as Topic; D012984:Software / Q000592:standards*; D018875:Vocabulary, Controlled
keywords: controlled health services research; database; factual vocabulary; medical informatics observational study