title: Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership.
abstract: BACKGROUND
  Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs.
  
  
  OBJECTIVE
  In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM.
  
  
  METHODS
  The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules.
  
  
  RESULTS
  Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system's medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM.
  
  
  CONCLUSIONS
  The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.
authors.affiliation: Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Department of Biomedical Informatics; Vanderbilt University, Nashville , TN.
authors.forename: F
authors.initials: F
authors.lastname: FitzHenry
authors.affiliation: Division of Cardiology, Brigham and Women's Hospital , Boston, MA.
authors.forename: F S
authors.initials: FS
authors.lastname: Resnic
authors.affiliation: Division of Cardiology, Brigham and Women's Hospital , Boston, MA.
authors.forename: S L
authors.initials: SL
authors.lastname: Robbins
authors.affiliation: Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Division of General Internal Medicine and Public Health, Vanderbilt University , Nashville, TN.
authors.forename: J
authors.initials: J
authors.lastname: Denton
authors.affiliation: Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Division of General Internal Medicine and Public Health, Vanderbilt University , Nashville, TN.
authors.forename: L
authors.initials: L
authors.lastname: Nookala
authors.affiliation: Department of Health, RAND Corporation, Santa Monica , CA.
authors.forename: D
authors.initials: D
authors.lastname: Meeker
authors.affiliation: Division of Biomedical Informatics, University of California , San Diego, CA.
authors.forename: L
authors.initials: L
authors.lastname: Ohno-Machado
authors.affiliation: Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Department of Biomedical Informatics; Vanderbilt University, Nashville , TN ; Division of General Internal Medicine and Public Health, Vanderbilt University , Nashville, TN ; Department of Biostatistics, Vanderbilt University , Nashville, TN.
authors.forename: M E
authors.initials: ME
authors.lastname: Matheny
mesh_terms: D065709:Common Data Elements; D057186:Comparative Effectiveness Research; D016208:Databases, Factual; D003695:Delivery of Health Care / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D017063:Outcome Assessment, Health Care / Q000379:methods*
keywords: Common data model; big data; comparative effectiveness