Facilitating phenotype transfer using a common data model

George Hripcsak, Ning Shang, Peggy L. Peissig, Luke V. Rasmussen, Cong Liu, Barbara Benoit, Robert J. Carroll, David S. Carrell, Joshua C. Denny, Ozan Dikilitas, Vivian S. Gainer, Kayla Marie Howell, Jeffrey G. Klann, Iftikhar J. Kullo, Todd Lingren, Frank D. Mentch, Shawn N. Murphy, Karthik Natarajan, Jennifer A. Pacheco, Wei Qi WeiKen Wiley, Chunhua Weng

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Background: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process. Methods: Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network. Results: It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode. Conclusion: Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.

Original languageEnglish (US)
Article number103253
JournalJournal of Biomedical Informatics
StatePublished - Aug 2019


  • Common data model
  • Electronic health records
  • Phenotyping

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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