Evaluating common data models for use with a longitudinal community registry.

Journal Article (Journal Article)

OBJECTIVE: To evaluate common data models (CDMs) to determine which is best suited for sharing data from a large, longitudinal, electronic health record (EHR)-based community registry. MATERIALS AND METHODS: Four CDMs were chosen from models in use for clinical research data: Sentinel v5.0 (referred to as the Mini-Sentinel CDM in previous versions), PCORnet v3.0 (an extension of the Mini-Sentinel CDM), OMOP v5.0, and CDISC SDTM v1.4. Each model was evaluated against 11 criteria adapted from previous research. The criteria fell into six categories: content coverage, integrity, flexibility, ease of querying, standards compatibility, and ease and extent of implementation. RESULTS: The OMOP CDM accommodated the highest percentage of our data elements (76%), fared well on other requirements, and had broader terminology coverage than the other models. Sentinel and PCORnet fell short in content coverage with 37% and 48% matches respectively. Although SDTM accommodated a significant percentage of data elements (55% true matches), 45% of the data elements mapped to SDTM's extension mechanism, known as Supplemental Qualifiers, increasing the number of joins required to query the data. CONCLUSION: The OMOP CDM best met the criteria for supporting data sharing from longitudinal EHR-based studies. Conclusions may differ for other uses and associated data element sets, but the methodology reported here is easily adaptable to common data model evaluation for other uses.

Full Text

Duke Authors

Cited Authors

  • Garza, M; Del Fiol, G; Tenenbaum, J; Walden, A; Zozus, MN

Published Date

  • December 2016

Published In

Volume / Issue

  • 64 /

Start / End Page

  • 333 - 341

PubMed ID

  • 27989817

Pubmed Central ID

  • PMC6810649

Electronic International Standard Serial Number (EISSN)

  • 1532-0480

Digital Object Identifier (DOI)

  • 10.1016/j.jbi.2016.10.016


  • eng

Conference Location

  • United States