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

Publication ,  Journal Article
Garza, M; Del Fiol, G; Tenenbaum, J; Walden, A; Zozus, MN
Published in: J Biomed Inform
December 2016

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.

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Published In

J Biomed Inform

DOI

EISSN

1532-0480

Publication Date

December 2016

Volume

64

Start / End Page

333 / 341

Location

United States

Related Subject Headings

  • Registries
  • Medical Informatics
  • Information Dissemination
  • Humans
  • Electronic Health Records
  • Biomedical Research
  • Biomedical Engineering
  • 4601 Applied computing
  • 4203 Health services and systems
  • 11 Medical and Health Sciences
 

Citation

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ICMJE
MLA
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Garza, M., Del Fiol, G., Tenenbaum, J., Walden, A., & Zozus, M. N. (2016). Evaluating common data models for use with a longitudinal community registry. J Biomed Inform, 64, 333–341. https://doi.org/10.1016/j.jbi.2016.10.016
Garza, Maryam, Guilherme Del Fiol, Jessica Tenenbaum, Anita Walden, and Meredith Nahm Zozus. “Evaluating common data models for use with a longitudinal community registry.J Biomed Inform 64 (December 2016): 333–41. https://doi.org/10.1016/j.jbi.2016.10.016.
Garza M, Del Fiol G, Tenenbaum J, Walden A, Zozus MN. Evaluating common data models for use with a longitudinal community registry. J Biomed Inform. 2016 Dec;64:333–41.
Garza, Maryam, et al. “Evaluating common data models for use with a longitudinal community registry.J Biomed Inform, vol. 64, Dec. 2016, pp. 333–41. Pubmed, doi:10.1016/j.jbi.2016.10.016.
Garza M, Del Fiol G, Tenenbaum J, Walden A, Zozus MN. Evaluating common data models for use with a longitudinal community registry. J Biomed Inform. 2016 Dec;64:333–341.
Journal cover image

Published In

J Biomed Inform

DOI

EISSN

1532-0480

Publication Date

December 2016

Volume

64

Start / End Page

333 / 341

Location

United States

Related Subject Headings

  • Registries
  • Medical Informatics
  • Information Dissemination
  • Humans
  • Electronic Health Records
  • Biomedical Research
  • Biomedical Engineering
  • 4601 Applied computing
  • 4203 Health services and systems
  • 11 Medical and Health Sciences