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J Biomed Inform. 2016 Dec;64:333-341. doi: 10.1016/j.jbi.2016.10.016. Epub 2016 Oct 29.

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

Author information

1
Duke Translational Medicine Institute, Duke University, 2424 Erwin Road, Hock Plaza Box 3850, Durham, NC 27705, USA. Electronic address: maryam.garza@duke.edu.
2
Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Room: Suite 140, Salt Lake City, UT 84108, USA. Electronic address: guilherme.delfiol@utah.edu.
3
Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Road, Suite 1102 Hock Plaza Box 2721, Durham, NC 27705, USA. Electronic address: jessie.tenenbaum@duke.edu.
4
Duke Translational Medicine Institute, Duke University, 2424 Erwin Road, Hock Plaza Box 3850, Durham, NC 27705, USA; Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, 501 Jack Stephens Drive, Mail Slot # 782, Little Rock, AR 72205, USA. Electronic address: acwalden@uams.edu.
5
Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Road, Suite 1102 Hock Plaza Box 2721, Durham, NC 27705, USA; Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, 501 Jack Stephens Drive, Mail Slot # 782, Little Rock, AR 72205, USA. Electronic address: mzozus@uams.edu.

Abstract

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.

KEYWORDS:

Common data model; Data model evaluation; Electronic health records

PMID:
27989817
DOI:
10.1016/j.jbi.2016.10.016
[Indexed for MEDLINE]
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