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J Biomed Inform. 2016 Aug;62:232-42. doi: 10.1016/j.jbi.2016.07.008. Epub 2016 Jul 5.

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution.

Author information

1
Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA. Electronic address: Jiang.Guoqian@mayo.edu.
2
Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA.
3
Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
4
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
5
Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
6
Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
7
Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; School of Computing, DePaul University, Chicago, IL, USA.
8
School of Computing, DePaul University, Chicago, IL, USA.
9
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA; Department of Medicine, Vanderbilt University, Nashville, TN, USA.
10
School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
11
Division of Health Informatics, Weill Cornell Medical College, Cornell University, New York City, NY, USA.

Abstract

The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.

KEYWORDS:

HL7 Fast Healthcare Interoperability Resources (FHIR); Metadata standards; Phenotype algorithms; Quality Data Model (QDM); Semantic Web technology

PMID:
27392645
PMCID:
PMC5490836
DOI:
10.1016/j.jbi.2016.07.008
[Indexed for MEDLINE]
Free PMC Article

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