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

Guoqian Jiang, Richard C. Kiefer, Luke V. Rasmussen, Harold R. Solbrig, Huan Mo, Jennifer A. Pacheco, Jie Xu, Enid Montague, William K. Thompson, Joshua C. Denny, Christopher G. Chute, Jyotishman Pathak

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)232-242
Number of pages11
JournalJournal of Biomedical Informatics
StatePublished - Aug 1 2016


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

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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