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

Guoqian Jiang1, Richard C Kiefer1, Luke V Rasmussen2

  • 1Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA.

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|July 10, 2016
PubMed
Summary
This summary is machine-generated.

A new data element repository (DER) makes the Quality Data Model (QDM) machine-readable for electronic clinical quality measures (eCQMs). This enhances EHR data use in clinical research and supports phenotype algorithm development.

Keywords:
HL7 Fast Healthcare Interoperability Resources (FHIR)Metadata standardsPhenotype algorithmsQuality Data Model (QDM)Semantic Web technology

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Area of Science:

  • Health Informatics
  • Clinical Data Modeling
  • Semantic Web Technologies

Background:

  • The Quality Data Model (QDM) is crucial for representing electronic health record (EHR) data for electronic clinical quality measures (eCQMs).
  • Current QDM specifications lack machine readability, hindering its use in automated processes and clinical research.
  • Existing formats like HL7 Health Quality Measures Format (HQMF) are descriptive but not computationally actionable.

Purpose of the Study:

  • To develop and evaluate a machine-readable data element repository (DER) for the QDM.
  • To provide service APIs for QDM data elements to support phenotype algorithm authoring and execution.
  • To address limitations in the QDM specification for enhanced machine interpretation.

Main Methods:

  • Utilized the ISO/IEC 11179 metadata standard for data element structure capture.
  • Leveraged Semantic Web technologies for semantic representation of metadata.
  • Proposed harmonization with HL7 FHIR and CIMI models to enhance QDM and DER capabilities.

Main Results:

  • Developed a functional DER providing machine-readable QDM data element service APIs.
  • Identified underspecified areas in QDM, including model constraints and value sets.
  • Demonstrated the scalability and extensibility of the DER approach compared to existing implementations like the Measure Authoring Tool (MAT).

Conclusions:

  • The developed DER significantly improves the machine readability of QDM for eCQMs.
  • Harmonization with FHIR and CIMI enhances QDM's extensibility and coverage.
  • The DER-based approach offers a scalable and extensible solution for EHR-driven phenotype algorithm development.