Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution.
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...
| Publicado en: | Journal of Biomedical Informatics Vol. 62; pp. 232 - 243 |
|---|---|
| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Aug2016
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117443023&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117443023 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Aug2016 vid: 62 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 117443023 117443023 NLM27392645 117443023 10.1016/j.jbi.2016.07.008 NLM27392645 117443023 ppf: 232 ppct: 11 formats: tig: atl: Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution. aug: au: Jiang, Guoqian Kiefer, Richard C. Rasmussen, Luke V. Solbrig, Harold R. Mo, Huan Pacheco, Jennifer A. Xu, Jie Montague, Enid Thompson, William K. Denny, Joshua C. Chute, Christopher G. Pathak, Jyotishman affil: Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA sug: subj: Algorithms Phenotype Research, Medical Semantics Resource Databases Funding Source Human ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|