ArticleJournal of biomedical informatics2016
Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution.
Article in Journal of biomedical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review.Journal of medical Internet research · 2024Pooled it
- Common data elements for food allergy clinical trials: an overview.Current opinion in allergy and clinical immunology · 2025Review
- HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.Methods of information in medicine · 2020Article
- Facilitating the design of HL7 domain models through a model-driven solution.BMC medical informatics and decision making · 2020Article
- UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Unleashing the value of Common Data Elements through the CEDAR Workbench.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- Clinical Research Informatics: Contributions from 2016.Yearbook of medical informatics · 2017Review
Corrections and comments
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Authors and funding
12 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.