ArticleJournal of the American Medical Informatics Association : JAMIA2022
Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
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Who cites it
18 citing papers in PubMed.
- Bidirectional data harmonization across the multiple chronic disease disparities research consortium: A pipeline description of opportunities and challenges.Journal of biomedical informatics · 2026Article
- Federation, not centralization: a new paradigm for electronic health record-based critical care research.Annals of the American Thoracic Society · 2026Article
- Leadership, Informatics Expertise, and Resources: Determinants of Institutional Data Sharing in the National Clinical Cohort Collaborative (N3C).AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- A case-crossover analysis of short-term PMCommunications medicine · 2025Article
- Automated Data Harmonization in Clinical Research: Natural Language Processing Approach.JMIR formative research · 2025Article
- MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.BMC medical informatics and decision making · 2025Article
- Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.Journal of medical Internet research · 2024Article
- Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID.Communications medicine · 2024Article
- Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review.BMC medical research methodology · 2023Review
- Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration.BMC medical informatics and decision making · 2023Article
- SARS-CoV-2 Reinfection is Preceded by Unique Biomarkers and Related to Initial Infection Timing and Severity: an N3C RECOVER EHR-Based Cohort Study.medRxiv : the preprint server for health sciences · 2023Article
- Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond.Journal of the American Medical Informatics Association : JAMIA · 2022Observational
- BGLM: big data-guided LOINC mapping with multi-language support.JAMIA open · 2022Article
- Vital Measurements of Hospitalized COVID-19 Patients as a Predictor of Long COVID: An EHR-based Cohort Study from the RECOVER Program in N3C.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Article
- An ordinal severity scale for COVID-19 retrospective studies using Electronic Health Record data.JAMIA open · 2022Article
- Review
- Gap Analysis of Glaucoma Examination Concept Representations within Standard Systemized Nomenclature of Medicine - Clinical Terms.Ophthalmology. GlaucomaArticle
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
objectiveThe goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. MATERIALS AND
methodsThe National COVID Cohort Collaborative (N3C) table of laboratory measurement data-over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test.
resultsOf the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). DISCUSSION: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference.
conclusionThe pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.
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