ArticleJAMIA open2021
Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure.
Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Linking Electronic Health Records for Multiple Sclerosis Research: Comparative Study of Deterministic, Probabilistic, and Machine Learning Linkage Methods.JMIR medical informatics · 2026Article
- From Concept to Implementation: A Policy and Standards Roadmap for a Global Medical Record System for Travelers with Chronic Disease.Clinical epidemiology · 2026Article
- ArcMAP - ML assisted medical concept mapping to accelerate NHS data standardization.Frontiers in digital health · 2026Article
- Harmonizing patient health data into the OMOP common data model: a case study using tuberculosis data from Douala General Hospital, Cameroon.Frontiers in digital health · 2026Article
- Evaluating the impact of OMOP-CDM on data quality insight generation in respiratory disease management.Frontiers in big data · 2026Article
- Using ontologies to facilitate healthcare process mining and analysis.Journal of intelligent information systems · 2026Article
- Streamlining electronic medical record data extraction and validation in digital hospitals: A systematic review to identify optimal approaches and methods.Learning health systems · 2025Article
- Kineret: Israel's Largest Hospital Network Transformed into the OMOP common data model for collaborative research.PloS one · 2025Article
- FOUNTAIN: a modular research platform for integrated real-world evidence generation.BMC medical research methodology · 2024Article
- Implementation of the Observational Medical Outcomes Partnership Model in Electronic Medical Record Systems: Evaluation Study Using Factor Analysis and Decision-Making Trial and Evaluation Laboratory-Best-Worst Methods.JMIR medical informatics · 2024Article
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.JAMIA open · 2024Article
- Conceptual design of a generic data harmonization process for OMOP common data model.BMC medical informatics and decision making · 2024Review
- Developing RxNorm Extension: A Step Toward Global Drug Data Harmonization in Observational Drug Research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned.JAMIA open · 2023Article
- MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.medRxiv : the preprint server for health sciences · 2023Article
- Assessing the Use of German Claims Data Vocabularies for Research in the Observational Medical Outcomes Partnership Common Data Model: Development and Evaluation Study.JMIR medical informatics · 2023Article
- Clinical Informatics Approaches to Facilitate Cancer Data Sharing.Yearbook of medical informatics · 2023Review
- Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelligence · 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
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveThe aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms. MATERIALS AND
methodsUsing heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers.
resultsWe identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD).
conclusionOur study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research.
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