ArticleApplied clinical informatics2019
Incrementally Transforming Electronic Medical Records into the Observational Medical Outcomes Partnership Common Data Model: A Multidimensional Quality Assurance Approach.
Article in Applied clinical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Albuminuria Changes as a surrogate endpoint inmedRxiv : the preprint server for health sciences · 2026Article
- Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study.Journal of medical Internet research · 2026Article
- Design and evaluation of an automated pediatric acute lymphoblastic leukemia registry from clinical data warehouses.BMC medical informatics and decision making · 2026Article
- Extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model for Critical Care Medicine: A Framework for Standardizing Complex ICU Data Using the Society of Critical Care Medicine's Critical Care Data Dictionary (C2D2).Critical care medicine · 2026Article
- Integrative Proteome- and Phenome-Wide Assessment Uncovers Causal Protein Drivers and Drug Targets for Heterogeneous Kidney Diseases.medRxiv : the preprint server for health sciences · 2025Article
- Development of a big data platform for collecting and utilizing clinical information from the Korea Biobank Network.BMC medical informatics and decision making · 2025Article
- A framework for sharing of clinical and genetic data for precision medicine applications.Nature medicine · 2024Article
- Evolution of a Graph Model for the OMOP Common Data Model.Applied clinical informatics · 2024Article
- GLP1R Gene Expression and Kidney Disease Progression.JAMA network open · 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
- Article
- A Federated Database for Obesity Research: An IMI-SOPHIA Study.Life (Basel, Switzerland) · 2024Article
- Hospital and Patient Factors Affecting Veterans' Hospital Choice.Medical care research and review : MCRR · 2024Article
- Outcomes of Veterans Treated in Veterans Affairs Hospitals vs Non-Veterans Affairs Hospitals.JAMA network open · 2023Article
- An Extract-Transform-Load Process Design for the Incremental Loading of German Real-World Data Based on FHIR and OMOP CDM: Algorithm Development and Validation.JMIR medical informatics · 2023Article
- Lifelong Association of Disorders Related to Military Trauma with Subsequent Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2023Article
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- Healthcare data quality assessment for improving the quality of the Korea Biobank Network.PloS one · 2023Article
- Conversion of Automated 12-Lead Electrocardiogram Interpretations to OMOP CDM Vocabulary.Applied clinical informatics · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe development and adoption of health care common data models (CDMs) has addressed some of the logistical challenges of performing research on data generated from disparate health care systems by standardizing data representations and leveraging standardized terminology to express clinical information consistently. However, transforming a data system into a CDM is not a trivial task, and maintaining an operational, enterprise capable CDM that is incrementally updated within a data warehouse is challenging.
objectivesTo develop a quality assurance (QA) process and code base to accompany our incremental transformation of the Department of Veterans Affairs Corporate Data Warehouse health care database into the Observational Medical Outcomes Partnership (OMOP) CDM to prevent incremental load errors.
methodsWe designed and implemented a multistage QA) approach centered on completeness, value conformance, and relational conformance data-quality elements. For each element we describe key incremental load challenges, our extract, transform, and load (ETL) solution of data to overcome those challenges, and potential impacts of incremental load failure.
resultsCompleteness and value conformance data-quality elements are most affected by incremental changes to the CDW, while updates to source identifiers impact relational conformance. ETL failures surrounding these elements lead to incomplete and inaccurate capture of clinical concepts as well as data fragmentation across patients, providers, and locations.
conclusionDevelopment of robust QA processes supporting accurate transformation of OMOP and other CDMs from source data is still in evolution, and opportunities exist to extend the existing QA framework and tools used for incremental ETL QA processes.
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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.