ReviewBMC medical informatics and decision making2024
Conceptual design of a generic data harmonization process for OMOP common data model.
Review in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
21 citing papers in PubMed.
- From Bits to Bedside: From Data to Knowledge in Vascular Surgery.Journal of clinical medicine · 2026Review
- From Medical Records to AI-Ready Datasets: A Practical Guide for Clinical Researchers.Journal of clinical medicine · 2026Article
- Data harmonization processes of cancer data into the observational medical outcomes partnership common data model.Scientific reports · 2026Article
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Integrating real-world data with gold-standard longitudinal clinical and genomic data to advance precision medicine for the Alzheimer's Disease Research Center Program and beyond: a proof-of-concept data platform.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Experiences With Integrating Medical Terminologies Into User Interfaces for a Decision Support System for Primary Care: Conceptual and Development Study.JMIR medical informatics · 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
- Federated learning for heterogeneous electronic health record systems with cost effective participant selection.Scientific reports · 2026Article
- From fragmentation to federation: multi-partner OMOP implementation in Uganda enabling global real-world evidence generation.Frontiers in digital health · 2026Article
- Toward bidirectional FHIR-OMOP CDM transformations using TermX to support the secondary use of real-world health data within a patient-centered digital health paradigm.Frontiers in medicine · 2026Article
- The impact of the Social Exposome in Cardiovascular Health and Disease.Journal of precision medicine (Amsterdam, Netherlands) · 2025Article
- Reality Check: The Aspirations of the European Health Data Space Amidst Challenges in Decentralized Data Analysis.Journal of medical Internet research · 2025Article
- The impact of artificial intelligence on biomarker discovery.Emerging topics in life sciences · 2025Article
- Integrating Knowledge: The Power of Ontologies in Psychiatric Research and Clinical Informatics.Biological psychiatry · 2025Review
- Article
- Application of an Externally Developed Algorithm to Identify Research Cases and Controls from EHR Data: Trials and Triumphs.Applied clinical informatics · 2025Article
- Navigating disruption in the PID landscape: embracing opportunities and anticipating threats in the next ten years.Frontiers in immunology · 2025Review
- Biostatistical challenges in multicenter clinical trials: Best practices for collaboration and data harmonization across institutions.Bioinformation · 2025Article
- Minimum elements for reporting a multi-jurisdiction feasibility assessment of algorithms based on routinely collected health data: Health Data Research Network Canada recommendations.International journal of population data science · 2025Article
- Limitations of NHIC claim code-based surveillance and the necessity of UDI implementation in Korea.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM.
methodsFor this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps.
resultsFrom 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps.
conclusionsThe generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.
Indexed as
Identifiers
What OpenQuestion holds
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.