ArticleJMIR medical informatics2023
Methods Used in the Development of Common Data Models for Health Data: Scoping Review.
Article in JMIR medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses 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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Pharmacoepidemiologic Research Based on Common Data Models: Systematic Review and Bibliometric Analysis.JMIR medical informatics · 2025Pooled it
- Leveraging Real-World Data for Clinical Decision Making in Geriatric Oncology.Drugs & aging · 2026Review
- Contextualizing Evaluation in Research Consortia: A Reflective Case Study from the Research Centers in Minority Institutions (RCMIs) Program.International journal of environmental research and public health · 2026Article
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Evaluating the Prototype of a Clinical Decision Support System in Primary Care: Qualitative Study.JMIR formative research · 2025Article
- Common Data Element (CDE) Success from Community-Engagement Not Mandates: If You Build It [Data Warehouse], Will They [Participants] Come?American journal of public health · 2025Article
- Kineret: Israel's Largest Hospital Network Transformed into the OMOP common data model for collaborative research.PloS one · 2025Article
- Utility of Treatment Pattern Analysis Using a Common Data Model: A Scoping Review.Healthcare informatics research · 2025Article
- A Current Perspective of Medical Informatics Developments for a Clinical Translation of (Non-coding)RNAs and Single-Cell Technologies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Decades in the Making: The Evolution of Digital Health Research Infrastructure Through Synthetic Data, Common Data Models, and Federated Learning.Journal of medical Internet research · 2024Article
- Trends in Antidiabetic Drug Use and Safety of Metformin in Diabetic Patients with Varying Degrees of Chronic Kidney Disease from 2010 to 2021 in Korea: Retrospective Cohort Study Using the Common Data Model.Pharmaceuticals (Basel, Switzerland) · 2024Article
- How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.Orphanet journal of rare diseases · 2024Article
- Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care.Journal of medical Internet research · 2024Article
- Methods Used in the Development of Common Data Models for Health Data: Scoping Review.JMIR medical informatics · 2023Article
- Conversion of CPRD AURUM Data into the OMOP Common Data Model.Informatics in medicine unlocked · 2023Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCommon data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems.
objectiveThis scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level.
methodsThis scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users' needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients' representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development.
resultsWe finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process.
conclusionsThe included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future.
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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.