SynthesisBMC medical informatics and decision making2025
Common data models and data standards for tabular health data: a systematic review.
Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.BMC medical informatics and decision making · 2026Pooled it
- Tumor metrics imaging core labs: primer for radiologists.Abdominal radiology (New York) · 2026Review
- A comparison of Fast Healthcare Interoperability Resources and Observational Medical Mutcomes Partnership electronic health record data within the All of Us Research Program.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Community-based health-focused longitudinal aging studies in East and Southeast Asia: landscape and future directions.The Lancet regional health. Western Pacific · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of health data supports knowledge-based decision-making in healthcare. Common Data Models (CDMs) and data standards facilitate the integration of diverse data sources and enable federated analysis by harmonizing data formats and terminologies.
methodsTo determine the best approaches to harmonizing patient data, we undertook a comprehensive literature search, which allowed us to identify the most popular and established CDMs (i2b2, Sentinel CDM, PCORnet CDM, OMOP CDM) and data standards (CDA, HL7 version 2, FHIR, openEHR). We established a set of criteria across the categories of Suitability, Popularity, Adaptability, Interoperability, and Support.
resultsThe CDMs and data standards are evaluated based on the defined criteria. Overall criteria the OMOP CDM and FHIR scored best. We highlight the strongest CDM and data standard for each criteria category.
conclusionGiven the unique characteristics, strengths, and weaknesses of each CDM and data standard, no single global representation can be selected. To promote broad adoption of CDMs and data standards, it is essential to enable transformation between different representations and utilize various formats within a single tool to facilitate their interoperability. Only then seamless data exchange and research across borders can be achieved. CLINICAL TRIAL NUMBER: Not applicable.
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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.