ArticleJournal of the American Medical Informatics Association : JAMIA2026
A multifaceted approach to advancing data quality and fitness standards in multi-institutional networks.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
objectiveTo construct a data quality (DQ) system that incorporates combinations of methods to evaluate data characteristics and analytic fitness across research questions for multiple uses. MATERIALS AND
methodsDrawing from experience of other data quality programs, network data extraction needs, and recurring study requirements, we developed 5 standards to guide development of a modular, multifaceted data quality system. These included annotation and documentation, ability to measure research readiness, reproducibility across networks, flexibility for the user, and interpretability to research and project teams. Implementation of checks based on these principles focused on reusability and interactive visualization of results.
resultsWe identified 10 check types producing over 444 check applications and deployed them in 2 multi-institutional networks. Check types span structural conformance to a data model, utility for common research needs, and study-specific customization. All check types are customizable without dependencies between them. A dashboard visualizes results, permitting adjustments based on number of data sources, need for source masking, and the user's focus. All components can be applied as written to any data source using OMOP and are readily modified for other data models. DISCUSSION: We have extended previous work through our novel and multifaceted approach to data quality assessment, addressing needs in both network data improvement and research usage. We developed a capable and deployable system rather than tailoring to specific use cases.
conclusionOur novel DQ assessment system provides essential components for future standardization and collaboration to improve fitness of clinical data for intended use.
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.