ArticleJournal of the American Medical Informatics Association : JAMIA2018
Exploring completeness in clinical data research networks with DQe-c.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Data quality assessment in healthcare, dimensions, methods and tools: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality Study.JMIR medical informatics · 2026Article
- Article
- Electronic Health Record Data Quality and Performance Assessments: Scoping Review.JMIR medical informatics · 2024Article
- Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024Review
- A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Outliers in diagnosis ratios: A clue toward possibly absent data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Effect of PIK3CA variants on glioma-related epilepsy and response to treatment.Epilepsy research · 2021Article
- Clinical Research Informatics: Contributions from 2018.Yearbook of medical informatics · 2019Article
- A federated EHR network data completeness tracking system.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- A Data Element-Function Conceptual Model for Data Quality Checks.EGEMS (Washington, DC) · 2019Article
- Biomedical informatics and data science: evolving fields with significant overlap.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Objective: To provide an open source, interoperable, and scalable data quality assessment tool for evaluation and visualization of completeness and conformance in electronic health record (EHR) data repositories. Materials and Methods: This article describes the tool's design and architecture and gives an overview of its outputs using a sample dataset of 200 000 randomly selected patient records with an encounter since January 1, 2010, extracted from the Research Patient Data Registry (RPDR) at Partners HealthCare. All the code and instructions to run the tool and interpret its results are provided in the Supplementary Appendix. Results: DQe-c produces a web-based report that summarizes data completeness and conformance in a given EHR data repository through descriptive graphics and tables. Results from running the tool on the sample RPDR data are organized into 4 sections: load and test details, completeness test, data model conformance test, and test of missingness in key clinical indicators. Discussion: Open science, interoperability across major clinical informatics platforms, and scalability to large databases are key design considerations for DQe-c. Iterative implementation of the tool across different institutions directed us to improve the scalability and interoperability of the tool and find ways to facilitate local setup. Conclusion: EHR data quality assessment has been hampered by implementation of ad hoc processes. The architecture and implementation of DQe-c offer valuable insights for developing reproducible and scalable data science tools to assess, manage, and process data in clinical data repositories.
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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.