ArticleJournal of the American Medical Informatics Association : JAMIA2017
A longitudinal analysis of data quality in a large pediatric data research network.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 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
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- A systematic review of how missing data are handled and reported in multi-database pharmacoepidemiologic studies.Pharmacoepidemiology and drug safety · 2021Pooled it
- Leveraging the PEDSnet clinical research network and electronic health record data to enhance efficiency of trial enrollment for a rare pediatric rheumatic disease.Pediatric rheumatology online journal · 2026Article
- Article
- Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance.Learning health systems · 2025Article
- The challenges and opportunities of continuous data quality improvement for healthcare administration data.JAMIA open · 2024Article
- Electronic health records identify timely trends in childhood mental health conditions.Child and adolescent psychiatry and mental health · 2023Article
- Targeted Data Quality Analysis for a Clinical Decision Support System for SIRS Detection in Critically Ill Pediatric Patients.Methods of information in medicine · 2023Article
- Improving child health through Big Data and data science.Pediatric research · 2023Review
- Using set visualisation to find and explain patterns of missing values: a case study with NHS hospital episode statistics data.BMJ open · 2022Article
- Global Regulatory and Public Health Initiatives to Advance Pediatric Drug Development for Rare Diseases.Therapeutic innovation & regulatory science · 2022Review
- The IeDEA harmonist data toolkit: A data quality and data sharing solution for a global HIV research consortium.Journal of biomedical informatics · 2022Article
- Measuring BMI change among children and adolescents.Pediatric obesity · 2022Article
- Population-based Assessment of Cardiometabolic-related Diagnoses in Youth With Klinefelter Syndrome: A PEDSnet Study.The Journal of clinical endocrinology and metabolism · 2022Article
- Interrelationships among age at adiposity rebound, BMI during childhood, and BMI after age 14 years in an electronic health record database.Obesity (Silver Spring, Md.) · 2022Article
- Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use.Learning health systems · 2022Article
- Landscape analysis for a neonatal disease progression model of bronchopulmonary dysplasia: Leveraging clinical trial experience and real-world data.Frontiers in pharmacology · 2022Article
- A data driven learning approach for the assessment of data quality.BMC medical informatics and decision making · 2021Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectivePEDSnet is a clinical data research network (CDRN) that aggregates electronic health record data from multiple children's hospitals to enable large-scale research. Assessing data quality to ensure suitability for conducting research is a key requirement in PEDSnet. This study presents a range of data quality issues identified over a period of 18 months and interprets them to evaluate the research capacity of PEDSnet. MATERIALS AND
methodsResults were generated by a semiautomated data quality assessment workflow. Two investigators reviewed programmatic data quality issues and conducted discussions with the data partners' extract-transform-load analysts to determine the cause for each issue.
resultsThe results include a longitudinal summary of 2182 data quality issues identified across 9 data submission cycles. The metadata from the most recent cycle includes annotations for 850 issues: most frequent types, including missing data (>300) and outliers (>100); most complex domains, including medications (>160) and lab measurements (>140); and primary causes, including source data characteristics (83%) and extract-transform-load errors (9%). DISCUSSION: The longitudinal findings demonstrate the network's evolution from identifying difficulties with aligning the data to a common data model to learning norms in clinical pediatrics and determining research capability.
conclusionWhile data quality is recognized as a critical aspect in establishing and utilizing a CDRN, the findings from data quality assessments are largely unpublished. This paper presents a real-world account of studying and interpreting data quality findings in a pediatric CDRN, and the lessons learned could be used by other CDRNs.
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