A longitudinal analysis of data quality in a large pediatric data research network. (8th April 2017)

• Record Type: Journal Article
• Authors: Khare, Ritu; Utidjian, Levon; Ruth, Byron J; Kahn, Michael G; Burrows, Evanette; Marsolo, Keith; Patibandla, Nandan; Razzaghi, Hanieh; Colvin, Ryan; Ranade, Daksha; Kitzmiller, Melody; Eckrich, Daniel; Bailey, L Charles
• Is Part Of: Journal of the American Medical Informatics Association. Volume 24:Number 6(2017:Nov.)
• Page Start: 1072
• Page End: 1079
• Publication Date: 2017-04-08
• Subjects: CDRN -- data quality -- electronic health record -- extract-transform-load -- secondary use; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocx033
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: PEDSnet 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 Methods: Results 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. Results: The 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. Conclusion: While 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.