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- Title
A longitudinal analysis of data quality in a large pediatric data research network.
- 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
- Abstract
<bold>Objective: </bold>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.<bold>Materials and Methods: </bold>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.<bold>Results: </bold>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%).<bold>Discussion: </bold>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.<bold>Conclusion: </bold>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as a critical aspect in establishing and utilizing a CDRN, the findings from data quality assessments are largely unpublished.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.
- Subjects
ELECTRONIC health records; DATA quality; METADATA; CHILDREN'S hospitals; MEDICAL informatics; LONGITUDINAL method; MEDICAL research; RESEARCH funding; ACQUISITION of data; STANDARDS
- Publication
Journal of the American Medical Informatics Association, 2017, Vol 24, Issue 6, p1072
- ISSN
1067-5027
- Publication type
journal article
- DOI
10.1093/jamia/ocx033