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A longitudinal analysis of data quality in a large pediatric data research network
Ritu Khare1,2, Levon Utidjian1,2, Byron J Ruth1
1Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Insights
PEDSnet identified over 2000 data quality issues, revealing improvements in pediatric data alignment and research readiness over 18 months.
Area of Science:
- Pediatric Health Research
- Clinical Data Research Networks
- Health Informatics
Background:
- PEDSnet aggregates electronic health record data from children's hospitals for large-scale research.
- Ensuring data quality is crucial for the validity of research conducted within PEDSnet.
- This study evaluates PEDSnet's research capacity by analyzing identified data quality issues.
Purpose of the Study:
- To present and interpret data quality issues identified over 18 months within PEDSnet.
- To assess the research capacity of the PEDSnet clinical data research network.
- To provide insights into data quality assessment for other clinical data research networks.
Main Methods:
- A semiautomated data quality assessment workflow was employed.
- Two investigators reviewed programmatic data quality issues.
- Discussions with data partners' extract-transform-load analysts identified issue causes.
Main Results:
- A longitudinal summary identified 2182 data quality issues across 9 submission cycles.
- The most frequent issues included missing data and outliers.
- Medications and lab measurements were the most complex domains; source data characteristics were the primary cause.
Conclusions:
- Longitudinal findings show PEDSnet's evolution in data alignment and understanding pediatric clinical norms.
- Data quality assessment findings are critical but often unpublished.
- This study offers a real-world account of data quality interpretation in a pediatric CDRN, providing lessons for others.
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.