Eintrag weiter verarbeiten
A longitudinal analysis of data quality in a large pediatric data research network
Gespeichert in:
| Zeitschriftentitel: | Journal of the American Medical Informatics Association |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , , , , |
| In: | Journal of the American Medical Informatics Association, 24, 2017, 6, S. 1072-1079 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| Zusammenfassung: | <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Objective</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Materials and Methods</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>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 (&gt;300) and outliers (&gt;100); most complex domains, including medications (&gt;160) and lab measurements (&gt;140); and primary causes, including source data characteristics (83%) and extract-transform-load errors (9%).</jats:p> </jats:sec> <jats:sec> <jats:title>Discussion</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>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as a critical aspect in establishing and utilizing a CDRN, the findings from data quality assessments are largely unpublished.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.</jats:p> </jats:sec> |
|---|---|
| Umfang: | 1072-1079 |
| ISSN: |
1067-5027
1527-974X |
| DOI: | 10.1093/jamia/ocx033 |