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A longitudinal analysis of data quality in a large pediatric data research network
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| 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)
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| Schlagwörter: |
| author_facet |
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 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 |
|---|---|
| author |
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 |
| spellingShingle |
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 Journal of the American Medical Informatics Association A longitudinal analysis of data quality in a large pediatric data research network Health Informatics |
| author_sort |
khare, ritu |
| spelling |
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 1067-5027 1527-974X Oxford University Press (OUP) Health Informatics http://dx.doi.org/10.1093/jamia/ocx033 <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> A longitudinal analysis of data quality in a large pediatric data research network Journal of the American Medical Informatics Association |
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Oxford University Press (OUP), 2017 |
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| title |
A longitudinal analysis of data quality in a large pediatric data research network |
| title_unstemmed |
A longitudinal analysis of data quality in a large pediatric data research network |
| title_full |
A longitudinal analysis of data quality in a large pediatric data research network |
| title_fullStr |
A longitudinal analysis of data quality in a large pediatric data research network |
| title_full_unstemmed |
A longitudinal analysis of data quality in a large pediatric data research network |
| title_short |
A longitudinal analysis of data quality in a large pediatric data research network |
| title_sort |
a longitudinal analysis of data quality in a large pediatric data research network |
| topic |
Health Informatics |
| url |
http://dx.doi.org/10.1093/jamia/ocx033 |
| publishDate |
2017 |
| physical |
1072-1079 |
| description |
<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> |
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| author | 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 |
| author_facet | 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, 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 |
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| description | <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> |
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| institution | DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-D161, DE-Zwi2, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1 |
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| spelling | 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 1067-5027 1527-974X Oxford University Press (OUP) Health Informatics http://dx.doi.org/10.1093/jamia/ocx033 <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> A longitudinal analysis of data quality in a large pediatric data research network Journal of the American Medical Informatics Association |
| spellingShingle | 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, Journal of the American Medical Informatics Association, A longitudinal analysis of data quality in a large pediatric data research network, Health Informatics |
| title | A longitudinal analysis of data quality in a large pediatric data research network |
| title_full | A longitudinal analysis of data quality in a large pediatric data research network |
| title_fullStr | A longitudinal analysis of data quality in a large pediatric data research network |
| title_full_unstemmed | A longitudinal analysis of data quality in a large pediatric data research network |
| title_short | A longitudinal analysis of data quality in a large pediatric data research network |
| title_sort | a longitudinal analysis of data quality in a large pediatric data research network |
| title_unstemmed | A longitudinal analysis of data quality in a large pediatric data research network |
| topic | Health Informatics |
| url | http://dx.doi.org/10.1093/jamia/ocx033 |