Journal:Design and refinement of a data quality assessment workflow for a large pediatric research network
| Full article title | Design and refinement of a data quality assessment workflow for a large pediatric research network |
|---|---|
| Journal | eGEMs |
| Author(s) | Khare, Ritu; Utidjian, Levon H.; Razzaghi, Hanieh; Soucek, Victoria; Burrows, Evanette; Eckrich, Daniel; Hoyt, Richard; Weistein, Harris; Miller, Matthew W.; Soler, David; Tucker, Joshua; Bailey, L. Charles |
| Author affiliation(s) | The Children's Hospital of Philadelphia, Seattle Children’s Hospital, Nemours Children’s Health System, Nationwide Children’s Hospital |
| Primary contact | Email: kharer at email dot chop dot edu |
| Year published | 2019 |
| Volume and issue | 7(1) |
| Page(s) | 36 |
| DOI | 10.5334/egems.294 |
| ISSN | 2327-9214 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://egems.academyhealth.org/articles/10.5334/egems.294/ |
| Download | https://egems.academyhealth.org/articles/10.5334/egems.294/galley/397/download/ (PDF) |
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Abstract
Background: Clinical data research networks (CDRNs) aggregate electronic health record (EHR) data from multiple hospitals to enable large-scale research. A critical operation toward building a CDRN is conducting continual evaluations to optimize data quality. The key challenges include determining the assessment coverage on big datasets, handling data variability over time, and facilitating communication with data teams. This study presents the evolution of a systematic workflow for data quality assessment in CDRNs.
Implementation: Using a specific CDRN as a use case, a workflow was iteratively developed and packaged into a toolkit. The resultant toolkit comprises 685 data quality checks to identify any data quality issues, procedures to reconciliate with a history of known issues, and a contemporary GitHub-based reporting mechanism for organized tracking.
Results: During the first two years of network development, the toolkit assisted in discovering over 800 data characteristics and resolving over 1400 programming errors. Longitudinal analysis indicated that the variability in time to resolution (15day mean, 24day IQR) is due to the underlying cause of the issue, perceived importance of the domain, and the complexity of assessment.
Conclusions: In the absence of a formalized data quality framework, CDRNs continue to face challenges in data management and query fulfillment. The proposed data quality toolkit was empirically validated on a particular network and is publicly available for other networks. While the toolkit is user-friendly and effective, the usage statistics indicated that the data quality process is very time-intensive, and sufficient resources should be dedicated for investigating problems and optimizing data for research.
Keywords: CDRN, checks, data quality, electronic health records, GitHub, issues
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added.
