Dataset Information

A longitudinal analysis of data quality in a large pediatric data research network.

ABSTRACT: 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as a critical aspect in establishing and utilizing a CDRN, the findings from data quality assessments are largely unpublished.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.

SUBMITTER: Khare R

PROVIDER: S-EPMC6259665 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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A longitudinal analysis of data quality in a large pediatric data research network.

Khare Ritu R Utidjian Levon L Ruth Byron J BJ Kahn Michael G MG Burrows Evanette E Marsolo Keith K Patibandla Nandan N Razzaghi Hanieh H Colvin Ryan R Ranade Daksha D Kitzmiller Melody M Eckrich Daniel D Bailey L Charles LC

Journal of the American Medical Informatics Association : JAMIA 20171101 6

<h4>Objective</h4>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.<h4>Materials and methods</h4>Results were generated by a semi ...[more]

PMID: 28398525

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Project description:BackgroundIn 2012 and 2013, we conducted a social network survey of a new translational research network (TRN) designed to deliver better care to cancer patients. Results of these two surveys showed that silos of researchers and clinicians existed before the TRN was established but that the network had mediated collaborative relationships. This paper reports on a third social network survey of the TRN and focusses on the structure of the collaborative arrangements among members.MethodsMembers of the TRN were invited to complete an on-line, whole network survey in May 2015. The survey asked respondents to identify personal impacts, outputs and wider outcomes attributable to their TRN membership. The final question asked respondents to select the name of TRN members with whom they had collaborated either formally or informally. For each member nominated, they were asked to say whether they had known this person before joining the TRN.ResultsResponse rate was 70 %. Over 4 years, the TRN has grown in size from 68 to 244 members. Relationships within and across the TRN have become more collaborative and interactive, with 1658 collaborative ties between members and over 40 % of ties with people unknown to participants before they joined the TRN. This points to a well-functioning network which has retained its focus on the original goals of the TRN and has fostered collaboration between researchers, clinicians, managers, consumers and TRN operational staff. This survey shows that the TRN's impact goes beyond outcomes from formal TRN-funded projects. About one third of respondents could list projects not directly funded by the TRN but which are attributed to TRN membership. Examples of practice change brought about through the TRN were given by 77 % of respondents. A substantial risk factor for the future is the high levels of dependency on key or central TRN participants.ConclusionsThe structure of the TRN with its active central actors and brokers has been able to foster collaboration on implementation initiatives that result in practice change. The role of a social professional network in driving this collaboration is shown.

Project description:ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ?25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e., no immunoprecipitation and mock immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.

Dataset Information

A longitudinal analysis of data quality in a large pediatric data research network.

Publications

A longitudinal analysis of data quality in a large pediatric data research network.

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