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Automated Tools for Clinical Research Data Quality Control using NCI Common Data Elements.

Hudson CL, Topaloglu U, Bian J, Hogan W, Kieber-Emmons T - AMIA Jt Summits Transl Sci Proc (2014)

Bottom Line: Clinical research data generated by a federation of collection mechanisms and systems often produces highly dissimilar data with varying quality.Poor data quality can result in the inefficient use of research data or can even require the repetition of the performed studies, a costly process.This work presents two tools for improving data quality of clinical research data relying on the National Cancer Institute's Common Data Elements as a standard representation of possible questions and data elements to A: automatically suggest CDE annotations for already collected data based on semantic and syntactic analysis utilizing the Unified Medical Language System (UMLS) Terminology Services' Metathesaurus and B: annotate and constrain new clinical research questions though a simple-to-use "CDE Browser." In this work, these tools are built and tested on the open-source LimeSurvey software and research data analyzed and identified to contain various data quality issues captured by the Comprehensive Research Informatics Suite (CRIS) at the University of Arkansas for Medical Sciences.

View Article: PubMed Central - PubMed

Affiliation: University of Arkansas for Medical Sciences, Little Rock, AR.

ABSTRACT
Clinical research data generated by a federation of collection mechanisms and systems often produces highly dissimilar data with varying quality. Poor data quality can result in the inefficient use of research data or can even require the repetition of the performed studies, a costly process. This work presents two tools for improving data quality of clinical research data relying on the National Cancer Institute's Common Data Elements as a standard representation of possible questions and data elements to A: automatically suggest CDE annotations for already collected data based on semantic and syntactic analysis utilizing the Unified Medical Language System (UMLS) Terminology Services' Metathesaurus and B: annotate and constrain new clinical research questions though a simple-to-use "CDE Browser." In this work, these tools are built and tested on the open-source LimeSurvey software and research data analyzed and identified to contain various data quality issues captured by the Comprehensive Research Informatics Suite (CRIS) at the University of Arkansas for Medical Sciences.

No MeSH data available.


Related in: MedlinePlus

CDE Importer for LimeSurvey
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Related In: Results  -  Collection


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f3-1861381: CDE Importer for LimeSurvey

Mentions: To apply syntactic constraints and annotate new clinical surveys with CDEs, the second tool, the CDE Importer, was developed as an plug in for the LimeSurvey to allow users to browse for and insert constraints defined by CDEs for each of their survey questions, explicitly annotating the survey questions with the CDE syntactic and, implicitly, semantic information in the same process. The four primary steps for annotating new survey questions with the CDE Browser include 1: browsing for and selecting a CDE using search terms, 2: discovering any associated questions defined for the selected CDE, 3: browsing for and selecting from the list of returned associated questions (if there are any), and 4: review constraints and finalize any insertion options. Screenshots of the application executing each of these four tasks is shown in Figure 3 below:


Automated Tools for Clinical Research Data Quality Control using NCI Common Data Elements.

Hudson CL, Topaloglu U, Bian J, Hogan W, Kieber-Emmons T - AMIA Jt Summits Transl Sci Proc (2014)

CDE Importer for LimeSurvey
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4333694&req=5

f3-1861381: CDE Importer for LimeSurvey
Mentions: To apply syntactic constraints and annotate new clinical surveys with CDEs, the second tool, the CDE Importer, was developed as an plug in for the LimeSurvey to allow users to browse for and insert constraints defined by CDEs for each of their survey questions, explicitly annotating the survey questions with the CDE syntactic and, implicitly, semantic information in the same process. The four primary steps for annotating new survey questions with the CDE Browser include 1: browsing for and selecting a CDE using search terms, 2: discovering any associated questions defined for the selected CDE, 3: browsing for and selecting from the list of returned associated questions (if there are any), and 4: review constraints and finalize any insertion options. Screenshots of the application executing each of these four tasks is shown in Figure 3 below:

Bottom Line: Clinical research data generated by a federation of collection mechanisms and systems often produces highly dissimilar data with varying quality.Poor data quality can result in the inefficient use of research data or can even require the repetition of the performed studies, a costly process.This work presents two tools for improving data quality of clinical research data relying on the National Cancer Institute's Common Data Elements as a standard representation of possible questions and data elements to A: automatically suggest CDE annotations for already collected data based on semantic and syntactic analysis utilizing the Unified Medical Language System (UMLS) Terminology Services' Metathesaurus and B: annotate and constrain new clinical research questions though a simple-to-use "CDE Browser." In this work, these tools are built and tested on the open-source LimeSurvey software and research data analyzed and identified to contain various data quality issues captured by the Comprehensive Research Informatics Suite (CRIS) at the University of Arkansas for Medical Sciences.

View Article: PubMed Central - PubMed

Affiliation: University of Arkansas for Medical Sciences, Little Rock, AR.

ABSTRACT
Clinical research data generated by a federation of collection mechanisms and systems often produces highly dissimilar data with varying quality. Poor data quality can result in the inefficient use of research data or can even require the repetition of the performed studies, a costly process. This work presents two tools for improving data quality of clinical research data relying on the National Cancer Institute's Common Data Elements as a standard representation of possible questions and data elements to A: automatically suggest CDE annotations for already collected data based on semantic and syntactic analysis utilizing the Unified Medical Language System (UMLS) Terminology Services' Metathesaurus and B: annotate and constrain new clinical research questions though a simple-to-use "CDE Browser." In this work, these tools are built and tested on the open-source LimeSurvey software and research data analyzed and identified to contain various data quality issues captured by the Comprehensive Research Informatics Suite (CRIS) at the University of Arkansas for Medical Sciences.

No MeSH data available.


Related in: MedlinePlus