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Statistical Software for Data Analysis and Clinical Trials01:12

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Clinical Research Informatics: Supporting the Research Study Lifecycle.

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    Summary
    This summary is machine-generated.

    This review highlights key Clinical Research Informatics (CRI) advancements from 2015-2016. A structured search and classification method revealed increased interest and automation in CRI, emphasizing its role across research stages.

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    Area of Science:

    • Informatics
    • Biomedical Research
    • Data Science

    Background:

    • Clinical Research Informatics (CRI) is crucial for modern healthcare research.
    • Understanding recent developments and trends in CRI is essential for advancing medical discoveries.
    • A systematic approach is needed to track and categorize the growing body of CRI literature.

    Purpose of the Study:

    • To summarize significant developments in Clinical Research Informatics (CRI) between 2015 and 2016.
    • To develop a robust strategy for searching and classifying CRI publications for better field comprehension.

    Main Methods:

    • A PubMed search strategy using Medical Subject Headings (MeSH) was employed.
    • Publications were filtered by date and other characteristics, followed by manual classification.
    • A manual review classified articles based on stages of the research study lifecycle.

    Main Results:

    • The search yielded 510 publications; 125 were identified as relevant to CRI.
    • Relevant publications were categorized across seven research lifecycle stages and a general infrastructure/standards category.
    • Key themes included electronic media applications, data standardization, and automation via data mining and big data.

    Conclusions:

    • There is growing institutional and international support for large-scale CRI projects.
    • MeSH-based searches are effective but require text word refinement to exclude related fields like computational statistics.
    • The research lifecycle model proved valuable for classifying CRI solutions based on user relevance.