Evidence-based medicine and big genomic data
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Evidence-based medicine and big genomic data

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  • English
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    • Alternative Title:
      Hum Mol Genet
    • Personal Author:
    • Description:
      Genomic and other related big data (Big Genomic Data, BGD for short) are ushering a new era of precision medicine. This overview discusses whether principles of evidence-based medicine hold true for BGD and how they should be operationalized in the current era. Major evidence-based medicine principles include the systematic identification, description and analysis of the validity and utility of BGD, the combination of individual clinical expertise with individual patient needs and preferences, and the focus on obtaining experimental evidence, whenever possible. BGD emphasize information of single patients with an overemphasis on N-of-1 trials to personalize treatment. However, large-scale comparative population data remain indispensable for meaningful translation of BGD personalized information. The impact of BGD on population health depends on its ability to affect large segments of the population. While several frameworks have been proposed to facilitate and standardize decision making for use of genomic tests, there are new caveats that arise from BGD that extend beyond the limitations that were applicable for more simple genetic tests. Non-evidence-based use of BGD may be harmful and result in major waste of healthcare resources. Randomized controlled trials will continue to be the strongest arbitrator for the clinical utility of genomic technologies, including BGD. Research on BGD needs to focus not only on finding robust predictive associations (clinical validity) but also more importantly on evaluating the balance of health benefits and potential harms (clinical utility), as well as implementation challenges. Appropriate features of such useful research on BGD are discussed.
    • Subjects:
    • Source:
      Hum Mol Genet. 27(R1):R2-R7
    • Pubmed ID:
      29474574
    • Pubmed Central ID:
      PMC6247896
    • Document Type:
      Journal Article
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