A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system

Genet Med. 2018 Apr;20(4):390-396. doi: 10.1038/gim.2017.122. Epub 2017 Aug 10.


Genomic technologies should demonstrate analytical and clinical validity and clinical utility prior to wider adoption in clinical practice. However, the question of clinical utility remains unanswered for many genomic technologies. In this paper, we propose three building blocks for rapid generation of evidence on clinical utility of promising genomic technologies that underpin clinical and policy decisions. We define promising genomic tests as those that have proven analytical and clinical validity. First, risk-sharing agreements could be implemented between payers and manufacturers to enable temporary coverage that would help incorporate promising technologies into routine clinical care. Second, existing data networks, such as the Sentinel Initiative and the National Patient-Centered Clinical Research Network (PCORnet) could be leveraged, augmented with genomic information to track the use of genomic technologies and monitor clinical outcomes in millions of people. Third, endorsement and engagement from key stakeholders will be needed to establish this collaborative model for rapid evidence generation; all stakeholders will benefit from better information regarding the clinical utility of these technologies. This collaborative model can create a multipurpose and reusable national resource that generates knowledge from data gathered as part of routine care to drive evidence-based clinical practice and health system changes.

Publication types

  • Review

MeSH terms

  • Capital Financing
  • Decision Making
  • Delivery of Health Care* / economics
  • Delivery of Health Care* / legislation & jurisprudence
  • Delivery of Health Care* / methods
  • Evidence-Based Practice* / economics
  • Evidence-Based Practice* / legislation & jurisprudence
  • Evidence-Based Practice* / methods
  • Genetic Testing* / methods
  • Genomics* / methods
  • Health Policy
  • Humans