Journal Articles

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

August 17, 2017

Lu CY 1, Williams MS 2, Ginsburg GS 3, Toh S 1, Brown JS 1, Khoury MJ 4 (2017). A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system. Genetics in Medicine.

Author Affiliations

1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
2. Genomic Medicine Institute Geisinger Health System, Danville, Pennsylvania, USA.
3. Center for Applied Genomics &Precision Medicine, Duke University, Durham, North Carolina, USA.
4. Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Abstract

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.

Comments are closed.

Connect With Us