Applying an Implementation Science Approach to Genomic Medicine

Although it is becoming increasingly more common for clinicians to use genomic data in their practices for disease prevention, diagnosis, and treatment, the process of integrating genomic data into the practice of medicine has been a slow and challenging one. Some of the major barriers impeding the incorporation of new genomic technology into clinical practice are: the difficulty of changing routine medical practices to account for the use of genetic testing, the limited knowledge of patients and providers about genomic medicine, assessing sufficient evidence to support the use of genetic tests, privacy and data security issues, and uncertainty about reimbursement.



The field of implementation science may be able to provide insights concerning efficient ways to incorporate genomic applications into routine clinical practice. The focus of implementation science studies is to identify integration bottlenecks and optimal approaches for a given setting and ultimately to promote the up-take of research findings. To explore the potential of implementation science to improve the integration of genomics into medicine, the National Academies of Sciences, Engineering, and Medicine held a workshop in Washington, DC, in November 2015. Participants explored the challenges and opportunities of integrating genomic advances into the clinic through the lens of implementation science. This report summarizes the presentations and discussions from the workshop.

Table of Contents


Front Matter
1 Introduction and Themes of the Workshop
2 Implementation Science: Methods and Approaches
3 Engaging Large and Diverse Populations for Analysis
4 Generating Evidence During Implementation
5 Genomics and Implementation at the Level of Population Health
6 Achieving the Vision
References
Appendix A: Workshop Agenda
Appendix B: Speaker Biographical Sketches
Appendix C: Statement of Task
Appendix D: Registered Attendees
Appendix E: Implementation Science: A Background
Appendix F: Large Genetic Cohort Studies: A Background