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Suggested Citation:"6 Closing Remarks." Institute of Medicine. 2009. Systems for Research and Evaluation for Translating Genome-Based Discoveries for Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12691.
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Page 65
Suggested Citation:"6 Closing Remarks." Institute of Medicine. 2009. Systems for Research and Evaluation for Translating Genome-Based Discoveries for Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12691.
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Page 66
Suggested Citation:"6 Closing Remarks." Institute of Medicine. 2009. Systems for Research and Evaluation for Translating Genome-Based Discoveries for Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12691.
×
Page 67
Suggested Citation:"6 Closing Remarks." Institute of Medicine. 2009. Systems for Research and Evaluation for Translating Genome-Based Discoveries for Health: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12691.
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Page 68

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6 Closing Remarks In the final session, three Roundtable members summarized what they each took away as the key messages of the workshop, and their thoughts on next steps. SHARON TERRY Genetic Alliance One of the problems in this society, Terry said, is dichotomist thinking: an optimistic or a pessimistic view; genetics versus other kinds of medicine; research versus clinical. In many cases it would be better to consider issues as continuums. One theme of the day was whether the current research infrastructure is adequate to assess genomic innovation. On the other hand, questions were raised as to whether genomics is really different from other kinds of biomedical innovation. Are genetic innovations and genome-based health being layered on top of a broken health care system? Or perhaps the ques- tion is whether genomics or genetics can actually help fix what is known to be broken in the system. Can the same mistakes be avoided and inequities eliminated, or will genome-based medicine only compound the problems of the current health care system, particularly in terms of costs? A task for the workshop was to consider what evidence is needed and 65

66 SYSTEMS FOR RESEARCH AND EVALUATION for whom. Teutsch showed how different stakeholders need different types of evidence for their respective decision-making processes (see Chapter 2, Figure 2-5). However, in doing this, Terry said, one must avoid dichoto- mies, or creating an “us against them” culture. Stakeholders must engage in crosstalk to understand where there are synergies or overlaps, and to develop continuums. In closing, Terry said there is a great deal of stress in the system result- ing from the difficulty of evidence development and assessment. A critical step is to develop novel partnerships to elucidate the value of genomics. Industry must work with academia, government, and consumers so that stakeholders understand their overlapping and disparate concerns, and then understand the investment each needs to make for post licensure studies, large cohort studies, registries and biobanks, and other activities that are needed to generate the evidence and address stakeholder concerns. SHARON KARDIA, PH.D. University of Michigan, School of Public Health One goal of the workshop was to consider the integrated systems needed for evidence creation. The presentations demonstrated that these integrated systems are up and running in many ways. Some are just starting, some are more mature, and hopefully they will contribute to the development of a learning health care system. The evolution of genomics technologies is running parallel with the advance of other new innovations and, therefore, our systems needs to be adaptive. Kardia recalled Berg’s comment that evi- dence-based research takes great care and infrastructure to conduct, and yet evidence that actually changes practice is rare. This is an important lesson, Kardia said. Although one strives to be efficient, effective, and streamlined to produce tangible results, as people, our processes are naturally messy. Even in that messiness, if one focuses on the common public good, pursues excellence in all arenas, and trusts in a collective process, then eventually progress will be made. In the past year, there has been progress in terms of the crosstalk among stakeholders, increased awareness of the key issues, and new ideas about how to translate genomics into improved health. This is an adaptive process, Kardia concluded. There is a balance between doing and thinking. Are people spending enough time in that deeper thinking process, Kardia asked, to find the tipping points where things that seem so slow to catalyze can more quickly come to fruition? Currently, there is a sense of frustration in trying to figure out what to do

CLOSING REMARKS 67 to speed up translation efforts. However, most people in this field are so focused on activities such as obtaining funding or creating stakeholder rela- tionships that we lack a cognitive map of the complex issues. As a result, it is a very important charge for the Institute of Medicine Roundtable to spend more time thinking about and articulating this bigger view of the issues. WYLIE BURKE, M.D., PH.D. University of Washington Throughout the day it was clear that funding matters for genomic trans- lation, Burke concluded. Careful thought must be given to what is funded, with attention to funding both infrastructures and studies. Someone asked why some current guidelines are not evidence based. An alternative ques- tion could be: Why are there guidelines when there is no evidence? Guide- lines exist because clinicians need to act. Therefore, careful consideration should be given to what information clinicians really need, and what new or different funding profiles may be needed to obtain that information. Other discussion focused on Institutional Review Boards, the Health Insurance Portability and Accountability Act (HIPAA), and unique patient identifiers, showing the need for more crosstalk about those issues. The discussion also made clear that tone must be very realistic about what is achievable in terms of creating research infrastructure, and accepting that not everything can, or should, be tested by a randomized controlled trial. In funding research to acquire information for decision making, there is a need to determine what the right combination of evidence is. Thinking schematically or conceptually, what kinds of innovation need what kinds of evidence? What type of research infrastructures can produce meaningful evidence about what kinds of different applications? Thinking ahead to where the technology is going is extremely important, Burke concluded. In research, as in clinical care, the cognitive piece tends to be underfunded. A priority-setting process is clearly needed. The Round- table convenes many different stakeholders and, therefore, a Roundtable discussion about appropriate processes for priority setting would provide a forum for interaction on this topic among the different stakeholders.

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With the advent of genome-wide association studies, numerous associations between specific gene loci and complex diseases have been identified—for breast cancer, coronary artery disease, and asthma, for example. This rapidly advancing field of genomics has stirred great interest in "personalized" health care from both the public and private sectors. The hope is that using genomic information in clinical care will lead to reduced health care costs and improved health outcomes as therapies are tailored to the genetic susceptibilities of patients.

A variety of genetically based health care innovations have already reached the marketplace, but information about the clinical use of these treatments and diagnostics is limited. Currently data do not provide information about how a genomic test impacts clinical care and patient health outcomes—other approaches are needed to garner such information.

This volume summarizes a workshop to address central questions related to the development of systems to evaluate clinical use of health care innovations that stem from genome-based research:

What are the practical realities of creating such systems?

What different models could be used?

What are the strengths and weaknesses of each model?

How effectively can such systems address questions about health outcomes?

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