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Diffusion and Use of Genomic Innovations in Health and Medicine: Workshop Summary
Not only is the U.S. health care system today fragmented and complex, but it is a system that delivers very poor value for the money as compared to health care systems in other developed countries.
Innovation is a learning process. Most new technologies are relatively primitive when they get to market, and benefits and risks often are refined over time. Sometimes, unanticipated new uses of technology occur as people use the technology. There is a question about how the efficiency of that process can be increased since it frequently takes a long time to obtain a good understanding of the uses and value of a technology. One approach might be to think explicitly about capacity development—that is, not just encouraging innovation but encouraging the capacity to incorporate new innovations into the health care system.
The theme heard over and over in the workshop was evidence. Everyone agrees, although from a variety of perspectives, that evidence is needed to support innovation in health care. It was suggested that evidence is needed to show that an innovation will make a difference in outcomes that patients will notice. Evidence that shows an innovation will help clinicians do better at something they are already doing or do something helpful and beneficial that they previously were unable to do is also important.
From a health technology assessment viewpoint, randomized clinical trials (RCTs) are a gold standard, but they are not the usual way in which technology is evaluated because there are not very many RCTs. Health technology assessment usually needs to use a combination of indirect evidence and causal inference, and that leads to a number of serious questions. How does one make optimal use of indirect evidence? When should RCTs be insisted upon?
What is the role of the post-market evaluation process; that is, for that early diffusion process where there is learning about the technology? What is the role for evaluation at that stage, either RCTs or observational data? What kinds of infrastructures would promote the best use of post-market evaluation?
Throughout the discussion of evidence there was a recurrent idea that further work on evidence standards is needed. One important point made is that test developers face different needs from regulators than they face from payers. There is a need to think about the hurdles for regulation and payment being the same.
When is a leap of inference justified?
What is the role of cost-effectiveness or other economic indicators in determining whether something comes to market?
Is there any way to enable health technology assessment to occur earlier in the process, beginning even pre-market?