Tunis of the Center for Medical Technology Policy. The higher the level of assurance needed that a patient will benefit from a genomic technology, the greater the burden on gaining the evidence to provide that certainty, which puts downward pressure on innovation. Similarly, the more emphasis that is placed on reducing health care costs, the greater the downward pressure on economic growth and jobs. “It’s not intuitively obvious what the optimal balance of innovation and certainty is that maximizes public health over time,” said Tunis.

Tunis focused on two related barriers to the development of clinically useful genomic diagnostic tests:

•    Regulatory and reimbursement decisions rely on a binary model of approval.

•    Evidentiary thresholds for regulatory and reimbursement decisions are poorly defined.

Today, regulatory and reimbursement decisions are made as if there were a “magic point” at which suddenly something is true where previously it was false, said Tunis. “We pretend that evidence is kind of an ordinal property as opposed to a continuous function, but that’s obviously not true.”

A much better model, said Tunis, is a progressive or adaptive regulatory and reimbursement framework. In this case, approval or disapproval decisions are not made at a particular time; rather, they are made progressively over time. Coverage with evidence development (CED) and managed entry schemes are examples of such models for reimbursement with initial approval conditional upon further study. Accelerated approval would be an example of a progressive regulatory model. “Having single yes/no decisions over time is just too crude an approach,” said Tunis. “If we’re going to solve this problem with technologies generally, and certainly with diagnostics, we need to think about our regulatory decision making in a way that’s more compatible with the accumulation of knowledge and the reduction of uncertainty over time.”

Decision making today is not predictable due to a lack of clarity regarding the regulatory and reimbursement pathways, Tunis said, reiterating Siegel’s remarks. What is needed is a collaboration involving regulators, payers, clinicians, patients, and other stakeholders to define what the evidentiary thresholds should be. This cannot be done at a generic level but rather must be fit for purpose. The evidentiary threshold will need to be defined in a way that is specific to indications and therapies. “Our current regulatory or reimbursement policy framework is not aligned with the nature of evidence and the accumulation of knowledge over time. Until it is, we’re going to have a very inefficient system,” said Tunis.



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