1. Session 3: Coverage and Reimbursement for Genetic Testing
1. What is missing that would be critical to having a positive coverage decision for exome testing of cancers prior to therapy selection?
2. Since reimbursement, even more so that regulatory, challenges are frequently cited as the single most significant impediment to personalized/precision medicine, what can payers do to ensure the equitable availability of appropriately priced, high quality genomic tests that improve health and well-being?
What is missing that would be critical to achieve reimbursement? Why is there so much concern about cost?
4. In the future, components of the Affordable Care Act may lead to changes in the overall oncology payment structure in which bundled payments and system-level value-based care agreements become increasingly common. What impact might such changes have on incentives to use complex genetic/molecular testing and the treatment recommendations that may follow?

2. For knowledge generation, broad panels will be critical
Recommendations: Newcomer
For knowledge generation, broad panels will be critical
Consider tiered levels of evidence to determine actionability of rare variants in product labeling
Consider establishing in-house expertise and external advisors to interpret diverse pipelines of data (non-clinical, registry, EMR, clinical trial) on actionability

3. Recommendations: Putcha
Coding
Develop “better” codes that recognize clinically important differences among tests that impact their cost and value (because you cannot pay for “value” if you cannot identify it)
PAMA: New advanced diagnostic laboratory test (ADLT) codes
PAMA: Codes that distinguish FDA-cleared/approved tests from LDTs
AMA: Proprietary laboratory assay (PLA) codes
If necessary, payers can further clarify appropriate use of codes (e.g., MolDX billing and coding guidelines for NGS-based somatic and germline panels–M00127 and M00130):
Do not stack Tier 1 or 2 codes for panels
81445, and apply only to tumor tissue-only panels (i.e., not “liquid biopsies” or matched tumor-normal panels) reporting only somatic variants (i.e., not germline), specifically SNVs and small indels (defined as ≤ 20 bp), not CNVs and rearrangements (i.e., “hotspot” panels)
If no codes accurately describe the test, code as and submit a dossier.

4. Recommendations: Putcha
Coverage
Transparently define and consistently apply criteria for analytical validity (AV), clinical validity (CV), and clinical utility (CU) that still allow adequate flexibility to consider nuances related to clinical context, evolving standards of care, patient and/or provider-specific factors, etc
Require and specify appropriate comparators: “Best practice” or “real world”
Apply CU metrics (e.g., improvement in “net health outcomes”) transparently and consistently for all healthcare interventions (i.e., diagnostics, therapeutics, physician services, hospital services, etc) so value-based trade-offs designed to optimize both the cost and effectiveness of care are possible
Consider risk-sharing approaches (e.g., coverage with evidence development)
Transparently define and consistently apply criteria to qualify for such approaches

5. Recommendations: Putcha
Coverage
Provide a brief, transparent rationale for coverage decisions, especially for any “deviations” from “standard” requirements
Provide a simple, robust and transparent appeals/reconsideration process
Develop a standardized application for coverage that includes assessment of AV, CV, and CU to facilitate efficiency and consistency in assessments, and to diminish administrative burdens on applicants and evaluators
Develop a realistic pre-submission process to enable test developers and payers to quickly and efficiently discuss the adequacy of a dossier in a “safe harbor” prior to full submission

6. Recommendations: Putcha
Reimbursement
• Price all healthcare interventions (i.e., diagnostics, therapeutics, physician services, hospital services, etc) transparently using similar value-based (or at least market-based) metrics
• Stop “silo-ing” money for different interventions so value-based trade-offs designed to optimize both the cost and effectiveness of care are possible
• If “silo-ing” is unavoidable, at least ensure that pricing within silos is internally consistent, and ideally reflects “value” or “market”, not simply “cost”
• Consider risk-sharing approaches (e.g., “reimbursement with evidence development”)

7. • Reimbursement is determined by the CLFS (PAMA)
Recommendations: Almas
• A test can show excellent analytic validity and excellent clinical validity. The question facing Medicare is “What is the evidence that the use of this test improves the beneficiary’s outcome?”
• The grade of evidence and strength of recommendation will determine coverage.
• Reimbursement is determined by the CLFS (PAMA)