Q: Adoption and insurer coverage for precision oncology may require evidence that it can improve patient outcomes. The current situation is confusing for many. What are some practical next steps that workers in the field can take to improve clinical evidence and consistency of payment?
A: It’s been said that the greatest challenges facing genomic medicine are not scientific, but economic. Much has been written about the need for improved clinical evidence and consistent reimbursement policies, but there have been relatively few studies that illuminate what steps can be taken to address them. Two new studies from the University of California San Francisco Center for Translational and Policy Research on Personalized Medicine (TRANSPERS) address these questions.
A newly published study in Genetics in Medicine combines insights from TRANSPERS collaborators and leading genomic medicine experts to identify evidence gaps in genomic medicine that comparative effectiveness research can address, with direct relevance to precision oncology. For genomic/precision medicine to fulfill its potential, it must be (1) evidence-based and (2) consider a full range of patient outcomes. Does the literature to date suggest that these objectives have been met? TRANSPERS and experts from multiple institutions addressed this question. This is the first study that uses a systematic structured literature review (combined with expert input) to provide an overarching assessment of comparative effectiveness research for genomic medicine. We found that all included reviews (N=21) identified potentially important clinical applications of the genomic medicine interventions. Most had significant methodological weaknesses and there were few studies of conditions other than cancer. There were only a few analyses examining a broad range of patient-centered outcomes. Our findings provide next steps about where to focus future research activities and policy initiatives by identifying conditions, tests, and interventions where comparative effectiveness questions may be appropriate for study. We also discuss the limitations of prior research and how they could be addressed.
Specifically for precision oncology, we found that studies are needed to measure whether tumor sequencing tests lead to better clinical outcomes than alternative prognostic methods in different stages of common cancers. Additionally, studies are needed for cancer risk assessment panels that examine the consequences of testing for individuals and families including acceptability to patients, adherence to screening, delivery of genomic testing, and models to estimate the incremental net benefit of testing and optimal testing intervals.
Another recent publication from TRANSPERS identifies opportunities to resolve reimbursement challenges of genetic panel tests for cancer risk assessment. This study, published in the Journal of the National Comprehensive Cancer Network used data from payers themselves to address not only reimbursement challenges but also opportunities for resolving those challenges. Hereditary cancer panels – testing for multiple genes and syndromes – are rapidly transforming cancer risk assessment but are controversial and lack formal insurance coverage. Our study of private payers found a number of barriers to coverage for hereditary cancer panels including poor fit with coverage frameworks, insufficient evidence, and departure from pedigree/family history-based testing toward population-based genetic screening. Opportunities for addressing these challenges includes refining target populations, developing evidence of actionability and pathogenicity/penetrance, and creating infrastructure and standards for informing and re-contacting patients. We also need to separate research from clinical use in the hybrid clinical research setting and adjust coverage frameworks. Our findings have particular relevance to the NIH’s Precision Medicine Initiative, which will assemble and study an unprecedented cohort of one million or more volunteers who will contribute genomic, clinical and lifestyle data to accelerate genetic science.
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