The Sulston Project Home PROJECT About Project Policy Delphi TOPICS Inclusion & Equity Payer Patent Stories Research Case Studies Publications Related Projects Team contact PAYER POLICY Context Genetic testing for inherited genes has come a long way and has saved many lives. We no longer are searching for only the BRCA1 and BRCA2 genes that predict breast and ovarian cancer risk; instead, we have dozens of inherited genes to work with that are associated with an increased risk of cancer. A technician at the Cancer Genomics Research Laboratory working on DNA Genotyping and Sequencing. Problem Genetic testing plays a crucial role in the diagnosis, prevention, and treatment of many cancers, but its power as a clinical tool is blunted by poor data sharing practices and a lack of standardization in how data are stored. What do we mean by this? From a research standpoint, we need an enormous amount of data from diverse populations to improve interpretation of genetic tests. We can’t do this if we do not collect data—labs are throwing away genetic testing data—and if they keep it, they aren’t sharing it. Genetic tests are not standardized either (different labs test different genes). From a patient standpoint, we need improved data sharing to improve diagnosis and treatment. This can happen if we have a more robust database. To make personalized medicine a reality, we must share data, standardize tests, and increase the race, gender, and ethnic diversity of the data pool. Without sufficient data, many DNA variants cannot be interpreted confidently, making it difficult to determine if a variant is harmless or cause for concern. If a benign variant is interpreted as high risk, it can lead to needless surgery (e.g., mastectomy, removal of ovaries and Fallopian tubes; removal of colon or colon and rectum). Conversely, if a high-risk variant is missed, undetected cancer can become metastatic before it is discovered, with dire consequences. Variants of uncertain significance (VUS) are a common finding in genetic testing. VUS’s cause patient anxiety, can lead to unnecessary surgical interventions, and increase health care costs by failing to prevent cancer or detect it early. Data collected by laboratories have the power to broaden our understanding of the genes associated with different cancers, but the data exist in silos that hinder interpretation. Each lab has its own testing and reporting criteria, making analysis even more challenging. Ask There is already great interest in creating data sharing incentives among some payers. We ask that you consider any of the policy options we are suggesting. An executive order could be applied to Medicare or other federally funded health programs, and private payers might follow suit. The House Ways and Means and Senate Finance Committees could create payer incentives under Medicare, the largest single payer. Solution Payers can make the system better, and help it improve over time. Medicare and other federal payers should leverage their power to get laboratories to share data. Here’s how payers can create incentives to share data: Preferred Networks and Standard Quality Practices Establish preferred networks of laboratories that adhere to standard quality practices, including the sharing of data with public databases. By building networks of labs committed to data sharing, payers can ensure that patients receive high-quality genetic testing services. The resulting data contribute to the knowledge commons, so the system improves over time. Data Sharing as a Condition of Payment By tying reimbursement for genetic testing to data sharing, payers create a strong financial incentive for laboratories to contribute information to the collective knowledge base. Combined Approach Implementing both preferred networks for data sharing and tying reimbursement to data sharing fosters a collaborative environment in which laboratories actively participate in sharing data and promote high-quality genetic testing practices, while rewarding labs that contribute most to improving the system over time.