PHP171 - Drivers of Genomic Precision Medicine Adoption in the US: Payer Preferences

Abstract

The current literature on the drivers of genomic precision medicine adoption is limited. The objective of this study was to estimate healthcare payers’ preferences for genomic precision medicine tests. Using qualitative research methods (n=6 payers), we identified six key attributes for payers’ uptake of genetic tests: type of information the test provides (screening vs. treatment prediction), probability that the member has an informative genetic marker, experts’ agreement on changing medical care based on the marker, members’ quality-of-life gains, life expectancy gains with statistical uncertainty, and cost to the plan. We designed a stated preference discrete choice experiment using these attributes and administered a web-survey on a representative sample of US healthcare payers. We used effects coding and analyzed the data using a nested logit modeling approach. The survey response rate was 58% (150 participants completed the survey). Approximately 53% respondents had prior experience evaluating genetic tests for reimbursements and 85% had greater than 5 years of healthcare decision-making experience. Based on the payers’ preferences, the relative importance of the attributes was in the following order of decreasing importance: quality-of-life, gains in life expectancy, expert agreement in recommendations, probability that the member has an informative genetic marker, cost to the plan, and screening vs treatment prediction. Payers’ marginal willingness to pay (mWTP) was highest for genetic tests that lead to improvement in quality-of-life ($544 to $5856) and lowest for tests that resolved uncertainty in life expectancy gains ($656 to $1679). The mWTP for an increase in the marker probability ranged from $434 to $2846. Payers exhibited a strong preference for genetic tests that improved quality of life and life expectancy. The results of this study can be used to predict genetic test adoption, guide future investment in and design of genetic tests, and inform pricing and reimbursement decisions.