A FRAMEWORK FOR PRIORITIZING RESEARCH INVESTMENTS IN PRECISION MEDICINE

Purpose: The adoption of precision medicine (PM) has been limited in practice to date, and yet its promise has attracted research investments. Developing foundational economic approaches for directing proper use of PM and stimulating growth in this area from multiple perspectives is thus quite timely.

Method: Building on our previously developed Expected Value of Individualized Care (EVIC) framework, we conceptualize new decision-relevant metrics to better understand and forecast the expected value of PM. Several aspects of behavior at the patient, physician and the payer level are considered that can inform the rate and manner in which PM innovations diffuse throughout the relevant population.  We illustrate this framework and the methods using a retrospective evaluation of the use of OncotypeDx genomic test among breast cancer patients.

Result: The enriched metrics can help inform many facets of PM decision making, such as evaluating alternative reimbursement levels for PM tests, implementation and education programs for physicians and patients, and decisions around research investments by manufacturers and public entities. We replicated prior published results on evaluation of OncotypeDx among breast cancer patients, but also illustrated that those results are based on assumptions that are often not met in practice. Instead, we show how incorporating more practical aspects of behavior around PM could lead to drastically different estimates of value. For OncotypeDx, population returns to a social insurer ranged from $17Billions to $37Billion and from $4Billion to $10Billion in revenues for the manufacturer depending on the nature of reimbursement policies and diffusion patterns.

Conclusion: We believe that the framework and the methods presented can provide decision makers with more decision-relevant tool to explore the value of PM. There is a growing recognition that data on adoption is important to decision makers. More research is needed to develop prediction models for potential diffusion of PM technologies.