Produced by the Deloitte Center for Health Solutions, 2009
Introduction
Personalized medicine has the power to transform health care within the foreseeable future, from a population-based model to a subpopulation and individual model. Scientific advances in biotechnology, health information technology (HIT), genomics, proteomics, therapeutic delivery, and computational biology have made possible a new approach to the diagnosis, treatment, and management of disease and disease risk. Personalized medicine is the application of these advances to the population at large, with distinction possible on an individual molecular level. As such, personalized medicine is a disruptive technology: It increases the functionality of disease detection, diagnosis and treatment beyond the current knowledge of most health care consumers and many providers, and fundamentally changes the business model for key health care business stakeholders.1
Because genetic analyses enable the design of therapies targeted to specific individuals who are most likely to benefit from the treatments, there is downstream benefit not only for those individuals, but for those paying for and prescribing the more efficacious treatment. Standard practices in discovering, approving, and administering drug therapies are based on entire disease populations, rather than on subgroups within those populations. This fundamental difference between current and potential practice presents challenges to the current health care business model, and varying economic and clinical benefits and barriers for stakeholders. It is a topic of national importance, as evidenced by a recent report, Priorities for Personalized Medicine, prepared by the President’s Council of Advisors on Science and Technology. The Council emphasizes the potential of personalized medicine to not only improve disease prevention and patient care, but also to affect two economic trends: the decreasing rate of medical product development and increasing health care costs.2 To better understand some of the economic factors of personalized medicine, this project explored the following questions:
- Does personalized medicine have a quantifiable return on investment (ROI)? Are clinical case study data available that could be used to develop a framework for determining ROI? If a positive ROI is demonstrated, to whom do the benefits accrue and in what timeframe?
- Can an economic framework be derived from case studies that will demonstrate differences in ROI across industry stakeholders? If so, can these ROI differences clarify the incentives/barriers for stakeholders to invest in or adopt personalized medicine? If there are significant gaps in value experienced among stakeholders, are there strategies that might bridge these gaps and accelerate adoption?
