RAND Roybal Center for Health Policy Simulation
The Economic Impact of Pharmacogenetics
Background
Personalized medicine, or pharmacogenomics, promises the ability to tailor drug therapies to individuals based on prior knowledge of their biological traits. This area of biomedical research is of particular interest in the case of aging individuals that are likely to have multiple prescriptions with individually variant side-effects and efficacies. From an economic perspective, the effect of implementing pharmacogenomics in general will be to change pharmaceuticals from “experience” goods, whose value is known to the consumer only after a period of consumption, to “inspection” goods, whose value is known to the consumer prior to consumption. By diminishing the need to try a drug in order to determine its effectiveness, pharmacogenomics would drastically reduce or even eliminate costs associated with ineffective or even harmful therapies. This study considers how changes in current clinical practice to include extant pharmacogenetics knowledge could impact drug expenditures. It is the first of three phases addressing the potential impacts of pharmacogenetics, the next two intended to develop predictive models of the effects of pharmacogenomic R&D strategies and the combined long-term effects of changes in practice and development on innovation and costs.
Several studies, particularly cost effectiveness analyses, have attempted to evaluate the impact of pharmacogenomics on currently existing therapies. These studies have not explicitly considered the time it takes patients to learn that a therapy will be unsuccessful in their analyses. The economic impact of a screen is dependent on expenditures made on an ineffective treatment during that learning time. Even if a given therapy is ineffective for the vast majority of patients, the benefits of pre-screening will be limited if patients can discover the drug’s efficacy quickly and with little cost. We propose an analysis characterizing the conditions under which screens for the most frequently prescribed drugs might limit expenditures.
Specific Aims
- Characterize learning times for each drug, controlling for factors such as changes in patient costs and concurrent prescriptions.
- Identify the learning time expenditures for each of 300 most frequently prescribed drugs (those intended for sustained use).
- Estimate the impact of widespread application of existing pharmacogenomic screens given learning time expenditures on the screenable drug.
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