Genomic research engages high public interest, in large part because it is seen as the key to dramatic advances in disease prevention. However, multiple elements contribute to the successful translation of genetic advances into improved health outcomes. Determining the net medical and societal effects of the use of genetic information in health care requires attention to each of these elements, which include: (a) the predictive value of genetic information in different clinical and public health settings; (b) the costs, efficacy and risks of interventions to improve health based on genetic information, and (c) the ethical, legal and social implications of procedures to identify people with genetic susceptibilities. The proposed project will address these issues and these questions through analysis of a series of genetic testing case examples.
The specific aims are: 1. To identify a series of case examples that demonstrate a spectrum of plausible uses of genetic information to improve health outcomes. 2. To convene an expert Working Group that will work collaboratively with the Investigator Team to develop a model process that incorporates provisional clinical practice guidelines for the case examples, an inventory of ethical, legal and social issues raised for each case example, decision models based on the provisional guideline and ELSI inventory to assess the implications of uncertainties in key parameters of the testing process, compares the use of genetic information to accepted, non-genetic intervention in terms of medical, economic and social costs and benefits for each case example, and identifies research an educational priorities related to each case example. 3. To identify the characteristics that define genetic tests that are likely to improve health outcomes. 4. To compare the results of the different case analyses to determine the extent which genetic testing requires special measures for informed consent and protection of privacy and patient autonomy. 5. To conduct an evaluation of the model process within a large health care system.
