Optimizing Personalized Care Using Economic Studies of Genomic Testing

Personalized medicine uses diagnostic tests to improve patient outcomes and reduce harms of therapy. This project’s objective is to develop methods for optimizing the use of such tests using a multi-criteria decision analysis framework. Using breast cancer as the focal case, we will: Phase 1: Extend a multi-cohort population simulation model to compare several scenarios for the use of genomic testing for breast cancer recurrence risk; Phase 2: a) Use computerized data to analyze use of genomic testing in two large populations: Kaiser Permanente Northern California, and patients in Washington State’s SEER; b) Draw a sample of oncologists and patients from these populations, and conduct structured surveys to measure perceptions and propensity to use genomic tests; Phase 3: Integrate data from phase 2 into the simulation model to compare: a) genomic testing based on clinical trial and guideline-recommended care vs. b) actual patterns of testing from the community populations.

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