How Much is the Value of Genomic Test Information? Evidence from Post-Cardiac-Stent Care Decisions

Personalized medicine aided by new genomic and molecular diagnostic technologies can improve the quality of care. However, these new technologies are often expensive and the value they offer to any individual patient are uncertain. We propose a data-driven approach to identify patients who would benefit the most from these new diagnostic technologies in the specific context of cardiac stent surgery. Our approach is based on a structural econometric model of how physicians choose medication by balancing various risk factors. We estimate the proposed model from a dataset containing patient attributes, physicians’ choice of medications, and test results of 427 stent surgery patients, 175 of whom received a new genomic test. The estimated model reveals that patient’s medical and socioeconomic conditions play a significant role in the physician’s choice of medication after the surgery. Through out-of-sample prediction on a set of matched parallel patient groups — one receiving genomic testing and the other not — we show that our proposed model can be used to estimate the value a patient will receive from such testing based on patient attributes more accurately than existing popular statistical models. Policy simulations show that identifying and testing of the patients likely to benefit the most can reduce the patient disutility by 7.6% and provide benefit from the test sooner to those who need it the most. Finally, we show that the model can be used to design a revenue-neutral subsidy for those at low socioeconomic status to further reduce the population disutility.