We discuss a decision-theoretic approach to building a panel-based, pre-emptive genotyping program. Pre-emptive genotyping permits seamless genotype guided therapy when such medications are prescribed, and panel-based testing allows providers to reuse previously collected genetic data when a new indication arises. Because it is cost-prohibitive to conduct panel-based genotyping on all patients, we describe a three-step approach identifies patients with the highest expected benefit. This approach first seeks to estimate risk of being put on medications with pharmacogenetic effects by constructing risk prediction models using readily available clinical data. It then uses literature-based estimates of adverse event rates, variant allele frequencies, secular death rates, and costs to construct a discrete event simulation that estimates the expected benefit of having an individuals’ genetic data in the electronic health record after an indication has occurred. Finally, it combines medication prescription risk with expected benefit of genotyping once a medication is indicated to calculate the expected benefit of pre-emptive genotyping.