Traditional surveillance of adverse infant outcomes following maternal medication exposures relies on pregnancy exposure registries, which are often underpowered. We characterize the statistical power of TreeScan™, a data mining tool, to identify potential signals in the setting of perinatal medication exposures and infant outcomes. We used empirical data to inform background incidence of major congenital malformations and other birth conditions. Statistical power was calculated using two probability models compatible with TreeScan, Bernoulli, and Poisson, while varying the sample size, magnitude of the risk increase, and incidence of a specified outcome. We also simulated larger exposure to referent matching ratios when using the Bernoulli model in the setting of fixed N:1 propensity score matching. Finally, we assessed the impact of outcome misclassification on power.