Abstract: We propose a computationally efficient methodology to determine the probability of pre- and post-medication SBP to be below the recommended treatment threshold for an individual after one or more medications intake from a single SBP measurement. We build upon a prior probabilistic framework that model individuals’ true SBP as a latent variable and makes predictions of pre- and post-medication SBP via Monte Carlo (MC) sampling. We extend this approach by introducing a computationally efficient formulation based on numerical recursive convolutions which also takes into account prior history of measured SBP across multiple visits via a Bayesian update mechanism. Our methodology produces results indistinguishable with state-of-art MC simulations irrespective of the experimental setting (i.e. measured SBP, standard deviation of the drug and measurement) while being less resource intensive. Moreover, results showed that the inclusion of historical data in the analysis leads to more accurate prediction of future true SBP.