Surgical activity has a substantial impact in all areas of hospitals. Additionally, social concerns arise related to equity and speed of access. Therefore, operating room management is paramount in modern society. This work studies the master surgery scheduling problem which is the problem of assigning surgical specialties to operating room blocks, which represent a shift of an operating room. For a master surgery schedule to be applicable in practice, multiple considerations must be taken into account. The particular focus of this work is in the integration of downstream units (i.e., wards or the ICU). Although, in a tactical planning scenario, operational bed requirements are unknown, these may be estimated based on historical data. We propose a novel stochastic programming model that captures the uncertainty in the bed requirements, with a recourse function that penalizes the overutilization of beds. A solution approach based on Benders decomposition is developed and results for generated instances mimicking real-life data are presented.