In current clinical practice, priority‐specific wait time targets are usually determined based on the consensus of medical specialists and health care administrators. The rationale behind this approach considers clinical urgency but it does not consider the efficient use of clinical resources and the patient volume associated with each priority class. The approach we present here aims to determine wait time targets in a systematic fashion that both respects clinically acceptable maximum recommended wait times and considers clinic size and demand distribution across patient classes. First, we discuss the performance of several advance patient scheduling policies in the literature in terms of average wait times and overtime and select one for illustrative purposes. Second, we simulate the chosen policy given a demand distribution and a fixed system capacity and approximate (using regression and neural networks) the average wait time for each priority class and the use of overtime as a function of potential wait time targets. Finally, using a parameterized cost function, we formulate forward and inverse mathematical problems to determine when the implicit unit wait time costs drop to zero as wait time target values increase. Using illustrative examples with two patient classes, and a practical application with four patient classes, we demonstrate the potential managerial benefits of the proposed approach in terms of improved clinic efficiency and reduced wait times. This approach ensures that wait times are set to the minimum value that still achieves the maximal resource efficiency ensuring that patients wait for service is not extended unnecessarily.