This article focuses on balancing supply and demand for physicians and panel patients on a tactical level to ensure a manageable workload for the physician and access to care for patients. Patients are part of the physician’s panel if they visit the physician somewhat regularly. For the first time, we propose deterministic integer linear programs that decide on the intake of new patients into panels over time, taking into account the future panel development. The main objective is to minimize the deviation between the expected panel workload and the physician’s capacity over time. We classify panel patients with respect to age and the number of visits in a period and assume a transition probability from one visit category to another from one period to the next. We can include stationary patient attributes and consider several physicians together. The programs work with aggregation levels for the new patients’ demand concerning the patient attributes. We conduct experiments with parameters based on real-world data. We consider the transition between visit categories and the new patients’ demand to be stochastic in a discrete-event simulation. We define upper bounds on the number of patients in a patient class to be accepted in a period through solving the programs several times with different demand inputs. Even in this uncertain environment, we can significantly reduce the expected differences between workload and capacity over time, taking into account several future periods instead of one. Using a detailed classification of new patients decreases the expected differences further
