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Many parts of the healthcare system remain fragmented and outpatient surgical care is no exception. In this study, we develop a coordinated pre‐operative scheduling approach between Anesthesiology and Internal Medicine to optimize patients’ medical conditions prior to surgery. Coordinating these two services has conceptual appeal because any health issues discovered by the anesthesiologist can often be addressed by a general internist. We design a patient‐centered approach that allows the patient to see both providers, if necessary, on a single visit. This problem is modeled as a two‐station stochastic network, where each station (or clinic) may be staffed by multiple parallel providers and patients see the first available provider. To solve the scheduling problem, we formulate an optimization model to maximize the net benefit of scheduling patients. The objective balances benefits against patient waiting time and clinic overtime costs. We develop a scheduling method with a booking limit to create a balanced network schedule. Due to the complexity of this problem, the solution approach is myopic. In addition, we develop a hybrid method that combines analytical calculation and simulation‐based optimization. We demonstrate our approach on a healthcare network at The University of Texas Health Sciences Center in San Antonio. We compare our method against other policies and show that it yields high quality and robust results. Based on the level of generality of our model and results, the insights gained are not limited to the particular application, but can be applied to other patient‐centered models where scheduling coordination can be used. [ABSTRACT FROM AUTHOR]

A sequential clinical scheduling method for patients with general service time distributions is developed in this paper. Patients call a medical clinic to request an appointment with their physician. During the call, the scheduler assigns the patient to an available slot in the physician's schedule. This is communicated to the patient before the call terminates and, thus, the schedule is constructed sequentially. In practice, there is very limited opportunity to adjust the schedule once the complete set of patients is known. Scheduled patients might not attend, that is, they might “no-show,” and the service times of those attending are random. A myopic scheduling algorithm with an optimal stopping criteria for this problem assuming exponential service times already exists in the literature. This work relaxes this assumption and develops numerical techniques for general service time distributions. A special case in which service times are gamma distributed is considered and it is shown that computation is significantly reduced. Finally, exhaustive experimental results are provided along with discussions that provide insights into the practical aspects of the scheduling approach. [ABSTRACT FROM AUTHOR]

In this paper a stochastic overbooking model is formulated and an appointment scheduling policy is developed for outpatient clinics. The schedule is constructed for a single service period partitioned into time slots of equal length. A clinic scheduler assigns patients to slots through a sequential patient call-in process where the scheduler must provide each calling patient with an appointment time before the patient's call terminates. Once an appointment is added to the schedule, it cannot be changed. Each calling patient has a no-show probability, and overbooking is used to compensate for patient no-shows. The scheduling objective captures patient waiting time, staff overtime and patient revenue. Conditions under which the objective evolution is unimodal are derived and the behavior of the scheduling policy is investigated under a variety of conditions. Practical observations on the performance of the policy are presented. [ABSTRACT FROM AUTHOR]