Scheduling rules to achieve lead-time targets in outpatient appointment systems.

This paper considers how to schedule appointments for outpatients, for a clinic that is subject to appointment lead-time targets for both new and returning patients. We develop heuristic rules, which are the exact and relaxed appointment scheduling rules, to schedule each new patient appointment (only) in light of uncertainty about future arrivals. The scheduling rules entail two decisions. First, the rules need to determine whether or not a patient's request can be accepted; then, if the request is not rejected, the rules prescribe how to assign the patient to an available slot. The intent of the scheduling rules is to maximize the utilization of the planned resource (i.e., the physician staff), or equivalently to maximize the number of patients that are admitted, while maintaining the service targets on the median, the 95th percentile, and the maximum appointment lead-times. We test the proposed scheduling rules with numerical experiments using real data from the chosen clinic of Tan Tock Seng hospital in Singapore. The results show the efficiency and the efficacy of the scheduling rules, in terms of the service-target satisfaction and the resource utilization. From the sensitivity analysis, we find that the performance of the proposed scheduling rules is fairly robust to the specification of the established lead-time targets.

A network flow approach for tactical resource planning in outpatient clinics.

This paper introduces a deterministic model to plan the physician requirements for outpatient clinics to achieve service targets for the appointment lead-times of patients. The Ministry of Health of Singapore has established targets for the median, 95th percentile, and 100th percentile of appointment lead-times for patients, since long appointment postponements are regarded as being unacceptable for health care services. The study is to match the capacity of the healthcare providers to the patient demand for a re-entry system, subject to restrictions on the appointment lead-times for patients. We propose a mixed-integer programming model for planning capacity with the minimization of the maximum required capacity as its objective. In the model we assume a finite planning horizon, deterministic arrivals, multiple types of patients, identical physicians, and dependent demand between types of patients. We solve this model with a Branch and Cut algorithm. We test the model with numerical experiments using real data from the chosen specialty at the outpatient clinic of the studied hospital. The results show the value of the proposed model via a systematic push-pull mechanism in scheduling patients' requests to minimize the objective. The clinic should use one of the appointment lead-time targets to determine the patients' appointment dates. Finally, from the sensitivity analyses we demonstrate that the objective is negatively correlated with first-visit patients' appointment lead-time targets, the discharge rates, and the re-visit patients' mean appointment lead-time; we find a positive correlation between the first-visit patients' mean appointment lead-time and the appointment lead-time targets.