Integrated resource scheduling for patient appointment series — Designing tactical blueprints for an orthopedic clinic

Managing the time that patients wait for their next appointment is a well-known challenge in hospitals. Scarce resources must be efficiently planned and coordinated to create schedules that allow departments of a hospital to keep these waiting times below set targets. The problem studied in this work is based on the situation in the Orthopedic Department at St.Olav’s hospital in Norway. Here, patients undergo an initial assessment by surgeons during a consultation appointment in the outpatient clinic (OC), and if they are diagnosed with the need for surgeries, times in operating rooms (OR) for the surgeries are assigned. After surgery, patients visit the surgeons at the OC for follow-up consultations. To perform these activities, in addition to surgeons, patients need resources, including OC rooms, ORs, and beds in wards. We have developed a new model for the Integrated Multi-Appointment Blueprint Scheduling Problem reflecting the planning problem at the department. The model combines a detailed representation of the queues of patients waiting for consultations and surgeries with integrated scheduling of the OC and ORs. The model creates tactical blueprints that assign activities to surgeons and rooms over a planning horizon. The model manages the waiting times of patients and targets set on individual queues while minimizing the total number of patients in all queues at the end of the planning horizon. To evaluate the proposed optimization model on the OC and OR performance, we create four cases of different size. Additionally, we study two aspects of cyclicity, cyclic or non-cyclic availability of surgeons and cyclic or non-cyclic room schedules. Finally, we compare the solutions of the proposed model with another model in a simulation experiment. The computational results show that our model improves the blueprints in terms of patient waiting time and waiting lists and is capable of handling the trade-off between waiting time targets and end queues more efficiently.