# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Outpatient Scheduling
It swaps between any two days, but only within the first two weeks. The model was designed to be used in the hematology-oncology clinic in the Regional Hospital at Trois-Rivieres. Given the model, they were able to schedule all patients within three weeks as opposed to the four that had been done manually. Additionally, it produced a more balanced schedule, better dividing treatments within the week. Another appointment scheduling paper comes from [PERSON] et al. [48]. They address patients who need oncology appointments. Similar to [PERSON] et al. [37], [PERSON] et al. [48] build a discrete-event simulation framework to help study the impact of their optimization model. The actual optimization model is a mixed integer linear programming model. They then evaluated their model with data collected directly from the British Columbia Cancer Agency regional centre. The process first begins with deriving data from the simulation. The simulation first considers tumor location and wait time tolerance. This determines how urgent a request is. Based on this information the request is then added to the schedule, diverted, or added as an add-on. It was found that first come first serve was the most impactful rule in place. No specialization rules could outperform this rule to a significant degree. The general conclusion in this paper is that the impact of scheduling software is most noticeable when capacity is close to the average level. Furthermore, [PERSON], [PERSON], and [PERSON] [50] look into scheduling chemotherapy appointments with daily outpatient-nurse scheduling. This problem was presented by the Centre Hospitalier Universitaire de Montreal. A two-stage decision support system procedure is proposed. The first stage is further divided into two substages. In the first substage an appointment scheduling problem is solved. This assigns a timeslot to each outpatient. The second substage solves a staff planning problem to determine which staff is available for each patient. Once these have been solved stage 2 solves the daily patient-nurse assignment problem. This step is a dynamic step that allows constraints such as last minute changes or cancellations. The first stage has both substages modelled as a mixed integer linear programming model. The second stage utilizes a simulation model to predict cancellations and absences for the online model. The online model is a combination of an MIP model updated with the simulation data. The final insights reveal that modeling the waitlist and using optimization was able to improve productivity as well as improve throughput. They also discovered that overbooking the hospital to some degree would be beneficial as it offsets cancellations. Following in the same vein, [PERSON] et al. [47] tackle chemotherapy outpatient scheduling with a case study based on the Odette Cancer Centre in Toronto. To solve this dynamic problem they break it into two steps. The first step is to solve the offline version of the problem using constraint programming. The second is to predict appointments and their features using statistical analysis. This allows the author's model to incorporate features such as extended stays and cancellations. Testing their model on 30 instances representative of real world events, their model was able to improve on the current scheduling method used in practice of 15%, but was not able to reach optimal solutions in most cases. A final look into cancer outpatient scheduling comes from [PERSON] et al. [51] in their paper \"Improving Patient-Care Services at an Oncology Clinic Using a Flexible and Adaptive Scheduling Procedure\". Like similar papers prior, the authors propose a two stage model. The first stage is an online model which accepts and schedules incoming appointments. This model is a multi objective model which attempts to minimize the number of appointments in the waiting list, the number of appointments which are not in the preferred slot, the number of buffered requests, and nurse overtime.