# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Operating Room Scheduling
#### 4.3.2 Papers
After being scheduled, a patient will go into a queue and wait to enter the OR before being routed through downstream units. For an optimization model they utilize the one by [PERSON] et al. [38]. When creating the model, three main situations needed to be handled: cancellations, emergency patient handling, and the warm-up period. To evaluate the DES, the following metrics were used: throughput, OR utilization, overtime, cancellations, and cost. Patient data was collected from January to June 2013. Evaluating the model and DES on TWH, the OR days needed for elective patients were decreased by 16.6%. The cost of this was slightly lower throughput in both emergency and elective patients. Using the new models, OR utilization was also increased by 49.9% to 63%. The model even managed to bring a 5.4% reduction in cost, and a 1.9% reduction in per surgery cost. Many other metrics were also considered. To study the effects each parameter has on the output an ablation style study was conducted. By ignoring the variable length of surgeries cancellations rose 2% and OR utilization dropped by 72.2%. Forgoing dynamic patient arrivals affected the aforementioned statistics by 5% and 67.1% respectively while limiting downstream resources had the effect of 23% and 50.6% respectively. Many papers focus on optimizing aspects of schedule making rather than the schedule itself. Instead of building a model, [PERSON] and [PERSON] (2018) as well as [PERSON] et al. (2019) try to optimize the estimations needed to make the models work. One aspect of schedule creation when booking appointments is the length of the appointment. There are not always good ways to determine how long a patient will need to be seen. If you overbook you will have long wait times, and if you underbook you will have lots of downtime that could have been used to treat patients. [PERSON] and [PERSON] (2018) address this in their paper \"Can machine learning optimize the efficiency of the operating room in the era of COVID-19\". This problem has been especially exasperated in the last year, as many elective surgeries have been cancelled due to the emergence of the novel coronavirus. Due to this, the number of people who can get through is more impactful. They compare a machine learning model which predicted the length of scheduling times compared to using the mean scheduling time and learned that in doing so the operating room would have been in overtime 27% of the time compared to 48% of the time otherwise. Additionally, the underline reduces from 37% to 18%. These data were collected from the Oakville Trafalgar Memorial Hospital in Ontario. [PERSON] et al. (2019) used discrete event simulation (DES) and data from the Toronto General Hospital to see if surgical cancellations could be reduced. The longest cases should be scheduled first, as this limits the amount of overtime and prevents rescheduling or cancellations. The main problem is that many appointments are random, and cannot be known upfront. To address this, the model first creates a schedule and assigns timeslots based on factors such as which OR is available. The simulation then takes data from the historical records and simulates whether the patients can go through with their surgeries. They then studied three different scenarios to see how this would affect the frequency at which surgeries were cancelled. The first is to create schedules based on the length of stay of their patients. Surgeons who usually had a shorter length of stay were scheduled earlier in the week. The second scenario is scheduling surgical procedures based on surgery time and surgical time variation. The last scenario was simply adding more beds to the OR. Creating schedules based on increasing length of stay consistently decreased surgery cancellations. Adding beds had the same effect. Additionally, ordering schedules from shortest to longest and from lowest variance to highest variance greatly outperformed the contrary. Much like the papers by [PERSON] et al. (2018) and [PERSON], [PERSON], and [PERSON] (2018), [PERSON] et al.