Chemotherapy Outpatient Scheduling at the Segal Cancer Center Using Mixed Integer Programming Models
Bibliographic record
Abstract
Appointment scheduling in an outpatient oncology clinic is a daunting task due to the stochastic and dynamic nature of the appointment requests. Each patient has a different trajectory and varying requirements of appointment length time that differ from one another. It is not possible to predict the amount of time required nor the amount of patients that will be treated in a day. Due to the oncologist's prescribed regimen, there is almost no flexibility to choose an appointment date because of the strict resting period required between treatments to achieve the best curative outcome. \n \nThe purpose of this thesis is to demonstrate the benefit of using integer programming to model and to solve some of the challenges faced by the Segal Cancer Center of the Jewish General Hospital in Montreal, Quebec, when designing appointment schedules. We study two scheduling problems. \n \nThe chemotherapy outpatient scheduling problem determines the allocation of patient appointment to days and the determination of appointment start time on those days for a planning horizon of four weeks. The objectives are to maximize the adherence to protocol, maximize the proper assignment of primary nurses to patients and minimize the completion date of treatments. With this model, the clinic can schedule appointment requests as they arise. \n \nWhen taking an integrated approach to solve the oncology clinic multi-stage scheduling problem, it is possible to coordinate the clinic's departments and determine the start time of each activity required by patients no matter their trajectory through the system. Due to the minimization of patient wait time and the completion time of their visit, there will be a better coordination within the clinic, reduction of staff idle time and a balance of resource utilization. Most importantly, it will ensure the completion of tasks within a single day, eliminating the current two-days scheduling policy of the Segal Cancer Center. \n \nThe findings of this thesis will facilitate decision making in healthcare scheduling, improve the service level of oncology clinics and serve as a workforce management tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".