Data-Driven Two-stage Appointment Radiotherapy Scheduling Model for Resource Optimization at a Tertiary Cancer Center
Bibliographic record
Abstract
Abstract Background: The timely delivery of radiotherapy (RT) is crucial to cancer care, and excessive delays in RT have been associated with detrimental oncological and psychological outcomes. Prior to receiving RT on treatment units (Linear accelerators), there are a few processes that need to take place including simulation (on CT simulators), radiotherapy plan generation/optimization and quality assurance. The assignment of patient schedules on CT simulators and Linear accelerators is currently done manually at most cancer centers. We propose that data-driven optimization of patient scheduling has the potential to improve wait-times, and optimize use of departmental resources. Methods: A two-stage Mixed Integer Programming model was developed to optimize the patient appointment scheduling process and to forecast machine utilization. The model was tested with historical institutional data from Princess Margaret Cancer Center. By analyzing the dataset and simulating historical patient arrivals, the model output is evaluated by comparing patient wait time statistics and monthly machine utilization against what occurred during this time frame. Results: Testing our model on data from 2019-06 to 2020-02, we found a reduction in average wait time from 11.2 to 6.7 business days for standard category patients. The number of standard patients exceeding the wait time target of 10 business days were reduced from 118 to 15 patients each month. In addition, our model could accurately estimate future machine utilization for both CT simulators and linear accelerators based on the model output appointments, which could facilitate better planning and utilization of departmental resources. Conclusion: Our scheduling model has the potential to reduce the standard patient wait time for radiation treatment without compromising the wait time for urgent patients. The model can be also used to forecast department resources and machine utilization based on the output of the scheduling model. Radiotherapy departments could use this model to generate patient appointment schedules as well as to reduce machine idle time or appointment over-booking.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".