Modeling and optimization of patient flow at the diagnostic imaging department of Trillium Health Centre
Bibliographic record
Abstract
Given that healthcare systems are quite complex with limited resources, it is necessary to search for ways that improve both patient satisfaction and overall efficiency. This thesis presents a study conducted at Trillium Health Centre, Mississauga, where the patient flow at the diagnostic imaging department was modeled and optimized. Trillium's imaging facility provides services to four main types of patients: emergency patients, fracture clinic patients, inpatients, and outpatients. Since outpatients have the lowest priority, they frequently wait for long periods of time to be serviced. The thesis covers the following: (1) Patient flow modeling, whereby discrete event simulation is used to model and validate the department's operations; (2) Model analysis, where patient waiting times and the utilization of staff and physical capacity are examined; (3) Potential recommendations, where sensitivity analysis is used to reduce patient waiting times and optimize the overall performance of the department.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".