Applying discrete-event simulation to Orthopaedic Clinics: Case studies and perspectives
Bibliographic record
Abstract
Discrete-event simulation is applied to three Orthopaedic Clinics across Ontario to find solutions to long patient wait times. The largest driver of patient wait time was found to be excessive overbooking. Improved patient scheduling rules, such as balancing the patient load across the entire clinic, decreased patient wait time. Using the simulation, the new scheduling algorithm decreased patient cycle time average and standard deviation by 40 minutes or 35%, and 12 minutes or 52%, respectively. Additional recommendations include balancing x-ray patients throughout the day, determining a maximum number of patients that can be seen in a clinic, implementing staggered shifts for staff, scheduling "add-on" patients towards the end of the day and referring non-operative on-call patients to the surgeon with the least patients. A study of hard-coding a minimum schedule and utilizing priority lists for patients beyond the minimum schedule has been initiated from this study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".