Simulation optimisation for mixing scheduled and walk-in patients
Why this work is in the frame
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Bibliographic record
Abstract
Mixed registration type clinics accept both walk-in and scheduled patients. Such clinics provide patients with an additional option for how they access care while patient bookings help providers ensure a full workday. The model described in this paper determines how many patients to schedule (and when) in mixed registration type clinics. The methodology, simulation optimisation allows stochastic features found in such clinic to be modelled and provides optimisation techniques to identify solutions. A general simulation optimisation formulation for mixed registration type clinics is presented. Furthermore, the methodology is applied to a case study of a collaborative emergency centre in Nova Scotia, Canada. We demonstrate how the model can be used in clinics with multiple providers and multiple objectives. We compare the simulation optimisation generated schedule with existing schedules and show the advantages the collaborative emergency centre can expect when using schedules developed with the presented methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it