Mixed Appointment Scheduling: Managing Routine and Same-Day Appointments for Outpatient Clinics
Bibliographic record
Abstract
This article introduces a novel scheduling approach for managing a mix of routine and same-day appointments in an outpatient clinic. While the well-known carve-out scheduling (COS) policy reserves the provider capacity for same-day patients, its use of dedicated slots for each patient class may increase the risk of system underutilization or overload and long patient wait times. We propose the mixed appointment scheduling (MAS) policy, which allows routine and same-day patients to share common appointment slots, and investigate its performance relative to COS. By formulating a new model to determine the optimal scheduling decisions under the MAS policy, we demonstrate that the problem’s objective function (the total daily cost) is no longer multimodular in the presence of priority service discipline and demand uncertainty. We develop efficient methods to identify optimal scheduling policies and show that MAS reduces system costs, improves utilization, and decreases delays, particularly when same-day demand is moderate relative to clinic capacity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".