Setting wait time targets in a multi‐priority patient setting
Bibliographic record
Abstract
In current clinical practice, priority‐specific wait time targets are usually determined based on the consensus of medical specialists and health care administrators. The rationale behind this approach considers clinical urgency but it does not consider the efficient use of clinical resources and the patient volume associated with each priority class. The approach we present here aims to determine wait time targets in a systematic fashion that both respects clinically acceptable maximum recommended wait times and considers clinic size and demand distribution across patient classes. First, we discuss the performance of several advance patient scheduling policies in the literature in terms of average wait times and overtime and select one for illustrative purposes. Second, we simulate the chosen policy given a demand distribution and a fixed system capacity and approximate (using regression and neural networks) the average wait time for each priority class and the use of overtime as a function of potential wait time targets. Finally, using a parameterized cost function, we formulate forward and inverse mathematical problems to determine when the implicit unit wait time costs drop to zero as wait time target values increase. Using illustrative examples with two patient classes, and a practical application with four patient classes, we demonstrate the potential managerial benefits of the proposed approach in terms of improved clinic efficiency and reduced wait times. This approach ensures that wait times are set to the minimum value that still achieves the maximal resource efficiency ensuring that patients wait for service is not extended unnecessarily.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".