A Newsvendor Approach to Design of Surgical Preference Cards
Bibliographic record
Abstract
Surgical procedures require a large number of consumable supplies that need to be kept in hospital inventory and transported to the operating rooms (OR) before the surgery. A surgical preference card (SPC) provides a list of items to be prepared for each surgery. For each item, a SPC also specifies how many should be taken to the OR (fill quantity). As the usage of most consumables in the OR is subject to uncertainty, the cards also specify how many of the filled items should be opened at the beginning of the surgery (open quantity). The fill and open quantities control the flow of consumables between the hospital inventory and the ORs and directly affect the wastage in ORs. In this work, we formulate the problem of determining the fill and open quantities on the preference cards as a stochastic optimization problem, where the objective is to minimize a weighted sum of the expected wastage and operational costs. We show that, as in the newsvendor problem, the optimal solution for the fill and open quantities takes the form of critical quantiles of the item usage distribution in the OR. The solution form together with historical usage data provide a data-driven approach to design of SPCs, as well as insights on the value of including an open decision. We demonstrate our approach using extensive numerical experiments and real usage data from a Canadian hospital. The results suggest a potential for significant reduction of wastage and operational costs in the ORs.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".