A Network‐Based Formulation for Scheduling Clinical Rotations
Bibliographic record
Abstract
We investigate the scheduling practices of a medical school that must assign a cohort of students to a series of clinical rotations, while respecting both operational and quality‐of‐service requirements. Students become available to start clerkship progressively throughout the year and can complete rotations at hospitals in different geographic regions. Each hospital may offer a subset of the clinical rotations, with different start dates, capacities, and cost rates. We propose a novel network‐flow model based on decision diagrams, a graphical structure that compresses the state space of a dynamic program, to model feasible schedules. We demonstrate that our network model has several interesting structural features, is computationally superior as compared to a classical mixed‐integer linear program, and can be used to generate useful insights that can aid in managerial decision‐making. Using a dataset collected from the American University of the Caribbean, we perform a counterfactual analysis which shows that had our scheduling approach been implemented, a cost reduction of approximately 19% on average could have been achieved. To understand how assignment decisions can affect future costs, we develop a discrete‐event simulation of the licensing examination and clerkship scheduling process. We then compare our exact scheduling approach with current practice and achieve an average cost reduction of 25%. We also show that this cost reduction is robust with respect to estimation and forecast uncertainty, specifically, the licensing exam failure rate and the future cohort size.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".