The Surgical Patient Routing Problem: A Central Planner Approach
Bibliographic record
Abstract
Many patients face difficulties when accessing medical facilities, particularly in rural areas. To alleviate these concerns, medical centers may offer transportation to eligible patients. However, the operation of such services is typically not tightly coordinated with the scheduling of medical appointments. Motivated by our collaborations with the U.S. Veterans Health Administration, we propose an integrated approach that simultaneously considers patient routing and operating room scheduling decisions. We model this problem as a mixed-integer program. Unfortunately, realistically sized instances of this problem are intractable, so we focus on a special case of the problem that captures the needs of low-volume (e.g., rural) hospitals. We establish structural properties that are exploited to develop a branch-and-price algorithm, which greatly outperforms a commercial solver on the original formulation. We discuss several algorithmic strategies to improve the overall solution efficiency. We evaluate the performance of the proposed approach through an extensive computational study calibrated with clinical data. Our results demonstrate that there exist opportunities for healthcare providers to significantly improve the quality of their services by integrating scheduling and routing decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".