The Surgical Patient Routing Problem: A Central Planner Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it