Optimization of elective patient admissions during pandemics: case of COVID-19
Bibliographic record
Abstract
With advent of the Covid pandemic, hospitals grappled with how to manage the sudden surge in demand while still treating those in need of service for other reasons. This study aims to provide an adaptive elective admission scheduling policy that maximizes throughput during a pandemic while maintaining the ability of a hospital to empty a specified number of beds over a short warning period (e.g. 5 days). This ability we call nimbleness. We propose a heuristic method based on two mixed-integer linear programming models (MILP) and a simulation model. The first MILP creates the initial schedule for patient admissions over the planning horizon while maximizing patient throughput. The second MILP considers the uncertainty of the emergency arrivals (including pandemic induced) and the patients’ length of stay and maximizes the number of scheduled patients admitted while ensuring the hospital’s nimbleness. A simulation model is built to create daily random arrivals and discharges. The models connect through an automated feedback loop until the heuristic approach converges on a solution. Numerical results demonstrate the ability of the approach to maintain high throughput while still responding to pandemic surges.
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How this classification was reachedexpand
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".