Master Surgical Schedule Planning to Reduce Variability in Post-Operative Ward Bed Demand
Bibliographic record
Abstract
Abstract Peaks in patients’ demand for inward hospitalization usually lead to disruptions in the provision of healthcare, having negative effects on patient and staff satisfaction. The two main sources of ward bed demand are the emergency department and the surgical center; while the former is random by nature, the latter may be managed through proper allocation of surgical specialties to time slots (or blocks) in the center’s timetable (or Master Surgical Schedule – MSS), and efficient scheduling of surgical procedures within time slots across specialties. We propose a three-step method to design an MSS timetable. In step 1, we mine historical data to determine the average duration of surgical procedures and the average length of stay in wards required by each surgical specialty. In step 2, we use a genetic algorithm to determine a good quality timetable that minimizes the ward bed demand variability overall specialties. In step 3, we approximate the new timetable to the one currently in use at the hospital through a refinement heuristic. Our propositions were tested using data from a tertiary public teaching hospital. The resulting timetable reduced post-operative ward bed demand variability by 99.9%, keeping 97% of surgical specialties allocated in their original slots. To the best of our knowledge, this is the first method for long-term MSS design that reduces post-operative ward bed demand variability and changes in allocations in the current surgical center’s timetable. We innovate by considering the hospital's current timetable to search for solutions promoting minimum changes to the surgical center’s operation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".