A flexible integer linear programming formulation for scheduling\n clinician on-call service in hospitals
Bibliographic record
Abstract
Scheduling of personnel in a hospital environment is vital to improving the\nservice provided to patients and balancing the workload assigned to clinicians.\nMany approaches have been tried and successfully applied to generate efficient\nschedules in such settings. However, due to the computational complexity of the\nscheduling problem in general, most approaches resort to heuristics to find a\nnon-optimal solution in a reasonable amount of time. We designed an integer\nlinear programming formulation to find an optimal schedule in a clinical\ndivision of a hospital. Our formulation mitigates issues related to\ncomputational complexity by minimizing the set of constraints, yet retains\nsufficient flexibility so that it can be adapted to a variety of clinical\ndivisions.\n We then conducted a case study for our approach using data from the\nInfectious Diseases division at St. Michael's Hospital in Toronto, Canada. We\nanalyzed and compared the results of our approach to manually-created schedules\nat the hospital, and found improved adherence to departmental constraints and\nclinician preferences. We used simulated data to examine the sensitivity of the\nruntime of our linear program for various parameters and observed reassuring\nresults, signifying the practicality and generalizability of our approach in\ndifferent real-world scenarios.\n
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".