Operations Research Applications in Ontario's Healthcare System during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic challenged former norms in hospital operations and motivated innovative strategies for allocating healthcare resources. We present three strategies for mitigating stress on Ontario’s healthcare system. First, we consider inter-hospital patient transfers to address capacity concerns and uneven distribution of COVID-19 caseloads among hospitals. We formulate a queueing control problem and propose an approximate solution approach. With a case study of 21 hospitals we show that patient transfers significantly reduce the number of patient-days above occupancy thresholds, while distributing the COVID caseload. Second, we formulate an integer program to facilitate staff redeployment, and summarize some major challenges in redeployment efforts. Finally, we propose a virtual assessment centre for LTC residents. With data from seven hospitals we estimate staffing and diagnostic imaging resource requirements, and find that 1) aggregating call volume across multiple hospitals greatly reduces staffing requirements; 2) existing outpatient facilities could likely meet the diagnostic imaging requirements.
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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 | 0.002 | 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 | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".