A Robust Optimization Approach for Advance Scheduling in Health Care Systems with Demand Uncertainty: Policy Insights
Bibliographic record
Abstract
Patient wait times have increased significantly over the past few decades. According to the Canadian Institute for Health Information (CIHI), 40% of Canadians have experienced difficulties in receiving diagnostics tests. MRI wait times have increased by 26% from 2012 to 2016. The lengthy wait times for the health care systems are translated to economic losses and risks to the lives of Canadians. These inefficiencies in health care systems are an indication that health care infrastructure investment has not been able to keep pace with the increased demands. While building new health care infrastructure to create capacity may be the first solution that comes to mind, it is often not feasible due to budget limitations. Optimizing the use of the existing capacity is a more feasible and cost-effective solution to healthcare system inefficiencies. This research builds on previous literature and proposes a robust optimization method for a multi-priority multi-period advance scheduling problem with wait time target which is solved using a proposed adversarial-based algorithm. A sensitivity analysis is conducted to calibrate the model parameters. Several numerical examples are used to extract practical policy insights. The advantages of the robust model in comparison with the deterministic model are highlighted. It is shown that the robust modelling leads to policies that are easier to execute and are more suitable for policy planning purposes when compared to deterministic modelling.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".