An Evaluation of Strategies to Reduce Waiting Times for Total Joint Replacement in Ontario
Bibliographic record
Abstract
BACKGROUND: In 2005, the median waiting time for total hip and knee joint replacements in Ontario was greater than 6 months, which is considered longer than clinically appropriate. Demand is expected to increase and exacerbate already long waiting times. Solutions are needed to reduce waiting times and improve waiting list management. METHODS: We developed a discrete event simulation model of the Ontario total joint replacement system to evaluate the effects of 4 management strategies on waiting times: (1) reductions in surgical demand; (2) formal clinical prioritization; (3) waiting time guarantees; and (4) common waiting list management. RESULTS: If the number of surgeries performed increases by less than 10% each year, then demand must be reduced by at least 15% to ensure that, within 10 years, 90% of patients receive surgery within their maximum recommended waiting time. Clinically prioritizing patients reduced waiting times for high-priority patients and increased the number of patients at all priority levels who received surgery each year within recommended maximum waiting times by 9.3%. A waiting time guarantee for all patients provided fewer surgeries within recommended waiting times. Common waiting list management improved efficiency and increased equity in waiting across regions. DISCUSSION: Dramatically increasing the supply of joint replacement surgeries or diverting demand for surgeries to other jurisdictions will reduce waiting times for total joint replacement surgery. Introducing a strictly adhered to patient prioritization scheme will ensure that more patients receive surgery within severity-specific waiting time targets. Implementing a waiting time guarantee for all patients will not reduce waiting times--it will only shuffle waiting times from some patients to others. To reduce waiting times to clinically acceptable levels within 10 years, increases in the number of surgeries provided greater than those observed historically or reductions in demand are needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".