Effect of single-entry referral models and team-based care on wait times for hip and knee joint replacement in Ontario: a simulation study
Bibliographic record
Abstract
BACKGROUND: Long wait times for scheduled surgery are a major problem in Canadian health systems. We sought to determine the extent to which single-entry referral models (next available consultation), team-based care models (next available surgery regardless of consulting surgeon), or both could affect wait times for consultations and surgery. METHODS: We performed a discrete-event simulation study of wait times for consultations and surgeries for knee and hip joint replacement in Ontario's 5 postal regions using prospectively collected data on surgical wait times. We simulated the effects of coordinated referral models on the wait time for consultation (wait 1) and surgery (wait 2). RESULTS: Coordinated models led to larger reductions in high-outlier wait times (as reflected by the 90th percentile and the percentage of patients exceeding wait-time targets) than on median wait times when compared with the status quo. Single-entry referral models largely influenced wait 1, and team-based models of care affected only wait 2. Fully integrated models incorporating both single-entry referral and team-based care largely prevented patients from exceeding both wait-1 and wait-2 targets; the percentage of patients exceeding wait-1 targets in these models was 0% in all regions, and the percentage exceeding wait-2 targets was 0% except for Ontario West (2.0%, from 35.7% at baseline), East (1.1%, from 22.7% at baseline), and North (1.0%, from 25.1% at baseline). INTERPRETATION: Coordinated referral and practice models improve access to scheduled surgery in Canadian health systems. Implementation of these models could largely eliminate prolonged wait times for joint replacement surgery in Ontario.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".