Improving accessibility through referral management: setting targets for specialist care
Bibliographic record
Abstract
The use of optimized referral distribution strategies to improve access to specialty care is assessed. A mathematical model of a generalized care pathway is developed and the distribution of referrals is posed as an optimization problem. The objective is to minimize time from referral to a targeted stage in the care pathway (e.g., specialist consult, surgery, etc.). Numerical simulations informed by data on hip and knee surgeries demonstrate wait reductions from 21 to 38 days (16.8–30.4%) from time of referral to time of consult and from 33 to 66 days (12.6–24.7%) to time of surgery. However, the optimized referral distribution strategy minimizes wait times to the targeted stage only; wait times to non-targeted stages in the care pathway are suboptimal and may increase as an unintended consequence. Consequently, to achieve desired improvements in access, the targeted stage for wait time minimization must be carefully identified and prioritized.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".