Triage of Rheumatology Referrals Facilitates Wait Time Benchmarks
Bibliographic record
Abstract
OBJECTIVE: In 2014 the Canadian Rheumatology Association published wait time benchmarks for inflammatory arthritis (IA) and connective tissue disease (CTD) to improve patient outcomes. This study's aim was to determine whether centralized triage and the introduction of quality improvement initiatives would facilitate achievement of wait time benchmarks. METHODS: Referrals from September to November 2012 were retrospectively triaged by an advanced practice physiotherapist (APP) and compared to referrals triaged by an APP from January to March 2014. Each referral was assigned a priority ranking and categorized into one of 2 groups: suspected IA/CTD, or suspected non-IA/CTD. Time to initial consult and time to notification from receipt of referral were assessed. RESULTS: A total of 558 (n = 227 and n = 331 from 2012 and 2014, respectively) referrals were evaluated with 35 exclusions. In 2012, there were 96 (42.5%) suspected IA/CTD and 124 (54.9%) suspected non-IA/CTD patients at the time of the initial consult. Mean wait times in 2012 for patients suspected to have IA was 33.8 days, 95% CI 27.8-39.8, compared to 37.3 days, 95% CI 32.9-41.7 in suspected non-IA patients. In 2014, there were 131 patients (43%) with suspected IA based on information in the referral letter. Mean wait times in 2014 for patients suspected to have IA was 15.5 days, 95% CI 13.85-17.15, compared to 52.2 days, 95% CI 46.3-58.1 for suspected non-IA patients. Time to notification of appointment improved from 17 days to 4.37 days. CONCLUSION: Centralized triage of rheumatology referrals and quality improvement initiatives are effective in improving wait times for priority patients as determined by paper referral.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".