Improving Appropriate Access to Care With Central Referral and Triage in Rheumatology
Bibliographic record
Abstract
OBJECTIVE: To evaluate the short-term and long-term impact of a centralized system for the intake and triage of rheumatology referrals on access to care and referral quality. METHODS: An innovative central referral process, the Central Referral and Triage in Rheumatology (CReATe Rheum) program, was implemented in 2006, serving a referral base of 2 million people. Referrals are received in a central office, triaged by trained nurses, and assigned to the next available appointment on a prioritized basis. To evaluate the short-term impact, we compared wait times, duplicate referrals, and no-shows from a pre-implementation practice audit to a 2-year post-implementation evaluation (January 2007 to December 2008). Rheumatologists also assessed the quality and completeness of the referral information and accuracy of the urgency category assigned during triage. We evaluated the long-term impact by tracking referral volume, wait times, and rheumatologist manpower each year until December, 2013. RESULTS: During the first 2 years, wait-time variability between rheumatologists decreased, and wait times were reduced for moderate and urgent referrals. CReATe Rheum improved the quality of referral information and eliminated duplicate referrals. The urgency of the referral was assigned correctly in 90% of referrals. Over the long term, CReATe Rheum maintained short wait times for more urgent patients despite a growing number of referrals and a stable number of rheumatologists. CONCLUSION: A centralized system for the intake and triage of rheumatology referrals improved referral quality, reduced system inefficiencies, and effectively managed wait times on a prioritized basis for a large referral population.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".