Telephone consultations with otolaryngology – head and neck surgery reduced emergency visits and specialty consultations in northern Alberta
Bibliographic record
Abstract
BACKGROUND: RAAPID (Referral, Access, Advice, Placement, Information, and Destination) is a 24-h call center in Alberta, Canada, facilitating urgent telephone consultations between physicians and specialists. We evaluated the extent to which RAAPID calls to Otolaryngology-Head and Neck Surgery (OHNS) reduced visits to the emergency department and specialty clinics. METHODS: This was a cross-sectional study evaluating all telephone consultations to OHNS from physicians in northern Alberta between 2013 and 2014 (T1) (where consultations by residents occurred) and 2015 to 2017 (T2) (where consultations were done by consultants during office hours and residents during after hours). Outcomes of the calls included medical advice, specialty clinic referrals, and emergency department (ED) referrals. Differences in the reduction of ED visits and costs, overall as well as in T1 and T2, were assessed using multivariate logistic regression. RESULTS: Overall, 62.3% (1064/1709) of telephone consultations reduced ED visits consisting of advice being provided (n = 884; 83.1%) and referral to specialty clinics (n = 180; 16.9%). The adjusted odds ratio of calls reducing emergency visits in T2 as compared to T1 was 2.47 (95% CI 1.99 to 3.08). The adjusted odds ratio of reducing ED visits during office hours compared to after-hours 2.54 (95% CI 1.77-3.64). The estimated direct costs avoided from ED visits in T1 and T2 were $42,224.22 and $114,393.86, respectively. CONCLUSION: RAAPID telephone consultations to OHNS were effective in reducing ED visits and healthcare costs. This model should be considered in other areas to improve efficiencies within the health system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".