LO16: Safety and efficiency of emergency physician supplementation in a provincially nurse-staffed telephone service for urgent caller advice
Bibliographic record
Abstract
Introduction: In 2008 British Columbia created a nurse (RN) staffed telephone triage service, (TTS) to provide timely advice to non-911 callers (811). A perception exists that some callers are inappropriately directed to emergency departments (EDs) thereby worsening crowding. We sought to determine whether supplementary emergency physician (EP) triage would decrease ED visits while preserving caller safety and satisfaction. Methods: TTS RNs use computer algorithms and judgment to triage callers. Potentially sick callers are directed to “seek care now” (red calls). Often this is to an ED depending on acuity and time of day. In the Vancouver Health Region from April-September 2016 between 8:00-24:00 hours, a co-located EP also spoke with “red” callers to provide further guidance. Callers were followed up with 1 week and satisfaction was evaluated on a 5-point Likert scale. The TTS data was linked to the regional ED database to assess ED attendance within 7 days, and the provincial vital statistics database for 30-day mortality. Our primary outcome was the proportion of unique “red” callers who did not attend the ED compared with a historical cohort one year earlier without EP triage in place. Secondary outcomes were the proportion of “red” callers advised not to attend the ED but (a) attended, (b) admitted, or (c) died. Results: In the study period there were 5105 “red” calls of which 3440 were transferred to the EP (67.4%), 2958 of EP assessed callers (86.0%) had a family doctor, but only one-quarter of such patients could contact their family doctor. Overall, 2301/3440 “red” callers did not attend an ED (67.0%) compared to 2508/4770 in the control period (52.6%), for an absolute reduction of 14.4% (95% CI 12.2 to 16.4%, p<0.0001). In callers for those <17 years old there was a 20.3% (95% CI 16.5 to 24.1%) reduction in ED visits compared to the control group: 771/1520 (50.7%) vs 364/1067 (30.4%). 40% of callers attending an ED (458/1139) were advised to try non-ED follow up by the MD and 108 (9.5%) were admitted, with no difference in 30-day mortality between groups. Age and CTAS distribution were similar between the two groups and the non MD-transferred cohort. Mean caller satisfaction was excellent (4.7/5.0). Conclusion: EP supplementation of a RN advice service has the potential to reduce ED visits by almost 15% while providing excellent safety and satisfaction.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".