Specialists Triaging Referrals to eConsult: a feasibility study including acceptability and impact of providing advice on primary health care providers
Bibliographic record
Abstract
BACKGROUND: Specialists review referrals for appropriateness and urgency. Limited capacity results in specialists declining referrals leaving primary care providers (PCP), patients, and specialists frustrated. Since specialist availability is unlikely to improve significantly, innovative solutions are required. This study evaluated the feasibility, acceptability, safety and impact of a new referral triage option Triaging Referrals to eConsult (TReC) which enables specialists to provide advice in lieu of an appointment (advice only) or provide advice to support the PCP until the appointment occurs (advice and appointment). METHODS: Utilization metrics were prospectively collected (number (%) of referrals converted, time from receipt of referral to completion (response time) and specialist self-reported billing time. To assess PCP opinions on safety (advice was clearly identified and actionable) and acceptability (comfort in patient not seeing a specialist, additional time burden and support for expansion) two surveys, one for those referrals triaged to advice only and another for those triaged to advice and appointment, were faxed 14 days after the referral response. RESULTS: From November 1, 2022, to October 31, 2023, five specialties converted 930/16,880 referrals-656 (3.8%) to Advice Only and 274 (1.6%) to Advice and Appointment for an overall conversion rate of 5.5%. 192/1010 (19%) PCPs returned the survey with over 80% agreeing that the advice was easily recognizable, conversion to eConsult was acceptable and the advice was helpful and actionable. INTERPRETATION: Enabling specialists to provide advice to PCPs, often in lieu of an appointment, was acceptable, feasible with no major patient safety concerns.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".