Improving Access to Gastroenterologist Using eConsultation: A Way to Potentially Shorten Wait Times
Bibliographic record
Abstract
BACKGROUND: Wait times for gastroenterologists in Canada continue to exceed recommended targets. Electronic consultation (eConsult) may reduce the need for face-to-face gastroenterologist visits. OBJECTIVE: The goal of this study was to identify the cases submitted to gastroenterologists though the Champlain BASE™ (Building Access to Specialists through eConsultation) eConsult service and explore their impact on primary care physicians' (PCPs) courses of action. METHODS: Gastroenterology cases submitted between June 2013 and January 2015 were categorized using a modification of the International Classification for Primary Care (ICPC-2) taxonomy. Question type (e.g., diagnosis or management) was classified using a validated taxonomy. RESULTS: Of the 121 gastroenterology consults reviewed, 33% were related to hepatology, 23% to GI symptoms, and 13% to specific luminal diseases. Among hepatology eConsults (n=40), 47% pertained to abnormal liver function testing. Overall, 51% of eConsults were related to diagnosis, 30% to management, 9% to drug treatments and 7% to procedures. PCPs received a reply within a median of 2.9 days. Only 25% of cases resulted in a face-to-face referral. CONCLUSIONS: The eConsult service provided timely, highly regarded advice from gastroenterologists directly to PCPs and often eliminated the need for a face-to-face consultation. An evaluation of the most commonly-posed questions could inform future continuing medical education activities for PCPs.
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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 | 0.004 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".