Evaluation of an Electronic Consultation Service in Obstetrics and Gynecology in Ontario
Bibliographic record
Abstract
OBJECTIVE: To describe the effectiveness of an electronic consultation (eConsult) service by examining the number of traditional referrals that were avoided as a result of the service, to characterize the type and content of the clinical questions being asked, and to describe the time required for the specialist to complete each eConsult. METHODS: This is a retrospective electronic chart review study. All eConsults directed to obstetrics and gynecology from July 2011 to January 2015 were reviewed. Each eConsult was categorized by clinical topic and question type in predetermined categories. Mandatory post-eConsult surveys for primary care providers were analyzed to determine the number of traditional consults avoided and to gain insight into the perceived value of eConsults. The amount of time reported by the specialist to answer each eConsult was analyzed. RESULTS: A total of 394 of 5,597 eConsults were directed to obstetrics and gynecology (7.0%). In 34.3% of eConsults, primary care providers indicated that a traditional consult was avoided. Pregnancy issues and gynecologic cancer screening issues were the most common queries. Primary care providers highly valued the eConsult and the majority of eConsults were completed within 15 minutes (98.8%). CONCLUSION: Electronic consultations were effective at reducing the number of traditional consults requested over 3.5 years. This initiative has potential to reduce current wait times for traditional consultation in Canada and to make the consultation process more effective. The service was feasible and well-received by primary care providers.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".