Evaluation of an Electronic Consultation Service in Obstetrics and Gynecology in Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it