Effect of implementation of an electronic consult referral platform (eConsult) to triage retina referrals in Manitoba
Bibliographic record
Abstract
OBJECTIVE: eConsult allows specialists to diagnose and recommend treatment plans for nonurgent conditions without the need for patient travel. Our purpose is to evaluate the effectiveness of eConsult in reducing unnecessary in-person retinal consultations in Manitoba. DESIGN: Retrospective eConsult chart review. PARTICIPANTS: Any person for whom an eConsult was submitted for a retina problem in Manitoba between November 2020 and October 2023 (n = 196). METHODS: The primary objective was to quantify eConsults requiring no in-person referral, routine in-person referral, or urgent (within 4 weeks) in-person referral. Secondary objectives included describing characteristics of eConsults and quantifying the amount of time spent on the platform by both the referring provider and the specialist. On the basis of these variables, patient travel, consultation time, and specialist billings savings were calculated. RESULTS: 66.8% of eConsults did not require in-person assessment (n = 131), 24.5% required to be seen on a routine basis (n = 48), and 8.7% required to be seen within 4 weeks (n = 17). This translated to a net cost of $2,660.43 for the provincial government in billings over 3 years, but 81 990 km saved in patient travel. 99% of eConsults came from optometrists (n = 194). Referring providers spent an average of 10.6 ± 9.4 minutes on the platform per referral, and the specialist consultant spent 9.1 ± 6.6 minutes. CONCLUSION: eConsult is a potentially cost-effective way to address increasing demand for retinal services, reduce wait times by reducing unnecessary referrals, and facilitate data sharing between optometrists and ophthalmologists.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".