Ten Steps to Establishing an e-Consultation Service to Improve Access to Specialist Care
Bibliographic record
Abstract
There is dissatisfaction among primary care physicians, specialists, and patients with respect to the consultation process. Excessive wait times for receiving specialist services and inefficient communication between practitioners result in decreased access to care and jeopardize patient safety. We created and implemented an electronic consultation (e-consultation) system in Eastern Ontario to address these problems and improve the consultation process. The e-consultation system has passed through the proof-of-concept and pilot study stages and has effectively reduced unnecessary referrals while receiving resoundingly positive feedback from physician-users. Using our experience, we have outlined the 10 steps to developing an e-consultation service. We detail the technical, administrative, and strategic considerations with respect to (1) identifying your partners, (2) choosing your platform, (3) starting as a pilot project, (4) designing your product, (5) ensuring patient privacy, (6) thinking through the process, (7) fostering relationships with your participants, (8) being prepared to provide physician payment, (9) providing feedback, and (10) planning the transition from pilot to permanency. In following these 10 steps, we believe that the e-consultation system and its associated improvements on the consultation process can be effectively implemented in other healthcare settings.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".