Ten Steps to Establishing an e-Consultation Service to Improve Access to Specialist Care
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
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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