Choosing a Model for eConsult Specialist Remuneration: Factors to Consider
Bibliographic record
Abstract
Electronic consultation (eConsult) is an innovative solution that allows specialists and primary care providers to communicate electronically, improving access to specialist care. Understanding the cost implications of different remuneration models available to pay specialists is of critical importance as adoption of these services continues to increase. We used data collected through the Champlain BASE (Building Access to Specialists through eConsultation) eConsult service to simulate the cost implications of different remuneration models in Canada. The prorated hourly rate model averaged $45.72 CAD (Canadian Dollar) per eConsult while the prorated hourly rate with incentive averaged $51.90 CAD per eConsult, and the fee for service cost $60.50 CAD per eConsult. Paying all specialty groups to block three hours per week for eConsults averaged $337.44 CAD per eConsult and paying for 1-h blocks averaged $133.41 CAD per eConsult. As the remuneration of specialists is the largest cost driver of an established eConsult service, our findings can inform policymakers considering the implementation of eConsult or wishing to further develop an existing service.
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.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".