What are the Costs of Improving Access to Specialists through eConsultation? The Champlain BASE Experience
Bibliographic record
Abstract
Excessive wait times and poor access to care are among the most significant problems facing health care service delivery in Canada and beyond. We implemented the Champlain BASE eConsult service in the region of Ottawa, Canada to increase access to specialist care. We have collected ongoing utilization data and provider surveys over a three year period, providing a unique opportunity to explore the economic aspects of this multispecialty eConsult service. This is an economic evaluation from the perspective of the payer: the Ministry of Health and Long-Term Care of Ontario. All eConsults submitted during April 1, 2011 to March 31, 2014 were included. We attributed cost savings only to those cases where an eConsult led to the avoidance of a face-to-face specialist visit. A total of 2606 eConsults directed to 27 different speciality groups were included. In 40.3% (n=1051) of cases processed, a face-to-face specialist visit was originally planned but avoided as a result of eConsult, while 29% led to a referral. The estimated cost per eConsult for Years 1, 2, and 3 were $131.05, $10.34, and $6.45 respectively. Results from a sensitivity analysis project that the eConsult service will break even once we reach 7818 eConsults. This is one of the first studies to examine costs across a multispecialty eConsult service. We saw a marked decrease in the cost per eConsult over each annual period. Future research is needed to identify and examine similar outcomes that may lead to cost savings.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".