Improving access to specialists in remote communities: a cross-sectional study and cost analysis of the use of eConsult in Nunavut
Bibliographic record
Abstract
BACKGROUND: (Building Access to Specialists through eConsultation) service facilitates asynchronous communication between primary care providers (PCP) and specialists. The service was extended to several PCPs in Nunavut in 2014. OBJECTIVE: To (1) describe the use of eConsult services in Nunavut, and (2) conduct a costing evaluation. DESIGN: A cross-sectional study and cost analysis of all eConsult cases submitted between August 2014 and April 2016. RESULTS: PCPs from Nunavut submitted 165 eConsult cases. The most popular specialties were dermatology (16%), cardiology (8%), endocrinology (7%), otolaryngology (7%), and obstetrics/gynaecology (7%). Specialists provided a response in a median of 0.9 days (IQR=0.3-3.0, range=0.01-15.02). In 35% of cases, PCPs were able to avoid the face-to-face specialist visits they had originally planned for their patients. Total savings associated with eConsult in Nunavut are estimated at $180,552.73 or $1,100.93 per eConsult. CONCLUSIONS: The eConsult service provided patients in Nunavut's remote communities with prompt access to specialist advice. The service's chief advantage in Canada's northern communities is its ability to offer electronic access to a breadth of specialties far greater than could be supported locally. Our findings suggest that a territory-wide adoption of eConsult would generate enormous 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".