Supporting the spread and scale-up of electronic consultation across Canada: cross-sectional analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the process of implementing an electronic consultation (eConsult) service and evaluate its impact along key metrics outlined by the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework. DESIGN: Cross-sectional study. SETTING: Clinics using eConsult in four provinces across Canada: Alberta, Manitoba, Quebec and Newfoundland and Labrador. PARTICIPANTS: All eConsult cases submitted in four participating provinces were included. INTERVENTION: The eConsult service is a secure online application that allows primary care providers and specialists to communicate regarding a patient's care. We measured the impact using system utilisation data and mandatory close-out surveys completed at the end of each eConsult. MAIN OUTCOME MEASURES: Implementation progress and impact were examined using the five categories outlined by the RE-AIM framework: reach, effectiveness, adoption, implementation and maintenance. RESULTS: Four provinces provided data from different periods, ranging from 4 years (Alberta) to 10 months (Manitoba). Total cases completed ranged from 96 (Manitoba) to 6885 (Alberta). Newfoundland had the largest menu of available specialties (n=35), while Alberta and Quebec had the smallest (n=22). The most frequently requested groups varied across provinces, with only endocrinology appearing in the top five for all provinces. The average specialist response time ranged from 3 days (Manitoba) to 16.7 days (Alberta). Between 54% (Newfoundland) and 66% (Manitoba) of cases resulted in new or additional information. Primary care providers avoided completing referrals they had originally considered in 36% (Newfoundland) to 53% of cases (Manitoba), while only between 27 % (Quebec) and 29% (Newfoundland) of cases resulted in a referral. In every province, services demonstrated higher rates of usage in their last quarter of data than their first. CONCLUSIONS: eConsult was successfully implemented in four new provinces across Canada. Implementation strategies and scope varied, but services demonstrated substantial consistency on several key metrics, most notably on whether new information was learnt and impact on decision to refer.
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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".