The Current State of Electronic Consultation and Electronic Referral Systems in Canada: an Environmental Scan
Bibliographic record
Abstract
Access to specialist care is a point of concern for patients, primary care providers, and specialists in Canada. Innovative e-health platforms such as electronic consultation (eConsultation) and referral (eReferral) can improve access to specialist care. These systems allow physicians to communicate asynchronously and could reduce the number of unnecessary referrals that clog wait lists, provide a record of the patient's journey through the referral system, and lead to more efficient visits. Little is known about the current state of eConsultation and eReferral in Canada. The purpose of this work was to identify current systems and gain insight into the design and implementation process of existing systems. An environmental scan approach was used, consisting of a systematic and grey literature review, and targeted semi-structured key informant interviews. Only three eConsultation/eReferral systems are currently in operation in Canada. Four themes emerged from the interviews: eReferral is an end goal for those provinces without an active eReferral system, re-organization of the referral process is a necessity prior to automation, engaging the end-user is essential, and technological incompatibilities are major impediments to progress. Despite the acknowledged need to improve the referral system and increase government spending on health information technology, eConsultation and eReferral systems remain scarce as Canada lags behind the rest of the developed world.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.035 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".