Scaling up eConsult for access to specialists in primary healthcare across four Canadian provinces: study protocol of a multiple case study
Bibliographic record
Abstract
BACKGROUND: Canada has been referred to as the land of 'perpetual pilot projects'. Effective innovations often remain small in scale, with limited impact on health systems. Several innovations have been developed in Canada to tackle important challenges such as poor access to services and excessive wait times - one of the most promising innovations that has been piloted is eConsult, which is a model of asynchronous communication that allows primary care providers to electronically consult with specialists regarding their patients' medical issues. eConsult pilot projects have been shown to reduce wait times for specialist care, prevent unnecessary referrals and reduce health system costs. eConsult has been spread throughout Ontario as well as to certain regions in Manitoba, Quebec, and Newfoundland and Labrador. Our aim is to understand and support the scale-up process of eConsult in Ontario, Quebec, Manitoba, and Newfoundland and Labrador. Our specific objectives are to (1) describe the main components of eConsult relevant to the scale-up process in each province; (2) understand the eConsult scale-up process in each province and compare across provinces; (3) identify policy issues and strategies to scaling up eConsult in each province; and (4) foster cross-level and cross-jurisdictional learning on scaling up eConsult. METHODS: We will conduct a qualitative multiple case study to investigate the scaling up of eConsult in four Canadian provinces using a grey literature review, key stakeholder interviews (10 interviews/province), non-participant observations, focus groups and deliberative dialogues. We will identify the main components of eConsult to be scaled up using logic models (obj. 1). Scaling up processes will be analysed using strategies adapted from process research (obj. 2). Policy issues and strategies to scale-up eConsult will be analysed thematically (obj. 3). Finally, a symposium will foster pan-Canadian learning on the process of scaling up eConsult (obj. 4). DISCUSSION: This study will likely increase learning and support evidence-based policy-making across participating provinces and may improve the capacity for a pan-Canadian scale-up of eConsult, including in provinces where eConsult has not yet been implemented. This work is essential to inform how similar innovations can reshape our health systems in the evolving information age.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
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 | 0.043 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".