Scaling-Up eConsult: Promising Strategies to Address Enabling Factors in Four Jurisdictions in Canada
Bibliographic record
Abstract
BACKGROUND: Effective healthcare innovations are often not scaled up beyond their initial local context. Lack of practical knowledge on how to move from local innovations to large-system improvement hinders innovation and learning capacity in health systems. Studying scale-up processes can lead to a better understanding of how to facilitate the scale-up of interventions. eConsult is a digital health innovation that aims to connect primary care professionals with specialists through an asynchronous electronic consultation. The recent implementation of eConsult in the public health systems of four Canadian jurisdictions provides a unique opportunity to identify different enabling strategies and related factors that promote the scaling up of eConsult across jurisdictions. METHODS: We conducted a narrative case study in four Canadian provinces, Quebec, Ontario, Manitoba, and Newfoundland & Labrador, over a 3-year period (2018-2021). We observed provincial eConsult committee meetings (n=65) and national eConsult forums (n=3), and we reviewed internal documents (n=93). We conducted semi-structured interviews with key actors in each jurisdiction (eg, researchers, primary care professionals, specialists, policy-makers, and patient partners) (n=40). We conducted thematic analysis guided by the literature on factors and strategies used to scale up innovations. RESULTS: We identified a total of 31 strategies related to six key enabling factors to scaling up eConsult, including: (1) multi-actor engagement; (2) relative advantage; (3) knowledge transfer; (4) strong evidence base; (5) physician leadership; and (6) resource acquisition (eg, human, material, and financial resources). More commonly used strategies, such as leveraging research infrastructure and bringing together various actors, were used to address multiple enabling factors. CONCLUSION: Actors used various strategies to scale up eConsult within their respective contexts, and these helped address six key factors that seemed to be essential to the scale-up of eConsult.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".