Policies to promote secure messaging between patients and primary care providers: A comparison of Canadian provinces
Bibliographic record
Abstract
Context Asynchronous secure messaging (ASM) between patients and primary care providers has been increasingly adopted to varying degrees across regions. It may improve patient experience and access to care, but in some regions, it has been associated with increased burnout. Canadian provinces are at different stages ASM implementation, and variations in remuneration, and support can help elucidate the range of options to promote uptake of this medium and evenually encourage appropriate use. Objective We aim to highlight variations in ASM policies and identify challenges to sustained ASM implementation. Study Design and Analysis We identified five Canadian provinces that had some policies around ASM, but were at different stages of implementation. We did a rapid review of the academic literature, document analysis of provincial reports and websites, followed by semi-structured interviews of key informants in each region. We used elements of the ‘Nonadoption, Abandonment, Scale-up, Spread, and Sustainability Framework’, which has been extensively used to study uptake of technology in healthcare to inform the data collection and analysis. Setting or Dataset We conducted 12 semi-structured interviews and 1 email interview with provincial level policy makers and virtual care experts from 5 Canadian provinces. Results The ASM initiatives in every region differed in scale, duration, remuneration and integration levels. Remuneration policies included flat fees (with and without caps), tiered flat fees, and block fees, and one region had no billing codes. Implementation approaches included messaging through a provincial patient portal, a standalone messaging platform, as well as local pilots with varying degrees of integration with electronic medical records. Some emphasized the importance of a standardized interface and a ‘Digital Front Door’ for users to initiate messages and be triaged. Only one province used a ‘verification process’ to ensure solutions met basic interoperability capabilities. Digital health equity did not seem to a be a priority in most cases. Conclusions Canadian provinces are rolling out ASM programs differently, creating opportunities for shared learning. Mechanisms to ensure interoperability capabilities provide an opportunity for isolated pilots to scale up more broadly. While the range of remuneration policies do not correlate directly with uptake, it has generally been quite low. By comparing the key features
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".