Improving Health Care Access: A Cross-Provincial Evaluation of a Virtual Consultation Platform in Prince Edward Island
Bibliographic record
Abstract
Introduction: Access to specialist care remains a major challenge in rural and remote areas of Canada. Virtual Hallway is a secure digital platform that enables timely, provider-to-provider telephone consultations between primary care providers (PCPs) and specialists. This study evaluates satisfaction and effectiveness of the platform in Prince Edward Island (PEI), a small, rural province, across two time points to assess its sustained impact and usage patterns. Methods: A cross-sectional survey was conducted during two periods in 2024. PCPs were invited to complete postconsultation surveys, which assessed user satisfaction, in-person referral avoidance, and motivations for specialist selection. Descriptive statistics and chi-square tests were used for analysis. Results: Survey data included 181 postconsultation responses. Referral avoidance remained high (85% and 75% at time points 1 and 2, respectively). User satisfaction was consistently high among both PCPs (98%–100%) and specialists (92%–98%). PCPs reported improved care quality even when referrals were not avoided. Over time, specialist selection shifted from local availability to unmet specialty access within PEI ( p = 0.03), suggesting increased use for complex cases. Conclusion: The Virtual Hallway platform demonstrated sustained effectiveness in improving access to specialist care and reducing in-person referrals in a rural province. These findings support broader implementation of virtual peer-to-peer consultation platforms to address access disparities across Canada.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".