Transforming healthcare delivery: a descriptive study of a novel provider-to-provider virtual care platform
Bibliographic record
Abstract
Introduction Addressing challenges in access to specialty care, particularly long wait times and geographic disparities, is a pressing issue in the Canadian healthcare system. This study aimed to evaluate the impact and feasibility of provider-to-provider phone consultations between primary care providers (PCPs) and specialists using a novel virtual care platform in Nova Scotia (Virtual Hallway). Methods We conducted a cross-sectional survey over 5 months, involving 211 PCPs and 34 specialists across Nova Scotia. The survey assessed the need for formal in-person referrals as well as clinician satisfaction. Statistical methods included descriptive statistics and the one-sample t-test. Results We found that 84% of provider-to-provider phone consultations negated the need for an in-person specialist referral. It was also reported that 90% of patients that did require in-person consultation had enhanced care while they awaited an in-person appointment with a specialist. Very high levels of satisfaction were reported among both PCPs and specialists, and there was a noticeable increase in billing volumes related to these consultations as measured by provincial billing codes. Conclusion The findings indicate that provider-to-provider phone consultations are feasible, well-accepted and also effective in reducing the need for in-person specialist visits. This approach offers a promising avenue for alleviating waitlist burdens, enhancing the quality of care, and improving the overall efficiency of healthcare delivery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".