Improved access and care through the implementation of virtual Hallway, a consultation platform in Nova Scotia: preliminary findings from a feasibility evaluation
Bibliographic record
Abstract
BACKGROUND: While previous studies have examined various platforms that enable providers to connect, Virtual Hallway (VH) stands out with its unique features. The value add is that this online platform connects primary care providers and specialists for synchronous phone-based conversations and aims to reduce referrals and enhance the quality of referrals. VH allows providers to easily log in, select the required specialty, book call times, receive reminders, and have calls documented, ensuring a high connection rate. In May 2022, the provincial health authority in Nova Scotia, a Canadian province, and VH initiated a feasibility study facilitated through the Health Innovation Hub in Nova Scotia. The goal was to enable primary care providers to connect with specialists, thereby reducing wait times and unnecessary referrals, and facilitating timely access to relevant clinical direction for patients. The current evaluation assessed utilization, value for money in economic analysis, and consultation experiences. METHODS: The study used post, cross-sectional, and cost-benefit study designs. We collected data through various methods, including administratively recorded utilization, theory-driven surveys, and cost data. Utilization was measured by the number of completed consults and the number of healthcare professionals using the VH platform. We analyzed the data using a combination of descriptive statistics and a cost-benefit analysis, which also involved conducting probabilistic sensitivity analysis. RESULTS: The study found that approximately 84% of the VH consultations avoided needing in-person specialist referrals. The return on investment was 1.8 (95% CI: 0.8 to 3.0), indicating that the monetary value of the measurable benefits associated with VH exceeded the value of the resources invested. The provider experience survey revealed high satisfaction levels with VH across user groups, with 92% of specialists and 96% of primary care providers reporting being satisfied or highly satisfied with their experience. These positive indicators of provider experience were further supported by the fact that 97% of respondents agreed or strongly agreed that they intended to continue to use VH in their practice, and 97% of respondents agreed or strongly agreed that they would recommend VH to a colleague. CONCLUSIONS: The study suggests that VH was well-received by users, with high levels of satisfaction reported and a reduced need for in-person referrals. It also represented value for money. Further research could explore how the availability of virtual health services can lead to reduced utilization of healthcare resources among different groups of patients.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".