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Record W4403524482 · doi:10.1186/s43058-024-00651-3

Improved access and care through the implementation of virtual Hallway, a consultation platform in Nova Scotia: preliminary findings from a feasibility evaluation

2024· article· en· W4403524482 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueImplementation Science Communications · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsThe Audio Recording AcademyNova Scotia Health Authority
Fundersnot available
KeywordsNova scotiaPhoneSpecialtyHealth careDescriptive statisticsReferralBusinessMedicineMedical emergencyNursingComputer scienceFamily medicineGeographyStatistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.204
GPT teacher head0.542
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it