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Record W4416182962 · doi:10.1038/s41746-025-02060-9

Changes in emergency and primary care use after adding virtual physicians to HealthLink BC’s 8-1-1 program

2025· article· en· W4416182962 on OpenAlexafffundabout
Sonya Cressman, Kurtis Stewart, Frank Scheuermeyer, Lindsay Hedden, Ross Duncan, Linda Riches, Riyad B. Abu‐Laban, D. A. Raff, J. Assali, Helen Novak Lauscher, K. Halani, B. Wong, Sandra Sundhu, Kendall Ho

Bibliographic record

Venuenpj Digital Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsBurnaby HospitalMinistry of HealthSimon Fraser UniversityProvidence Health CareMichael Smith Health Research BCUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersCanadian Institutes of Health ResearchMinistry of Health, British Columbia
KeywordsTelehealthPrimary careHealth careIntervention (counseling)TelemedicineEmergency departmentPrimary health careService (business)Health services

Abstract

fetched live from OpenAlex

Health human resource constraints in Canada have left millions of patients without timely access to primary care (PC), leading many to attend emergency departments (ED) to see a doctor. We evaluated changes in service use and costs resulting from the addition of virtual physicians to HealthLink BC's 8-1-1 program. Visits to ED and PC within 30 days and healthcare costs were measured with time series data from 445,630 telehealth users. The virtual physician intervention was associated with 16.14 (p = 0.001, 95% CI = 25.95-6.33) fewer ED visits and 106.14 (p < 0.001, 95% CI = 74.78-137.49) more PC visits in 30 days per 1000 patients. Patients above age 15, without a known chronic illness or existing relationship with a PC provider, had higher PC follow-up and patient-paid medical travel costs; healthcare system costs were neutral over a year. Long-term health outcomes from increased PC follow-up and variability in the amount of medical travel costs paid by patients will be important economic parameters to consider in Canadian health policy evaluations.

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.

How this classification was reachedexpand

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)0.000
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 score0.766
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.351
Teacher spread0.327 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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