Uptake and patient and provider communication modality preferences of virtual visits in primary care: a retrospective cohort study in Canada
Bibliographic record
Abstract
OBJECTIVES: To evaluate the uptake of a platform for virtual visits in primary care, examine patient and physician preferences for virtual communication methods and report on characteristics of visits and patients experience of care. DESIGN: A retrospective cohort study. SETTING: Primary care practices within five regions in Ontario, Canada after 18 months of access to virtual care services. PARTICIPANTS: 326 primary care providers and 14 291 registered patients. INTERVENTIONS: Providers used a platform that allowed them to connect with their patients through synchronous (audio/video) and/or asynchronous (secure messaging) communication. MAIN OUTCOME MEASURES: User-level data from the platforms including patient demographics, practice characteristics, communication modality used, visit characteristics and patients' satisfaction. RESULTS: Among the participants, 44% of registered patients and 60% of registered providers used the platform at least once. Among patient users, 51% completed at least one virtual visit. The majority of virtual visits (94%) involved secure messaging. The most common patient requests were for medication prescriptions (24%) and follow-up from previous appointment (22%). The most common provider request was to follow-up on test results (59%). Providers indicated that 81% of virtual visits required no follow-up for that issue and 99% of patients reported that they would use virtual care services again. CONCLUSIONS: While there are a growing number of primary care video visit services, our study found that both patients and providers in rostered practices prefer secure messaging over video. Despite fears that virtual visits would be overused by patients, when patients connected with their own primary care provider, many virtual visits appeared to replace in-person visits, and patients did not overwhelm physicians with requests. This approach may improve access and continuity in primary care.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".