Characteristics and healthcare use of patients attending virtual walk-in clinics: a cross-sectional analysis
Bibliographic record
Abstract
ABSTRACT Importance Virtual walk-in clinics have proliferated since the onset of COVID-19. Yet, little is known about those who participate in this care model, and how virtual walk-in clinics contribute to care continuity and patient healthcare utilization. Objectives To describe the characteristics and healthcare use of patients using virtual walk-in clinics compared to the general population, and a subset that received any virtual family physician visit. Design This was a retrospective, population-based, cross-sectional study. Setting Ontario, Canada’s most populous province. Participants Patients who had received at least one family physician visit at one of 13 virtual walk-in clinics from April 1 st to December 31 st , 2020. They were compared to Ontario residents who had any virtual family physician visit in the same time period. Main Outcome(s) and Measure(s) Patient characteristics and 30-day post-visit healthcare utilization. Results Virtual walk-in patients (N=132,168) had fewer comorbidities and lower previous healthcare utilization than Ontarians with any virtual visit. Less than 0.1% of virtual walk-in visits were with a patient’s own family physician. Compared to Ontarians having any virtual family physician visit, virtual walk-in patients were significantly less likely to have a subsequent in-person visit with the same physician (0.2% vs. 11.0%, SMD = 0.48), more likely to have a subsequent virtual visit (30.3% vs. 21.9%, SMD = 0.19), and twice as likely to have an emergency department visit within 30 days (8.3% vs. 4.1%, SMD = 0.18), an effect that persisted after adjustment and across rurality groups: large urban (aOR 2.26; 95% CI 2.08-2.45), small urban (aOR 2.08; 95% CI 1.99-2.18), and rural (aOR 1.87; 95% CI 1.69-2.07). Conclusions and Relevance Compared to Ontarians attending any family physician virtual visit, virtual walk-in patients were less likely to have a subsequent in-person physician visit, but were more likely to visit the emergency department. Low continuity and the lack of physical examination may be contributing to increased emergency department utilization for virtual walk-in clinic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".