Virtual and in-person visits by Ontario physicians in the COVID-19 era
Bibliographic record
Abstract
Introduction We examined the coronavirus disease 2019 (COVID-19) pandemic impact on weekly trends in the billing of virtual and in-person physician visits in Ontario, Canada. Methods In this retrospective cohort study, physician billing records from Ontario were aggregated on a weekly basis for in-person and virtual visits from 3 January 2016 to 27 March 2021. For each type of visit, a segmented negative binomial regression analysis was performed to estimate the weekly pre-pandemic trend in billing volume per thousand adults (3 January 2016 to 14 March 2020), the immediate change in mean volume at the start of the pandemic, and additional change in weekly volume in the pandemic era (15 March 2020 to 27 March 2021). Results Before the start of the pandemic, the weekly volume of virtual visits per thousand adults was low with a 0.5% increase per week (rate ratio [RR]: 1.0053, 95% confidence interval [CI]: 1.0050–1.0056). A dramatic 65% reduction in in-person visits (RR: 0.35, 95% CI: 0.32–0.39) occurred at the start of the pandemic while virtual visits grew by 21-fold (RR: 21.3, 95% CI: 19.6–23.0). In the pandemic era, in-person visits rose by 1.4% per week (RR: 1.014, 95% CI: 1.011–1.017) but no change was observed for virtual visits ( p-value = 0.31). Overall, we noted a 57.6% increase in total weekly physician visits volume after the start of the pandemic. Discussion These results are meaningful for virtual care reimbursement models. Future study needs to assess the quality of care and whether the increase in virtual care volume is cost-effective to society.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".