Changes in driving distance to specialist physicians in the era of virtual care: a population-based cohort study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Whether virtual health care has changed access to services for patients living far from a specialist physician is unknown. We aimed to determine whether driving distances between patients and their specialists had changed following increased availability of virtual care in Ontario, such that specialists saw patients from farther away. METHODS: We performed a population-based cohort study using linked health and administrative databases. We included all specialist physicians working in Ontario from Jan. 1, 2019, to Nov. 30, 2019 (pre-virtual care period) and from Jan. 1, 2022, to Nov. 30, 2022 (virtual care period), and their patients. Outcomes were measures of proximity between specialists and their patients including differences in 90th-percentile driving distance, mean driving time, and the proportion of patients with driving times longer than 60 minutes between time periods. We used multivariable linear regression models to compare outcomes across physician specialties, adjusting for physician age, sex, practice size, and location. RESULTS: We included 11 096 specialists (4232 surgical and 6864 medical; 0.8% rural). After adjustment, we found no meaningful changes in the 90th-percentile driving distance between time periods for surgical (difference 6.7 km, 95% confidence interval [CI] -4.1 km to 17.5 km) or medical specialties (difference 1.3 km, 95% CI -6.6 km to 9.2 km). For surgical specialists, the proximity measures of mean driving time increased by 5 minutes (95% CI 1 min to 10 min) and the proportion of patients living more than 60 minutes away increased by 2.1% (95% CI 0.7% to 3.9%), but we saw no significant change for medical specialists. INTERPRETATION: After expansion of virtual care, the distance between specialists and patients did not meaningfully change. To make virtual care more accessible, especially for those living in rural areas, attention should be paid to other factors such as referral patterns and the role of patients in determining the type of visit they prefer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".