Association of Virtual Care Expansion With Environmental Sustainability and Reduced Patient Costs During the COVID-19 Pandemic in Ontario, Canada
Bibliographic record
Abstract
Importance: There is a growing focus on environmental sustainability in health care. Objective: To estimate the environmental and patient-level financial benefits associated with the widespread adoption of virtual care during the COVID-19 pandemic. Design, Setting, and Participants: This population-based cross-sectional study obtained data from linked administrative databases in the universal health care system of Ontario, Canada, from March 2020 to December 2021. Participants included all people with a physician claim for at least 1 episode of virtual care. Exposures: Patients were stratified by age, socioeconomic status quintiles, Charlson Comorbidity Index, and area of residence (rural or urban). Main Outcomes and Measures: The primary outcomes were total travel distance and estimated travel-related carbon dioxide emissions avoided owing to virtual care visits. Different model assumptions were used to account for electric and hybrid vehicles and public transit use. The secondary outcomes were estimated patient costs (gasoline, parking, or public transit expenses) avoided. Results: During the 22-month study period, 10 146 843 patients (mean [SD] age, 44.1 [23.1] years; 5 536 611 women [54.6%]) had 63 758 914 physician virtual care visits. These visits were associated with avoidance of 3.2 billion km of travel distance and between 545 and 658 million kg of carbon dioxide emissions. Patients avoided an estimated total of $569 to $733 million (Canadian [US $465-$599 million]) in parking, public transit, and gasoline costs. Carbon dioxide emission avoidance and patient cost savings were more apparent in patients living in rural areas, those with higher comorbidity, and those who were older than 65 years. Conclusions and Relevance: Results of this study suggest that virtual care was associated with a large amount of carbon dioxide emissions avoided owing to reduced patient travel and with millions of dollars saved in parking, gasoline, or public transit costs. These benefits are likely to continue as virtual care is maintained as part of the health care system.
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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".