Telemedicine and reduction of travel-related environmental impact of digestive clinic care in a Canadian province
Bibliographic record
Abstract
Background: Telemedicine offers a promising approach to reduce the carbon footprint of healthcare delivery by minimizing travel-related greenhouse gas emissions. In this study, we quantified the carbon emissions savings from shifting gastroenterology clinic visits from in-person to telemedicine in a single gastroenterologist's clinic in a major urban Canadian centre that serves a mixed urban and rural Canadian population. Methods: A cross-sectional analysis was conducted on 5690 telemedicine encounters from March 2020 to March 2022 at a tertiary-care gastroenterology clinic in Winnipeg, Manitoba, for a single gastroenterologist. Carbon emissions related to travel from home to clinic were estimated. The values are presented as CO2e, a standardized measure used to compare and aggregate the impact of different greenhouse gases on global warming. Travel distances were estimated using driving routes or flights for non-drivable locations. Clinic operational emissions were also estimated to assess total potential savings. Results: The total potential travel distance avoided was 880 336 km. Rural patients accounted for 92.7% of this distance. The average CO2e emissions saved per encounter was 42.9 kg, with rural encounters averaging 106.7 kg and urban encounters 4.6 kg. Clinic operational emissions were minimal at 0.06 kg of CO2e per encounter, compared to travel-related emissions. Over the 2 years, telemedicine visits saved approximately 244 079 kg of CO2e, underscoring the significant environmental benefit of virtual care. Conclusion: Telemedicine reduces the carbon footprint of gastroenterology outpatient care by minimizing patient travel, especially for rural populations. Incorporating telemedicine into routine practice can promote environmental sustainability within healthcare systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".