Telemedicine: A Path to Greener Healthcare?
Bibliographic record
Abstract
Telemedicine is an emerging tool for overcoming healthcare access barriers, particularly for vulnerable populations in rural and underserved communities. More recently, it has also been investigated as a way to reduce the healthcare sector’s carbon footprint. Evidence suggests that by minimizing travel for in-person visits and reducing resource use, telemedicine can help lower greenhouse gas emissions and contribute to both population and planetary health. A systematic review concluded that telemedicine reduces healthcare’s carbon footprint, suggesting its potential role in achieving a net-zero healthcare system. However, delivering on the promise of a greener healthcare system is complex, as it depends on numerous interrelated factors. While telemedicine has the potential to reduce carbon emissions by decreasing patient travel and optimizing resource use, its actual impact depends on multiple factors, such as the mode of transportation replaced (e.g., public vs. private transit), the energy consumption of powering digital infrastructure such as data servers, and the types of clinical encounters for which telehealth is accessible. For instance, telemedicine may be widely used for prescription renewals or follow-up visits but less applicable for acute or complex care—meaning that if patients must still travel for certain inquiries, the overall emissions reduction may be smaller than anticipated. While this paper acknowledges telemedicine’s potential to improve care access, its primary focus is on evaluating its environmental implications. This paper will analyze the advantages and limitations of telemedicine in reducing greenhouse gas emissions and examine feasibility and potential unintended environmental consequences.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".