A110 SUSTAINABILITY IN EVERYDAY GI PRACTICE: QUANTIFYING TRAVEL RELATED CARBON EMISSIONS FOR IN-PERSON APPOINTMENTS AND MODELS FOR REDUCTION THROUGH USE OF TELEHEALTH
Bibliographic record
Abstract
Abstract Background The health care industry alone contributes to approximately 5% of annual carbon emissions and gastroenterology represent a more resource intensive subspecialty so much so that multiple societies have begun initiatives with aims to reduce the environmental impact of gastroenterology practice. After COVID-19 there was a large adoption of telehealth across many disciplines including gastroenterology. This had the benefit of reducing viral transmission but also significantly reduced travel related emissions for routine appointments. Since that time there has been a varied continuation of virtual care. Aims We aimed to quantify the average carbon emissions associated with travel to non-endscopic gastroenterology appointments. We also aimed to develop models for emission reductions based on possible changes to practice (e.g. conversion of follow-up appointments to telehealth). Methods Over a 2-week period, we conducted a cross-sectional analysis evaluating carbon emissions associated with travel to gastroenterology appointments. The average number of appointments per day was determined and by using postal codes we were able to estimate travel distances for patients. Carbon emissions were based on these travel distances using standard estimates (including average emissions by car, percentage of patients using alternate transportation, non-tail pipe emissions) and we assessed various estimated of emissions related to telehealth. We then used variable practice models to determine the potential emissions reductions. Results We assessed 975 appointments, of which 71 were excluded (e.g. insufficient data, non-physician appointments), leaving 904 included appointments of which 75% were follow-up (678) and the remained were new consultations (226). Sixteen different gastroenterologists had an average of 22.7 patients per day. The mean return distance travelled per appointment was 57.3 km which translates to 14.9 kg CO2 per patient visit. An average day at in clinic would then equate to 337.3 kg CO2 per day, equivalent to 146.6 L gasoline or 15.5 trees’ annual carbon capture. By converting only appointments with a return distance over 100 km or follow-up appointments, we found that a 77% emissions reduction could be achieved. Conclusions There can be significant emissions savings with conversion of in-person visits to telehealth while still allowing for some visits to be in-person. Given the unique nature of gastroenterology requiring in-person visits for endoscopy, this may serve as one mechanism by which the collective group of Canadian gastroenterologists can substantilally reduce carbon emissions related to their practice. Impact on carbon emissions from conversion of in-person appointments to telehealth for gastroenterology Funding Agencies None
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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 | 0.002 | 0.003 |
| 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".