Reducing healthcare waste by eliminating exam table paper in a primary care practice: a sustainable quality improvement initiative
Bibliographic record
Abstract
Purpose Climate change is now the greatest threat to human survival. The healthcare system contributes significantly to global pollution and greenhouse gas emissions. Individual practitioners play an important role in helping to reduce these impacts in day-to-day practice. Deimplementation of unnecessary processes and products, such as exam table paper, in medical offices is one simple approach to incorporating principles of planetary health into practice. All quality improvement (QI) projects must start to consider environmental impacts to fully evaluate change ideas. Methods We designed a single Plan-Do-Study-Act cycle using the Institute for Health Improvement Model for Improvement. We removed the exam table paper from our primary care office and measured changes in staff time, laundry, financial costs, paper use and carbon dioxide (CO2) emissions. Results Eliminating exam table paper in our clinic resulted in modest annual cost savings of $C718 and improved staff efficiency and motivation to introduce other green office practices. In our clinic alone, this change will save 8.2 km of exam table paper, 10 trees and 148 kg of CO2e (equivalent to driving 1233 km) every year. There were no negative consequences or feedback. Conclusions This simple QI project demonstrates the feasibility of implementing a small change in a primary care clinic that can improve environmental sustainability with multiple co-benefits. If all family physicians in Canada eliminated exam table paper in their offices, it would result in savings of approximately 95 940 km of paper, 121 680 trees, $C8 400 600 and 3054 T CO2 emissions, equivalent to driving around the world 360 times.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".