34 Engaging family medicine residents to consider sustainable prescribing practices for diagnosing and treating asthma
Bibliographic record
Abstract
In primary care, one of the largest sources of greenhouse gases are from pressurized metered-dose inhalers (pMDIs). pMDIs are an effective delivery device for respiratory illnesses, however they contain HFA, a powerful greenhouse to propel medication into a patient’s lungs Furthermore, about a third of patients prescribed inhalers do not have an objective diagnosis of asthma or COPD. Choosing Wisely Canada recommends only continuing treatment with objective confirmation of asthma or COPD. Additionally, the Canadian Thoracic Society updated national guidelines for the management of very mild and mild asthma; this includes initiation of a combination dry powder inhaler (formoterol/budesonide) instead of HFA-containing short-acting beta agonist therapy. Given the under-testing for asthma diagnosis and historical prescription of HFA-containing puffers as first-line therapy, we suspect a large burden of inappropriate, suboptimal asthma care in Canada. Our goal is to reduce pMDI prescriptions at the Queen’s Family Health Team (QFHT) by 50% by June 2023. We re-designed the Quality Improvement and Patient Safety (QIPS) curriculum for our first year post graduate family medicine residents to tackle inappropriate inhaler prescriptions and switching appropriate prescriptions to more environmentally friendly options. Residents received training focused on healthcare sector’s impact on the environment and QIPS. These future family physicians then worked in clinical teams and competed to see which team had the largest overall decrease in pMDIs. Each resident team had autonomy to decide how they approach the problem (e.g., switch inhaler device, check for diagnostic lung function testing and deprescribe where possible). Each month, metrics on pMDI prescriptions were shared with the entire health team. At the start of this project, we found that 58% of all inhaler prescriptions within our health team were pMDIs, and eight months into the project, we observe asignficant reduction in pMDI prescriptions, and a corresponding increase in DPI prescriptions. Total carbon deprescribing will be calculated. The project completes in June 2023.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".