An initiative to improve asthma management in an academic group family medicine practice: optimizing guideline adherence and advancing environmental stewardship
Bibliographic record
Abstract
Abstract Background Asthma is a common condition encountered in family medicine clinics. Over the past decade, the guidelines for the treatment of asthma have undergone extensive revision and modification. Salbutamol inhalers are still regularly prescribed despite evidence that they worsen outcomes and are not recommended according to the most recent global initiative for asthma (GINA) guidelines in individuals over 12 years of age. Additionally, pressurized metred dose inhalers (pMDIs) significantly impact the carbon footprint of asthma care. The climate crisis is the most important health crisis of our generation. Several alternatives to pMDIs are available and recommended by GINA. Local problem At our clinic, several patients are on suboptimal asthma therapy for a variety of reasons. Several of our patients are also prescribed pMDIs when alternatives are available. This can result in both poorer health outcomes and a large carbon footprint. Methods We identified all patients over the age of 12, who were not on asthma therapy consistent with 2024 GINA guidelines. These patients were sent a standardized text message informing them of the benefits of changing their therapy and offering a consultation. Intervention For patients who accepted the consultation, we explained the benefits and risks of changing their medication regimen, provided information about environmental impact when applicable, and changed prescriptions for those who consented. Results Fifty-three patients were identified as suitable for intervention. Fifty-three patients were sent a text message regarding the potential for alternative inhaler therapy. Eleven patients agreed to switching after receiving a text message, 17 agreed following a follow-up phone call, 23 patients did not respond, and 2 declined switching therapy. Of eligible patients, 53% had their inhaler therapy optimized. Our pMDI prescription went from 34% to 19%, representing a notable reduction in the environmental impact of asthma management in our practice. Conclusions Through this quality improvement initiative, we were able to optimize the asthma therapy of patients in our practice and reduce the carbon footprint of prescription by reducing the number of pMDI inhalers prescribed and the overall number of inhalers prescribed.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".