4CPS-346 Evaluation of the implementation of ‘inhaler interviews’ during medication reconciliation in the pneumology service
Bibliographic record
Abstract
Background and importance At the request of the pneumology specialists, we managed to set up medication reconciliation in the service. Taking advantage of this new activity, we proposed to evaluate patients’ ability to use their inhalers. Aim and objectives The objectives were to promote the correct use of inhalation devices and to ensure proper patient management. Material and methods An initial work consisted of making an inventory of the inhalation devices. The Zéphir guide, a video tutorial on the use of inhalers, set up by the Société de Pneumologie de Langue Française (SPLF), enabled us to acquire the right gestures. In collaboration with the pneumologists, we determined the eligibility criteria for medication reconciliation by prioritising patients with COPD or asthma. During the intake interview, the RQESR 2019 (Quebec Respiratory Health Education Network) checklist for the use of inhalation devices allowed us to evaluate the patient‘s control of aerosol use. Interviews were carried out by the pharmacy intern. Results In 2.5 months, we assessed 65 patients with an average age of 65.6 years. 49.2% of the patients had more than one inhaler at home. The average length of the patient interviews was 12.4 min. The shortest interview needed for mastering device use lasted 5 min whereas the longest, when extensive training was required, lasted 25 minutes. In 85% of patients, device use was compliant. Training was therefore offered to 15% of patients using a demonstration kit which was traced in the patient file. The positive points of this new activity were the multidisciplinary nature of the work carried out by healthcare professionals to help ensure the proper use of drugs, and detection and correction of device misuse. The limitations encountered were the difficulty in obtaining the devices and time required to receive them. Conclusion and relevance Implementation of this activity has been gradual (training, development of medication reconciliation, research into new monitoring indicators). This work has also made it possible to carry out a more indepth reflection, within the medical and pharmaceutical teams, with a view to optimising the range of inhalers and proposing user friendly devices or those not requiring hand–lung coordination. References and/or acknowledgements Conflict of interest No conflict of interest
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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 | 0.050 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".