Optimisation du parcours du médicament à la suite de l’implantation du nouveau logiciel informatique GESPHARx8
Bibliographic record
Abstract
Objectif : Optimiser le circuit du médicament, de la numérisation des ordonnances jusqu’à l’envoi aux unités de soins de l’Hôpital régional de Rimouski. Description de la problématique : L’implantation d’un nouveau système d’information en pharmacie nécessite un changement des pratiques pouvant augmenter les délais de traitement des ordonnances. L’évaluation du circuit du médicament apparaît essentielle afin de cibler les éléments limitants et d’améliorer les processus de travail. Résolution de la problématique : Une collecte de données prospective consistait à noter l’heure d’envoi des médicaments par pneumatique. Par la suite, le délai moyen entre la numérisation des ordonnances et l’envoi des médicaments a été calculé rétrospectivement. Les pharmaciens et les assistants techniques en pharmacie ont été sondés afin de cibler les principaux facteurs limitants à la distribution. Enfin, les pharmaciens ont colligé les erreurs à la saisie des ordonnances. Conclusion : Le projet a révélé certaines étapes limitantes dans le circuit. Plusieurs solutions ont été suggérées pour optimiser l’efficience du circuit, dont la conscientisation du personnel infirmier et de la pharmacie sur les délais et la mise en place d’audits systématiques d’évaluation au long terme. Summary Objective: Optimize the medication circuit, from prescription scanning to delivery of medication to care units, at Hôpital régional de Rimouski. Problem description: Implementing a new pharmacy information system requires a change in practices that may increase prescription processing times. An assessment of the medication circuit is essential to identify limiting factors and improve work processes. Problem resolution: Prospective data collection consisted of recording the time at which medications were sent by pneumatic tube. Subsequently, the average time between prescription scanning and medication delivery was calculated retrospectively. Pharmacists and pharmacy technical assistants were surveyed to identify the main factors affecting their dispensing activities. Finally, pharmacists compiled errors associated with prescription entry. Conclusion: The project revealed some limiting steps in the medication circuit. Many solutions were suggested to optimize the efficiency of the circuit, including raising awareness of prescription processing times among nursing and pharmacy staff as well as implementing long term systematic assessment audits.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".