Optimiser la prescription d'antimicrobiens : une solution informatisée
Bibliographic record
Abstract
Resume Objectif : L’objectif de cet article consiste a presenter une intervention d’optimisation de l’usage des antimicrobiens articulee autour de l’utilisation d’un systeme expert. Mise en contexte : Au Centre hospitalier universitaire de Sherbrooke, une equipe travaille depuis 2005 a la creation d’un systeme d’aide a la decision pour cibler les prescriptions d’antimicrobiens sous-optimales. Resultats : Apres 53 semaines d’utilisation, 1344 interventions ont ete effectuees, dont 1222 (90,9 %) ont ete acceptees par les differentes equipes medicales. Ceci represente une moyenne de 1,7 interventions acceptees/heure de presence de la pharmacienne. Pour la periode decrite, la consommation totale en antimicrobiens a diminue de 13,5 %, ce qui represente une economie de 305 000 $ (15 %) sur les antimicrobiens du departement de pharmacie. Ceci constitue une economie de 405 $/heure de presence de la pharmacienne. Discussion : Les resultats observes s’expliquent par l’evaluation exhaustive de toutes les nouvelles prescriptions et de toutes les modifications subsequentes d’antimicrobiens par le logiciel. De plus, la surveillance hebdomadaire permet egalement de detecter des pratiques en emergence. Conclusion : La collaboration medicale, pharmaceutique et informatique ont permis de developper un outil adapte a la realite quebecoise afin d’optimiser l’utilisation des antimicrobiens et de diminuer les couts lies a l’antibiotherapie. Abstract Objective: The purpose of this article is to discuss an intervention optimizing the use of antimicrobials using experts in the field. Context: At the Centre hospitalier universitaire de Sherbrooke , a team has been working since 2005 to create a decisionmaking support system targeting sub-optimal prescriptions for antimicrobials. Results: After 53 weeks of use, 1344 interventions were made of which 1222 (90.0%) were accepted by the different medical teams. This represents, on average, 1.7% of interventions being accepted per hour of pharmacist presence. For this period, total antimicrobial consumption decreased by 13.5%, representing savings of 305 00$ (15%) with respect to the pharmacy department’s antimicrobial agents. This constitutes savings of 405$ per hour of pharmacist presence. Discussion: The observed results can be explained by the exhaustive review that was done of all new prescriptions and of all subsequent modifications to antimicrobial therapy by the software. In addition, emergent practice habits were detected through weekly surveillance. Conclusion: The collaboration between the medical, pharmacy and information technology fields allowed the development of a tool adapted to the Quebec reality to optimize antimicrobial use and to decrease costs related to antimicrobial therapy. Key words: Antimicrobials, pharmacist, software, surveillance
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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; both teacher heads agree on what is shown here.
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".