Utilisation des bilans comparatifs des médicaments en tant que prescription de départ
Bibliographic record
Abstract
Resume Objectif : Comparer la transmission de l’information engendree par l’utilisation des bilans comparatifs des medicaments au conge, comparativement aux ordonnances manuscrites et sommaires de depart et decrire la comprehension des pharmaciens communautaires. Mise en contexte : Puisque les donnees dans la litterature scientifique demontrant l’efficacite du bilan comparatif des medicaments au conge sont peu nombreuses, nous avons procede a l’evaluation de son implantation a l’Hopital de l’Enfant-Jesus du Centre hospitalier affilie universitaire de Quebec. Resultats : Les divergences observees entre la liste des medicaments actifs a domicile avant l’admission et l’ordonnance de depart etaient intentionnellement documentees dans 97,7 % des cas lors de l’utilisation des bilans comparatifs des medicaments au conge, comparativement a 86,1 % des cas lors de l’utilisation des ordonnances manuscrites et 78,8 % des cas dans les sommaires de depart. Les resultats du sondage realise aupres des pharmaciens communautaires revelent aussi que l’usage des lettres de transfert et des bilans comparatifs des medicaments au conge entraine un taux de satisfaction eleve parmi ces professionnels de la sante, puisqu’il leur fournit une meilleure comprehension de l’etat du patient. Discussion : Les resultats demontrent que le bilan comparatif des medicaments au conge permet de diminuer le nombre de divergences non documentees. Le sondage demontre une grande satisfaction de la part des pharmaciens communautaires, portant sur l’amelioration de l’information transmise par les bilans comparatifs des medicaments au conge du patient. Conclusion : L’implantation du bilan comparatif des medicaments au conge a permis d’ameliorer la communication entre les differents acteurs de la sante. Abstract Objective: To compare the transfer of information through use of medication reconciliation at discharge as compared to written prescriptions and discharge summaries, and to describe the comprehension of community pharmacists. Context: Given that few data exist in the scientific literature demonstrating the efficacy of medication reconciliation at discharge, we proceeded to evaluate its implementation at the Hopital de l’Enfant-Jesus of the Centre hospitalier affilie universitaire de Quebec . Results: The deviations observed between the list of active drugs at home prior to admission and the discharge prescription were intentionally noted in 97.7% of cases when medication reconciliation was used at discharge, as compared to 86.1% of cases when written prescriptions were used and to 78.8% of cases when discharge summaries were used. The survey done with community pharmacists also reveals that the use of a transfer letter and medication reconciliation at discharge lead to a higher rate of satisfaction among these healthcare professionals because these provide a better understanding of the state of the patient. Discussion: The results show that medication reconciliation at discharge decreases the number of reported deviations. The survey demonstrates greater satisfaction among community pharmacists because the transfer of information is improved through the use of medication reconciliation at a patient’s discharge. Conclusion: The implementation of medication reconciliation at discharge improved communication between the different players in healthcare.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".