Problèmes et erreurs reliés à la prescription détectés par les pharmaciens de l'Hôpital Laval : description, interventions et impact
Bibliographic record
Abstract
Resume Objectifs : Decrire le phenomene des problemes et erreurs relies a la prescription des medicaments a l’Hopital Laval ainsi que le role du pharmacien dans leur detection et leur prevention. Methodologie : Durant quatre semaines, les pharmaciens de l’Hopital Laval ont repertorie les ordonnances problematiques acheminees a la pharmacie centrale. Resultats : Durant la periode a l’etude, 30 009 ordonnances ont ete traitees a la pharmacie centrale, dont 740 se sont averees problematiques. Dans 60,4 % des cas, une imprecision est a l’origine du probleme de prescription. Que ce soit pour clarifier une ordonnance ou pour la faire modifier, les pharmaciens referent peu a leurs collegues pharmaciens, preferant contacter l’infirmiere du patient. Les interventions effectuees par les pharmaciens sont acceptees dans 88,0 % des cas. Finalement, les activites cliniques d’un pharmacien a l’unite de soins apparaissent avoir un impact positif sur la prevalence des problemes relies a la prescription de medicaments. Conclusion : Le pharmacien joue un role important dans la detection et la prevention des problemes et erreurs relies a la prescription des medicaments. Abstract Objective: To describe the problems and errors related to the prescription of medication at the Hopital Laval, as well as the role of the pharmacist in their detection and prevention. Methods: For four weeks, the Hopital Laval pharmacists tracked problematic prescriptions that were sent to the hospital’s main pharmacy. Results: During the study period, 740 of the 30,009 prescriptions evaluated at the main pharmacy were deemed problematic. In 60.4% of cases, imprecision was at the root of the problem. Whether to clarify a prescription or to have a prescription modified, pharmacists preferred to contact the patient’s nurse as opposed to referring to colleagues. The interventions done by pharmacists were accepted in 88% of cases. Finally, the work of the clinical pharmacist on the ward appears to have had a positive impact on the prevalence of problems related to the prescription of drugs. Conclusion: The pharmacist plays an important role in the detection and prevention of problems and errors related to the prescription of drugs. Key Words: error, problem, prescription, medication, pharmacist, intervention, impact.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".