Doit-on s’inspirer de la tarification à l’activité pour le financement des médicaments onéreux en établissements de santé ?
Bibliographic record
Abstract
Au Canada, entre 1985 a 2005, l’ensemble des depenses de sante a augmente en moyenne de 6,5 % par annee comparativement a 9,5 % pour les frais de medicaments. La part des medicaments au sein des depenses totales de sante etait estimee a pres de 17% en 2007 pour un total de pres de 30 milliards de dollars, ce qui la place au second rang des depenses apres les frais lies aux hopitaux. De plus, les depenses en medicaments que recense l’Institut canadien d’information sur la sante n’incluent pas les depenses en medicaments en hopital, qui sont incluses dans le cout des hopitaux. Le rapport 2007-2008 sur les pharmacies hospitalieres canadiennes confirme cette croissance des couts en medicaments. Si les depenses en medicaments continuent de croitre a un rythme plus eleve que le reste des composantes du systeme de sante, une reflexion portant sur des approches permettant de favoriser une utilisation optimale des medicaments en etablissement de sante s’impose. Cette derniere repose notamment sur une selection appropriee des medicaments et sur le respect des regles d’utilisation et de remboursement etablies par les decideurs, qui tiennent compte de donnees probantes, de l’etat du patient et du rapport entre les couts et les avantages. L’objectif de cet article est de presenter le modele francais de tarification a l’activite (T2A) et de gestion des medicaments onereux de maniere a susciter la reflexion quant a son applicabilite dans le contexte canadien de la gestion des medicaments en etablissements de sante.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".