Exploration et modèle d’analyse de ratios de coûts de médicaments par indicateurs de volumes d’activités en établissement de santé
Bibliographic record
Abstract
Resume Les couts des medicaments utilises en etablissement de sante augmentent plus rapidement que la plupart des autres postes de depenses du reseau de la sante. Les systemes d’information utilises pour recueillir les couts de medicaments en etablissements de sante sont peu interfaces, souvent limites et developpes d’abord dans une perspective clinique plutot qu’economique. Nous decrivons une demarche visant a integrer des donnees provenant du systeme de gestion du dossierpatient par les archives, du systeme de gestion des approvisionnements et du systeme de gestion du dossier pharmacologique afin d’en tirer des applications pratiques. Nous explorons le profil des couts de medicaments pour ces clienteles en identifiant des ratios susceptibles de mieux nous renseigner sur les couts et de faciliter la planification et les comparaisons. Il existe peu de donnees publiees sur les couts de medicaments en etablissements de sante. De plus, il n’existe pas d’unites de mesure largement etudiees pour favoriser des comparaisons internes et externes valables. Le modele propose permet d’integrer des donnees de differents systemes d’information et de favoriser la recherche de ratios utiles a la planification et aux comparaisons. Ainsi, une meilleure connaissance de ces donnees peut contribuer a planifier des ressources materielles et humaines, de meme qu’une hierarchisation plus structuree des soins pharmaceutiques. Abstract The costs of medication used in health care institutions are growing more rapidly than those of most other expenditures in the health care system. Information systems used to capture medication costs in health institutions were developed from a clinical, rather than an economic, perspective. As a result, their capacity is often limited and their data poorly coordinated. The present article discusses a procedure for integrating data from systems that manage supplies, medication and the archival storage of patient records for the purpose of offering certain practical applications. We explore the medication cost profile for these clienteles while identifying the ratios most likely to inform us about costs and therefore facilitate planning and comparisons. Few studies have been published on the costs of medication in health care institutions. What’s more, an extensive study of measuring units to encourage valid internal and external comparisons has never been done. The proposed model makes it possible to integrate data from various information systems and to encourage the search for ratios that can be used in planning and making comparisons. We believe that a better knowledge of these data may aid in the planning of material and human resources and lead to a more structured hierarchical organization of drug services.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".