A Funding Methodology With Adjustment for Hospital Quality Performance
Notice bibliographique
Résumé
Rationale: Improving hospital quality performance while improving outcomes and efficiency and effectiveness of a provincial hospital system has been paid high attention. It requires predictable, stable, and multi-year funding with accountability by provincial government. Modeling of multi-year-funding with quality performance adjustment is essential in this processing, required to reach equity, reflect hospital performance and resource utilization with high quality. Objective: To model the quality performance adjusted funding formula based on the original multi-year funding strategy for the hospitals in a province with adjustment for several major factors to reach equity based on the data in the past several years using the statistical models. Methodology: Two quality performance indicators, readmission rate and mortality rate, were chosen as major indicators, which affect the volume and unit cost. To assess the quality performance with equity, GLM (Generalized Linear Model) model was applied in standardizing the readmission rate and mortality rate adjusted for several factors and the ratio of real rate vs. expected rate were calculated. The volume (weighted cases) and unit cost (cost per weighted case) were adjusted for the standard ratios of readmission and mortality. Bayesian model were employed in the recalculation of the weighted cases and cost per weighted cases. The expected weighted cases for individual hospitals were calculated using GLM model with adjustment for regional socio-economic status, community population, age group and gender. The expected unit cost (cost per weighted case) for individual hospitals were calculated using composite weighted robust regression model. Results: The expected readmission rate and mortality rate were calculated by the probabilities of events under the certain conditions using GLM model with appropriate distribution. The adjusted weighted cases for each hospital were recalculated using Bayesian model. The multi-year unit cost formula with quality performance was established. The funding plan could be developed based on those results above to promote hospitals to improve their quality while improving efficiency and effectiveness. Discussions: It is necessary to reflect the quality performance in the multi-year funding methodology. It can reach equity better and encourage hospitals to improve their service quality, and real efficiency and effectiveness. It is also essential to recalculate the volume and unit cost with appropriate adjustment for readmission and death in order to make the funding plan more efficiently.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».