Performance Management in Long-Term Care With Composite Measures of Quality
Notice bibliographique
Résumé
Context: There is considerable interest in assessing quality of care in health care settings for performance management, improvement, accountability and recently also payment purposes. While individual providers and patients may want and need detailed indicators of quality performance specific to their needs, purchasers and regulatory bodies require aggregate or composite measures of quality that serve as robust signals of quality. Indicators must also be responsive to differences and changes in provider performance. Objectives: The objective of this paper is to evaluate (compare and contrast) hospital quality performance based on aggregations of quality indicators that are individually publicly reported for Long-Term Care (LTC) Hospitals in Ontario, Canada. Methods: We employed a retrospective facility-level cohort study. The cohort comprises 113 LTC hospitals from 1997 through 2005. We employed 12 valid and reliable risk-adjusted LTC quality indicators for this study representing both process and outcome quality measures. Adjusting for varying facility-level sample sizes, 95% binominal control limits were used to statistically identify significant differences in individual facility performance from provincial averages across each quality indicator. Aggregate measures were calculated using varying weights that adjusted for variability, reliability, correlation, and the health impact of different quality indicators. Observations: The prevalence of quality concerns ranged from 4% of residents (falls and worsening locomotion) to 31% (unregulated pain and worsening bladder continence). Most larger facilities tended to have more positive performance compared to moderate-sized and smaller facilities. Small sample sizes introduced considerable uncertainty in individual quality measures that was mitigated somewhat by aggregating individual quality measures. Aggregate indices were better able to mitigate small sample size concerns. While providing similar results, some variation in specific facilities identified as significantly higher and lower did exist across different weighting. Weighted composite indicators were able to identify LTC hospitals with particularly poor performance indicated by constituent quality indicators. Conclusions: An aggregate index of performance may be useful as an indicator of potentially underlying concerns in particular facilities. Transparent weighting mechanisms are useful to identify and emphasize priority areas for improvement. Priority weighting could be used to target pay-for-performance or other accountability initiatives. Individual facilities aiming to make improvements may still need specific information on individual indicators.
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 | 0,037 | 0,104 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,007 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».