Hospital Performance, the Local Economy, and the Local Workforce: Findings from a US National Longitudinal Study
Notice bibliographique
Résumé
BACKGROUND: Pay-for-performance is an increasingly popular approach to improving health care quality, and the US government will soon implement pay-for-performance in hospitals nationwide. Yet hospital capacity to perform (and improve performance) likely depends on local resources. In this study, we quantify the association between hospital performance and local economic and human resources, and describe possible implications of pay-for-performance for socioeconomic equity. METHODS AND FINDINGS: We applied county-level measures of local economic and workforce resources to a national sample of US hospitals (n = 2,705), during the period 2004-2007. We analyzed performance for two common cardiac conditions (acute myocardial infarction [AMI] and heart failure [HF]), using process-of-care measures from the Hospital Quality Alliance [HQA], and isolated temporal trends and the contributions of individual resource dimensions on performance, using multivariable mixed models. Performance scores were translated into net scores for hospitals using the Performance Assessment Model, which has been suggested as a basis for reimbursement under Medicare's "Value-Based Purchasing" program. Our analyses showed that hospital performance is substantially associated with local economic and workforce resources. For example, for HF in 2004, hospitals located in counties with longstanding poverty had mean HQA composite scores of 73.0, compared with a mean of 84.1 for hospitals in counties without longstanding poverty (p<0.001). Hospitals located in counties in the lowest quartile with respect to college graduates in the workforce had mean HQA composite scores of 76.7, compared with a mean of 86.2 for hospitals in the highest quartile (p<0.001). Performance on AMI measures showed similar patterns. Performance improved generally over the study period. Nevertheless, by 2007--4 years after public reporting began--hospitals in locationally disadvantaged areas still lagged behind their locationally advantaged counterparts. This lag translated into substantially lower net scores under the Performance Assessment Model for hospital reimbursement. CONCLUSIONS: Hospital performance on clinical process measures is associated with the quantity and quality of local economic and human resources. Medicare's hospital pay-for-performance program may exacerbate inequalities across regions, if implemented as currently proposed. Policymakers in the US and beyond may need to take into consideration the balance between greater efficiency through pay-for-performance and socioeconomic equity.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 tête enseignante, 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 ».