Costs of health care across primary care models in Ontario
Notice bibliographique
Résumé
BACKGROUND: The purpose of this study is to analyze the relationship between newly introduced primary care models in Ontario, Canada, and patients' primary care and total health care costs. A specific focus is on the payment mechanisms for primary care physicians, i.e. fee-for-service (FFS), enhanced-FFS, and blended capitation, and whether providers practiced as part of a multidisciplinary team. METHODS: Utilization data for a one year period was measured using administrative databases for a 10% sample selected at random from the Ontario adult population. Primary care and total health care costs were calculated at the individual level and included costs from physician services, hospital visits and admissions, long term care, drugs, home care, lab tests, and visits to non-medical health care providers. Generalized linear model regressions were conducted to assess the differences in costs between primary care models. RESULTS: Patients not enrolled with a primary care physicians were younger, more likely to be males and of lower socio-economic status. Patients in blended capitation models were healthier and wealthier than FFS and enhanced-FFS patients. Primary care and total health care costs were significantly different across Ontario primary care models. Using the traditional FFS as the reference, we found that patients in the enhanced-FFS models had the lowest total health care costs, and also the lowest primary care costs. Patients in the blended capitation models had higher primary care costs but lower total health care costs. Patients that were in multidisciplinary teams (FHT), where physicians are also paid on a blended capitation basis, had higher total health care costs than non-FHT patients but still lower than the FFS reference group. Primary care and total health care costs increased with patients' age, morbidity, and lower income quintile across all primary care payment types. CONCLUSIONS: The new primary care models were associated with lower total health care costs for patients compared to the traditional FFS model, despite higher primary care costs in some models.
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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 | 0,008 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,007 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».