Impact of remuneration and organizational factors on completing preventive manoeuvres in primary care practices
Notice bibliographique
Résumé
BACKGROUND: Several jurisdictions attempting to reform primary care have focused on changes in physician remuneration. The goals of this study were to compare the delivery of preventive services by practices in four primary care funding models and to identify organizational factors associated with superior preventive care. METHODS: In a cross-sectional study, we included 137 primary care practices in the province of Ontario (35 fee-for-service practices, 35 with salaried physicians [community health centres], 35 practices in the new capitation model [family health networks] and 32 practices in the established capitation model [health services organizations]). We surveyed 288 family physicians. We reviewed 4108 randomly selected patient charts and assigned prevention scores based on the proportion of eligible preventive manoeuvres delivered for each patient. RESULTS: A total of 3284 patients were eligible for at least one of six preventive manoeuvres. After adjusting for patient profile and contextual factors, we found that, compared with prevention scores in practices in the new capitation model, scores were significantly lower in fee-for-service practices (β estimate for effect on prevention score = -6.3, 95% confidence interval [CI] -11.9 to -0.6) and practices in the established capitation model (β = -9.1, 95% CI -14.9 to -3.3) but not for those with salaried remuneration (β = -0.8, 95% CI -6.5 to 4.8). After accounting for physician characteristics and organizational structure, the type of funding model was no longer a statistically significant factor. Compared with reference practices, those with at least one female family physician (β = 8.0, 95% CI 4.2 to 11.8), a panel size of fewer than 1600 patients per full-time equivalent family physician (β = 6.8, 95% CI 3.1 to 10.6) and an electronic reminder system (β = 4.6, 95% CI 0.4 to 8.7) had superior prevention scores. The effect of these three factors was largely but not always consistent across the funding models; it was largely consistent across the preventive manoeuvres. INTERPRETATION: No funding model was clearly associated with superior preventive care. Factors related to physician characteristics and practice structure were stronger predictors of performance. Practices with one or more female physicians, a smaller patient load and an electronic reminder system had superior prevention scores. Our findings raise questions about reform initiatives aimed at increasing patient numbers, but they support the adoption of information technology.
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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 | 0,001 | 0,003 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».