The cream-skimming behaviors of tertiary hospitals under medical alliances: evidence from China
Notice bibliographique
Résumé
BACKGROUND: While previous studies have delved into the formation and development of medical alliances in China, there has been limited focus on investigating inequity in the referral rates and the quality of care received provided to patients with experience of referral and those without under the introduction of medical alliances. This study explored: (1) inequity in the odds of being referred to healthcare institutions within medical alliances; and (2) inequity in the quality of care received between the referred and non-referred patients. METHODS: This study employed a dataset comprising 440,950 individuals who had at least one outpatient visit at healthcare facilities in Hangzhou city, Zhejiang province, China from January 1, 2020 to September 24, 2021. Quality of outpatient care was measured by the odds of having seven-day all-cause follow-up encounters to any healthcare institution. Binary regression models combined with random effects were constructed to examine inequity in the referral rates and the quality of care received. A set of sensitivity analyses were conducted to check the robustness of study findings. RESULTS: This study has three key findings. First, outpatients' insurance status, rather than their specific diseases and health conditions, was identified the most significant determinant driving healthcare institutions' referral decisions. Compared with outpatients covered by public health insurance programs, those without such coverage were more likely to be referred by tertiary hospitals to primary care facilities (coefficient = 1.33; 95% CI: 0.56-2.11) while being less likely to be referred by primary care facilities to tertiary hospitals (coefficient = -2.00; 95% CI: -3.08 - -0.92). Second, the referred outpatients received poorer quality of care, as indicated by higher odds of having all-cause follow-up encounters within seven days at any healthcare institution, compared to those non-referred outpatients. Third, outpatients with chronic diseases and public health insurance coverage not only experienced higher referral rates but poorer quality of outpatient care after being referred from tertiary hospitals to primary care facilities, compared to their counterparts. CONCLUSION: This study demonstrated that tertiary hospitals "siphoned-off" outpatients with public health insurance coverage from primary care facilities. Outpatients who were older, were male, with chronic diseases, and with public health insurance coverage were more likely to experience not only higher referral rates but poorer quality of outpatient care after being referred from tertiary hospitals to primary care facilities. Tailored policies are required to protect and compensate the most vulnerable population groups.
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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 | 0,003 | 0,001 |
| 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 | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».