What stops private hospitals from engaging with publicly funded health insurance schemes? A mixed-methods study on PMJAY/MJPJAY in Maharashtra, India
Notice bibliographique
Résumé
BACKGROUND: Reducing patient expenditure and expanding healthcare access through private sector hospitals is widely touted strategy for governments to achieve Universal Health Care, including in India. However, private sector engagement in India's publicly funded health insurance schemes (PFHIS) remains low and is uneven across geographies and by hospitals size. This paper examines challenges to achieving effective private sector engagement in PFHIS by analysing private sector participation and exploring diverse stakeholder perspectives. METHODS: This case study used sequential mixed methods design and was conducted in 2023-24 in Maharashtra, India. We combined quantitative analysis of the geographic distribution of empanelled private hospitals (993 across Maharashtra's 36 districts) and qualitative interviews (n = 16) with diverse stakeholders to understand why some facilities do not engage. The analysis was guided by our framework on private sector engagement that examined policy factors, hospital level factors and operational factors. RESULTS: Only 13% of private hospitals were empanelled in Maharashtra's PFHIS, with higher empanelment in urban areas and among small and medium sized hospitals; rural areas had few empanelled hospitals and few large private hospitals participated. Districts with few empanelled private hospitals had lower overall hospitalization rates, suggesting persistent unmet population need for affordable hospitals. Low private sector engagement was driven by multiple factors: at the policy level, insufficient state budgets, low reimbursement rates, fixed scheme packages, strict empanelment criteria, complex claims processes, and delayed reimbursements; at the hospital level, economic non-viability, concerns about patient load and profile, and limited administrative capacities; and at the operational level, inadequate monitoring mechanisms for PFHIS and empanelled hospitals, gaps in the empanelment process, and delays in patient pre-authorization and claims processing. CONCLUSION: This study enhances understanding of private sector engagement challenges and provides insights for improving PFHIS and UHC in India. The framework developed can also be applied beyond India to assess the complexities of intent, capacity, and interactions between private and public actors in PFHIS. To create an enabling environment for private sector engagement and achieve the scheme's objectives, the state could increase reimbursement rates, implement responsive grievance redressal, regulate private hospitals, and improve governance processes. A two-fold strategy of strengthening the public health system and engaging with regulated private hospitals could enhance the scheme's effectiveness.
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 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 | 0,020 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».