What stops private hospitals from engaging with publicly funded health insurance schemes? A mixed-methods study on PMJAY/MJPJAY in Maharashtra, India
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".