Universalising Healthcare in India: Managing the Provider–Purchaser Split
Bibliographic record
Abstract
Several countries with diverse health systems have achieved universalization (UHC). The trajectory towards universal coverage almost always has three typical features: (i) a political process driven by a range of regulatory changes to simplify access; (ii) an increase in health spending; (iii) an increase in the share of pooled spending rather than paid out-of-pocket. Therefore, a study was undertaken to understand the extent of the provider-purchaser relationship of governments to achieve UHC while reforming healthcare. The present paper focuses on extensive secondary research across countries and evaluates the experiences of select developed and developing economies with India’s experiments on- Financing mechanisms, management arrangements, governance and health outcomes; to offer a comparison of practices and their impact. While Italy, the UK, Germany, Australia, Japan, Canada and most recently China are countries that have achieved UHC; countries like USA and Brazil are on the verge of achieving UHC. These nine countries represent the entire spectrum of pure purchasing models, mixed and pure provisioning models to help us leverage from their experience. All countries that have attained UHC have a well-defined package of services that the government commits to fund and provide for (both public and private). Additionalities around wellness and cosmetic care is managed through supplementary insurance. Overall funding is through an autonomous body, at arm’s length of government; primarily to govern and manage the state’s health priorities. And the government purely behaves as a regulator setting policy and giving directions to the providers. However, ensuring the sustenance of such a mixed model requires; (i) a well-regulated ecosystem that thrives on evidence, (ii) the governments must clearly define the role/s of each stakeholder and hold them accountable for their deliverables in attaining UHC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".