Health technology assessment and its role in the future development of the Indian healthcare sector
Bibliographic record
Abstract
Public expenditure on healthcare in India is low by international comparison, and access to essential treatment pushes many uninsured citizens below the poverty line. In many countries, policymakers utilize health technology assessment (HTA) methodologies to direct investments in healthcare, to obtain the maximum benefit for the population as a whole. With rising incomes and a commitment from the Government of India to increase the proportion of gross domestic product spent on health, this is an opportune moment to consider how HTA might help to allocate healthcare spending in India, in an equitable and efficient manner. Despite the predominance of out-of-pocket payments in the Indian healthcare sector, payers of all types are increasingly demanding value for money from expenditure on healthcare. In this review we demonstrate how HTA can be used to inform several aspects of healthcare provision. Areas in which HTA could be applied in the Indian context include, drug pricing, development of clinical practice guidelines, and prioritizing interventions that represent the greatest value within a limited budget. To illustrate the potential benefits of using the HTA approach, we present an example from a mature HTA market (Canada) that demonstrates how a new treatment for patients with atrial fibrillation - although more expensive than the current standard of care - improves clinical outcomes and represents a cost-effective use of public health resources. If aligned with the prevailing cultural and ethical considerations, and with the necessary investment in expert staff and resources, HTA promises to be a valuable tool for development of the Indian healthcare sector.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".