Evaluating Health System Efficiency using Data Envelopment Analysis: A case of Indian Healthcare System
Bibliographic record
Abstract
Purpose-With increased demand and restricted healthcare resources, it becomes important to take a step back and evaluate the efficiency of healthcare delivery. The present study aims to evaluate the health system efficiency of India by benchmarking it against its peers in BRICS countries and against OECD countries. Design/Methodology/Approach: The input and output variables required for measuring the efficiency of healthcare system were identified. A Data Envelopment Analysis (DEA) approach was used and efficiency frontier identified with the rankings of the BRICS and OECD countries. India is thus benchmarked against its peers (BRICS) and against OECD countries. Finding: India was found to operate at the efficiency frontier along with China, Russia, Brazil, and South Africa, however it ranked fourth. When benchmarked against OECD countries, India operates on the efficiency frontier along with Canada, Greece, Japan, Korea, Mexico, Spain, Sweden, Switzerland, Turkey, Great Britain, Chile and Israel. Countries like Germany, United States of America, Czech Republic, Slovakia and Lithuania operate at a lower healthcare efficiency and need to use their resources wisely. Practical/Research Implications: Developing countries like India can look to improve its healthcare system delivery by replicating best practices of healthcare systems from its peers and the top 10 OECD countries. Majority of the OECD countries in the top 10 have implemented universal health coverage, have higher physician and nurse density and higher hospital bed ratios. They are inclined towards branded drugs vis-à-vis generics and have follow evidence based medicine. From a theoretical perspective, it adds to the body of literature of DEA and health system efficiency. Originality/Value: This is a pioneer study that benchmarks India against its peers and against OECD countries drawing unique insights about healthcare efficiency
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".