Efficiency-quality trade-off in allocating resource to public healthcare systems
Bibliographic record
Abstract
In practice, both comprehensive hospital (e.g. AAA hospital) and primary hospital (e.g. community hospital) can exist in healthcare system, where the comprehensive hospital can provide a guaranteed service, but the waiting time of patients is relatively long. By contrast, the primary hospital is less congested, but the patients cannot be treated if the illness is found to be severe. Then the trade-off between efficiency (primary hospital) and quality (comprehensive hospital) should be considered. In this paper, we consider a resource allocation problem in a public service system with multi-type service providers and patients. To capture the interactions between the multi-type hospitals, a queueing-game-theoretical model is established. And we obtain the following results. First, the socially optimal reimbursement policy is obtained, and the sensitivities of parameters are examined, which indicate that, somewhat interestingly, both the optimal budget to primary hospital and the maximal social welfare are non-monotone in the effectiveness parameter or the joining probability of patients. Second, by comparing the socially optimal strategy with individually equilibrium strategy, we find that individual behaviour of patients does not necessarily lead to systems more congested than what is socially desirable. Third, we demonstrate the robustness of our model by extending it to a three-level system and a system with multiple parallel primary hospitals.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".