Quality control mechanisms under capitation payment for medical services
Bibliographic record
Abstract
As a result of rising health care costs, many countries, including the United States, have turned to managed care organizations and the use of capitation payment systems. Although this type of system is an effective mechanism for reducing excessive utilization of health care, it may lead to the underprovision of medical services. In this paper propensity to underprovide medical services in a prepayment system as well as the effects of auditing/monitoring on physician behaviour and patient well‐being are examined. Conditions are found under which managed care yields more efficient outcomes than traditional fee‐for‐service care. Suite à la croissance importante des coûts des soins, plusieurs pays, y compris les Etats Unis, ont commencéà se tourner vers des organisations spécialisées pour gérer la prestation des services et à faire usage de systèmes de rémunération per capita. Même si ce genre de système est un mécanisme efficace pour réduire l'usage excessif des service de santé, il peut entraîner une offre déficiente de services médicaux. Ce mémoire examine la propensitéà fournir moins de services dans un système de pré‐paiement. On examine aussi les effets de la surveillance et de la vérification sur le comportement des médecins et sur le bien‐être des patients. On met en lumière les conditions qui assurent que les soins fournis dans un tel système donneront de meilleurs résultats que la rémunération à l'acte.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".