Information Asymmetry, Insurance, and the Decision to Hospitalize
Bibliographic record
Abstract
In a theoretical model, we analyze the effects of various kinds of demand- and supply-side incentives in the context of a model in which patients and doctors must decide not only on an aggregate quantity of health services to use in treating various kinds of illness, but also have a choice between different kinds of providers (in particular, outpatient services rendered by primary-care physicians or inpatient services provided by hospital-based specialists). We present two broad models, the traditional fee-for-service payment scheme and a managed care setup where physicians are paid via capitation, and analyze them both with and without information asymmetry. We find that under certain plausible conditions, second-best optimal managed care plans may dominate second-best optimal conventional plans that rely on cost control through demand-side cost sharing. À l'aide d'un modèle théorique dans lequel patients et médecins doivent choisir la quantité de service à utiliser ainsi que celui, de l'omnipraticien ou du spécialiste uvrant à l'hôpital, qui fournira ces services, nous analysons différents mécanismes d'incitation agissant sur l'offre et la demande. Nous étudions essentiellement deux modes d'organisation : le système conventionnel de rémunération à l'acte et le système de gestion intégrée des soins avec une rémunération per capita; à la fois en présence et en l'absence d'asymétrie d'information. Nous obtenons comme résultat qu'à certaines conditions plausibles, l'optimum de second-rang auquel mène le système de gestion intégrée est supérieur à celui que donne le système conventionnel de rémunération à l'acte qui répercute une partie des coûts sur l'utilisateur.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".