Models of funding and reimbursement in health care: A conceptual framework
Bibliographic record
Abstract
Abstract: What is the best way to pay providers to deliver health services? The research evidence strongly suggests that there is no single answer; rather, one must consider the incentives and disincentives inherent in alternative health‐care funding models. This article suggests a conceptual framework for categorizing payment mechanisms that highlights different policy options and their associated tradeoffs. It concentrates on the following: distinctions between need, demand, and utilization as they affect the rationale for government involvement; models of the possible funding flows (two‐way, three‐way, and four‐way) as they affect policy levers; and implications of various approaches to payment. The framework is then used to clarify the advantages and disadvantages of particular approaches to financing health care and to clarify the tradeoffs involved. Sommaire: Quel est le meilleur moyen de payer les fournisseurs pour leur prestation de services de santé? Les données probantes de la recherche laissent fortement entendre qu'il n'existe pas une seule et unique réponse; il faut plutôt tenir compte des incitatifs et des désincitatifs inhérents aux autres modèles de financement des soins de santé. Pour classer par catégories les mécanismes de paiement, le présent article propose un cadre conceptuel qui souligne différentes options de politiques et les compromis qui leur sont associés. Il se concentre sur : les distinctions entre besoin, demande et utilisation qui ont une incidence sur la justification de l'intervention du gouvernement; les modèles des flux de financement possibles (à deux, trois et quatre directions) qui ont une incidence sur les leviers politiques; et les implications de diverses approches à l'égard du paiement. Le cadre est alors utilisé pour préciser les avantages et les inconvénients des approches particulières concernant le financement des soins de santé, et pour clarifier les compromis impliqués.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".