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Enregistrement W2055999055 · doi:10.1001/jama.298.15.1797

Is P4P Really FFS?

2007· article· en· W2055999055 sur OpenAlexaffabout
Walter P. Wodchis, Joseph S. Ross, Allan S. Detsky

Notice bibliographique

RevueJAMA · 2007
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensToronto Rehabilitation Institute
Organismes subventionnairesnon disponible
Mots-clésMedicine

Résumé

récupéré en direct d'OpenAlex

IT IS COMMON FOR SOCIETAL TRENDS AND PREFERENCES TO be recycled. Men’s neckties have varied from thin to wide and back to thin again. Government policies have favored individual responsibility, then social responsibility, and again individual responsibility. In health care, policy makers have swung between concern about access and concern about cost, attempting to increase or decrease medical care supply in response. Until 1980, there was widespread belief in a fairly severe physician shortage in Canada and the United States. By 1990, it was thought that too many physicians were performing too many services. Now, in the 2000s, physician shortages are again the topic of discussion. In the 1960s and 1970s, health policy makers were concerned about access and institutionalized a system that expanded insurance for patients (thus separating them from the true costs of care) and organized a reimbursement system based on fee for service (FFS) and costs incurred. As access expanded, so did health care expenditures. By the 1980s and early 1990s, health care costs in Canada and the United States were thought to be too high. The view that supply created its own demand was common and best epitomized by Roemer’s law: hospital bed utilization reflects supply. In his article on “protecting the commons,” Hiatt criticized the FFS model as having significant negative externalities for society, providing too much health care at the expense of other social services. A solution was to pay physicians and hospitals a flat amount to provide care through prospective case payments, capitation, or global budgets rather than per service. The 1990s were filled with reforms that used 2 mechanisms to correct the problem of rising health care costs and “protecting the commons”: (1) managed care, the insertion of third-party management tools that included incentives for reducing care and (2) competition between health care systems and “plans” for patients on the basis of cost and quality. Together these ideas were known as “managed competition,” an approach originally proposed by Enthoven. The objectives of managed competition, capitation, and global budgets (supply-side constraints) were to reduce unnecessary care without reducing necessary or appropriate care. In managed care, health care administrators would be able to control which patients received which types of care to increase the proportion of care that was deemed appropriate. Theoretically, managed competition made sense, but it quickly fell out of favor in many circles, particularly among the public, precisely because it was successful at achieving its goal; ie, it effectively reduced the provision of care and kept health care costs from continuing to escalate. Restricting only unnecessary care proved to be quite difficult. The RAND Health Insurance Experiment provided an analogous example of the relationship between (consumer) cost sharing (a demand-side intervention) and appropriateness. Increased patient co-payments reduced all health care service use, not just ineffective or unnecessary utilization. It seems that both supply-side and demand-side attempts to control costs provide financial barriers that limit both necessary and unnecessary treatment. Now, as the pendulum appears poised to swing away from cost concerns and back toward improving access, the Institute of Medicine’s Quality Chasm and Rewarding Performance reports have broadened priorities, also focusing attention on improving quality. To improve both quality and access, a new idea has been promoted: pay for performance (P4P).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,909
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,063
Tête enseignante GPT0,293
Écart entre enseignants0,231 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations27
Publié2007
Routes d'admission2
Résumé présentoui

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