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Enregistrement W3124436994 · doi:10.55016/ojs/sppp.v7i1.42464

Ontario’s Experiment with Primary Care Reform

2014· article· en· W3124436994 sur OpenAlexaffabout
Arthur Sweetman, Gioia Buckley

Notice bibliographique

RevueThe School of Public Policy Publications · 2014
Typearticle
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésPrimary carePrimary (astronomy)Political scienceMedicineFamily medicinePhysicsAstronomy

Résumé

récupéré en direct d'OpenAlex

For the past decade-and-a-half, the government of Ontario has been implementing sweeping reforms in an effort to improve primary health care delivery. Altering physician-compensation models is central to this initiative. One measure of the scale of change is that in 2000 roughly 95 per cent of general/family practitioners were paid traditional fee-for-service, but by 2013 that proportion had plunged to just 28 per cent. The province has clearly succeeded in largely replacing the traditional fee-for-service payment structure with blended payment models that are mostly group-oriented and include: 1) capitation (in some cases): a single payment for providing a particular “basket” of services to a patient for a fixed period, for example a year, regardless of the number of services provided, 2) fee-for-service payment, for services outside the capitated basket and provided in special situations, and 3) various bonuses and incentives (sometimes called pay-for-performance) that mostly focus on preventive care and the management of chronic conditions. Physicians in rural and northern areas, as well as some clinics, also have salary and similar models as options. Ontario has simultaneously introduced patient “rostering” — the formalized connecting of one patient to one physician and/or physician team/group — creating a relationship better suited to delivering preventive healthcare services. However, when surveyed, many patients are unaware that they have been “rostered” meaning that at present much of the benefit must be derived from the physician side alone. It remains to be seen whether or not it is important for patients to be aware that they are rostered. Beyond its clinical benefits, rostering has appreciable rhetorical and political value, as well as potential as a planning tool in efforts to ensure that the local and provincial supply of primary care is appropriate. In a health-care system as large and complex as Ontario’s, reform is more evolutionary than revolutionary; but the province has arguably moved rapidly within this context. Expenditures have been substantial and the initiatives groundbreaking. However, the same challenges that make reform a formidable undertaking also make it difficult to readily, or quickly, measure success, especially since many changes are ongoing. It is not yet demonstrably clear to what degree the government’s goals are being achieved. At present, there are mixed and conflicting findings about whether some of these changes have moved the health system towards the intended goals of improving health-care access and quality, and patient satisfaction, let alone whether the potential improvements can justify the resources expended to achieve them. Naturally, those results we do have at this point offer insight only into the short-term effects of these changes. Especially, it is too early for sufficient evidence to have accumulated on the impact of new physician-group models on downstream costs, including drug prescriptions, specialist care, hospital costs and the use of diagnostic tests. These are, however, central questions that will in large part determine success. Also, it appears that the Ontario government could have accomplished nearly all of its goals so far without having implemented capitation, although capitation may prove beneficial in the longer term as the scarcity of physicians since the 1990s seems to be shifting towards a surplus. In this new era, the health ministry will likely need to take a more hands-on role than it has in the past, including improved system monitoring. Going forward many stakeholders should be involved in evaluating this experiment on an ongoing basis to ensure that it is serving the healthcare needs of the population in an effective and efficient way..

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 enseignants0,842
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,062
Tête enseignante GPT0,399
Écart entre enseignants0,337 · 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

Citations25
Publié2014
Routes d'admission2
Résumé présentoui

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