Four Essays in Health Economics
Notice bibliographique
Résumé
This thesis addresses health-policy relevant questions regarding quantity and quality of service delivery in primary healthcare using health administrative data from the province of Ontario. It is comprised of four chapters that explore the following questions: (1) What is the impact of switching from an enhanced fee-for-service (EFFS) payment model to a blended capitation payment model on the specialist referral rates of primary care physicians? (2) What are the rates of inappropriate laboratory testing in the province of Ontario? (3) What are the costs and determinants (physician and practice characteristics) of these inappropriate tests? (4) What is the impact of primary care payment structure on the quantity (number and cost) and the quality (appropriateness) of clinical laboratory testing? Fee-for-service (FFS) payment systems give physicians an incentive to treat patients on the margin of being referred, whereas in capitation systems physicians do not have a financial incentive to treat such marginal patients. Chapter 1 empirically examines how these two payment systems affect referral rates. The results show an increase in specialist visits upon a switch from an EFFS model to a blended capitation model when the physician is listed as the referring physician in the data, but no change in total specialist visits for these physicians’ patients. This change is not observed immediately upon switching payment models. Physicians paid by blended capitation who practice in an interdisciplinary health team have fewer specialist visits per rostered patient compared to EFFS physicians, despite an increase in their patients’ specialist visits after joining the interdisciplinary team. Using a definition of inappropriateness that quantifies ordering clinical laboratory tests too often or too soon following a previous test, Chapter 2 examines the rates of inappropriate laboratory testing for nine selected analytes in Ontario. The chapter finds that the percentage of inappropriate tests ranges from 6% to 20%. Moreover, between 60% and 85% of the time, the physician ordering an inappropriate test is the same physician who ordered the previous test. The findings also show that specialists are more likely than primary care physicians to order repeat tests too soon. Chapter 3 examines the costs and determinants associated with the rates of inappropriate laboratory utilization. The associated costs of inappropriate/redundant laboratory testing for the selected analytes ranges between 6 – 20% of the total cost of each test. Statistical analyses of the association of physician and practice characteristics with inappropriate testing are done using a logit model. Conditional upon the variables within the model, male physicians, physicians trained outside of Canada, older physicians, and a younger patient population are all shown to be associated with less inappropriate testing. Primary care physicians in group practices and in payment models with pay-for-performance (P4P) incentives are less likely to order inappropriate tests and specialist physicians are twice as likely to order inappropriately compared to FFS primary care physicians. Differences in physician, practice and patient characteristics, however, explain only a small amount of the variation in inappropriate utilization. Chapter 4 examines how physicians’ laboratory test ordering patterns change following a switch from an FFS payment model enhanced with P4P to a blended capitation payment model, and the differences in ordering patterns between traditional staffing and interdisciplinary teams within the blended capitation model. Using a propensity score weighted fixed-effects specification to address selection, the chapter estimates that a mandatory switch to capitation would lead to an average of 3% fewer laboratory requisitions per patient. Patients’ laboratory utilization also becomes more concentrated with the rostering physician. More importantly, using diabetes-related laboratory tests as a case study, physicians order 3% fewer inappropriate/redundant tests after joining the blended model and 9% fewer if they joined an interdisciplinary care team within the blended model.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,012 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».