Policies to Optimize Physician Billing Data in Academic Alternative Relationship Payment Plans: Practices and Perspectives
Notice bibliographique
Résumé
ABSTRACT
 ObjectivesChanges in physician reimbursement policies may hinder the collection of billing claims in administrative databases. Various provincial academic alternative payment programs (APPs) use incentive- or punitive-based tools to motivate physicians to submit billing claims called shadow billings; however, these incentives are not well documented in the literature. We conducted a nation-wide survey and semi-structured face-to-face interviews in Alberta, Canada to determine existing policies and guidelines for incentivizing and promoting physician billing practices.
 ApproachMail and online surveys were sent out to academic department head physicians in the following provinces: British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, New Brunswick, Prince Edward Island and Newfoundland and Labrador. Face-to-face interviews were conducted in the province of Alberta with managers, government stakeholders, and physicians/administrators from academic APPs and Fee-for-Service plans. Face-to-face interviews and responses by mail and email submission were summarized using content analysis grouped by question type.
 ResultsIn total, there were 46 respondents (15 interviews, 26 mail/online). Content analysis revealed three primary perspectives, grouped at the level of individual physician, academic, and government. Across all of these unique perspectives, three primary themes emerged: 1) governance; 2) accountability; and 3) funding. Within these themes, findings were categorized as either (a) instruments or tools to promote physician billing in AAPPs; (b) enabling factors to support physician billing in AAPPs; and, (c) constraining factors impeding physician billing in AAPPs.
 ConclusionAccording to the majority of our respondents, financial disincentives (i.e. income at risk, financial clawbacks) appear to be most effective as a mechanism to motivate physicians within an academic APP to submit their billings. However, key barriers to successful implementation and delivery of academic APPs include a lack of alignment between government stakeholders, academic leadership and APP physician members and differences in the organizational and accountability structures of APP plans between academic facilities. It is necessary in moving forward to achieve commonly defined standards and frameworks between the various APP models across provinces and academic institutions.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,006 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».