Qualitative analysis of the dynamics of policy design and implementation in hospital funding reform
Notice bibliographique
Résumé
BACKGROUND: As in many health care systems, some Canadian jurisdictions have begun shifting away from global hospital budgets. Payment for episodes of care has begun to be implemented. Starting in 2012, the Province of Ontario implemented hospital funding reforms comprising three elements: Global Budgets; Health Based Allocation Method (HBAM); and Quality-Based Procedures (QBP). This evaluation focuses on implementation of QBPs, a procedure/diagnosis-specific funding approach involving a pre-set price per episode of care coupled with best practice clinical pathways. We examined whether or not there was consensus in understanding of the program theory underpinning QBPs and how this may have influenced full and effective implementation of this innovative funding model. METHODS: We undertook a formative evaluation of QBP implementation. We used an embedded case study method and in-depth, one-on-one, semi-structured, telephone interviews with key informants at three levels of the health care system: Designers (those who designed the QBP policy); Adoption Supporters (organizations and individuals supporting adoption of QBPs); and Hospital Implementers (those responsible for QBP implementation in hospitals). Thematic analysis involved an inductive approach, incorporating Framework analysis to generate descriptive and explanatory themes that emerged from the data. RESULTS: Five main findings emerged from our research: (1) Unbeknownst to most key informants, there was neither consistency nor clarity over time among QBP designers in their understanding of the original goal(s) for hospital funding reform; (2) Prior to implementation, the intended hospital funding mechanism transitioned from ABF to QBPs, but most key informants were either unaware of the transition or believe it was intentional; (3) Perception of the primary goal(s) of the policy reform continues to vary within and across all levels of key informants; (4) Four years into implementation, the QBP funding mechanism remains misunderstood; and (5) Ongoing differences in understanding of QBP goals and funding mechanism have created challenges with implementation and difficulties in measuring success. CONCLUSIONS: Policy drift and policy layering affected both the goal and the mechanism of action of hospital funding reform. Lack of early specification in both policy goals and hospital funding mechanism exposed the reform to reactive changes that did not reflect initial intentions. Several challenges further exacerbated implementation of complex hospital funding reforms, including a prolonged implementation schedule, turnover of key staff, and inconsistent messaging over time. These factors altered the trajectory of the hospital funding reforms and created confusion amongst those responsible for implementation. Enacting changes to hospital funding policy through a process that is transparent, collaborative, and intentional may increase the likelihood of achieving intended effects.
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 | 0,083 | 0,088 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,012 | 0,022 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».