Qualitative analysis of the dynamics of policy design and implementation in hospital funding reform
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.083 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".