Standardising costs or standardising care? Qualitative evaluation of the implementation and impact of a hospital funding reform in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Since 2011, the Government of Ontario, Canada, has phased in hospital funding reforms hoping to encourage standardised, evidence-based clinical care processes to both improve patient outcomes and reduce system costs. One aspect of the reform - quality-based procedures (QBPs) - replaced some of each hospital's global budget with a pre-set price per episode of care for patients with specific diagnoses or procedures. The QBP initiative included publication and dissemination of a handbook for each of these diagnoses or procedures, developed by an expert technical group. Each handbook was intended to guide hospitals in reducing inappropriate variation in patient care and cost by specifying an evidence-based episode of care pathway. We explored whether, how and why hospitals implemented these episode of care pathways in response to this initiative. METHODS: We interviewed key informants at three levels in the healthcare system, namely individuals who conceived and designed the QBP policy, individuals and organisations supporting QBP adoption, and leaders in five case-study hospitals responsible for QBP implementation. Analysis involved an inductive approach, incorporating framework analysis to generate descriptive and explanatory themes from data. RESULTS: The 46 key informants described variable implementation of best practice episode of care pathways across QBPs and across hospitals. Handbooks outlining evidence-based clinical pathways did not address specific barriers to change for different QBPs nor differences in hospitals' capacity to manage change. Hospitals sometimes found it easier to focus on containing and standardising costs of care than on implementing standardised care processes that adhered to best clinical practices. CONCLUSION: Implementation of QBPs in Ontario's hospitals depended on the interplay between three factors, namely complexity of changes required, internal capacity for organisational change, and availability and appropriateness of targeted external facilitators and supports to manage change. Variation in these factors across QBPs and hospitals suggests the need for more tailored and flexible implementation supports designed to fit all elements of the policy, rather than one-size-fits-all handbooks alone. Without such supports, hospitals may enact quick fixes aimed mainly at preserving budgets, rather than pursue evidence- and value-based changes in care management. Overestimating hospitals' change management capacity increases the risk of implementation failure.
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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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.028 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".