British Columbia Hospitals: examination and assessment of payment reform (B-CHeaPR)
Bibliographic record
Abstract
BACKGROUND: Accounting for 36% of public spending on health care in Canada, hospitals are a major target for cost reductions through various efficiency initiatives. Some provinces are considering payment reform as a vehicle to achieve this goal. With few exceptions, Canadian provinces have generally relied on global and line-item budgets to contain hospital costs. There is growing interest amongst policy-makers for using activity based funding (ABF) as means of creating financial incentives for hospitals to increase the 'volume' of care, reduce cost, discourage unnecessary activity, and encourage competition. British Columbia (B.C.) is the first province in Canada to implement ABF for partial reimbursement of acute hospitalization. To date, there have been no formal examinations of the effects of ABF policies in Canada. This study proposal addresses two research questions designed to determine whether ABF policies affect health system costs, access and hospital quality. The first question examines the impact of the hospital funding policy change on internal hospital activity based on expenditures and quality. The second question examines the impact of the change on non-hospital care, including readmission rates, amount of home care provided, and physician expenditures. METHODS/DESIGN: A longitudinal study design will be used, incorporating comprehensive population-based datasets of all B.C. residents; hospital, continuing care and physician services datasets will also be used. Data will be linked across sources using anonymized linking variables. Analytic datasets will be created for the period between 2005/2006 and 2012/2013. DISCUSSION: With Canadian hospitals unaccustomed to detailed scrutiny of what services are provided, to whom, and with what results, the move toward ABF is significant. This proposed study will provide evidence on the impacts of ABF, including changes in the type, volume, cost, and quality of services provided. Policy- and decision-makers in B.C. and elsewhere in Canada will be able to use this evidence as a basis for policy adaptations and modifications. The significance of this proposed study derives from the fact that the change in hospital funding policy has the potential to affect health system costs, residents' access to care and care quality.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".