How to reform western care payment systems according to physicians, policy makers, healthcare executives and researchers: a discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Many developed countries are reforming healthcare payment systems in order to limit costs and improve clinical outcomes. Knowledge on how different groups of professional stakeholders trade off the merits and downsides of healthcare payment systems is limited. METHODS: Using a discrete choice experiment we asked a sample of physicians, policy makers, healthcare executives and researchers from Canada, Europe, Oceania, and the United States to choose between profiles of hypothetical outcomes on eleven healthcare performance objectives which may arise from a healthcare payment system reform. We used a Bayesian D-optimal design with partial profiles, which enables studying a large number of attributes, i.e. the eleven performance objectives, in the experiment. RESULTS: Our findings suggest that (a) moving from current payment systems to a value-based system is supported by physicians, despite an income trade-off, if effectiveness and long term cost containment improve. (b) Physicians would gain in terms of overall objective fulfillment in Eastern Europe and the US, but not in Canada, Oceania and Western Europe. Finally, (c) such payment reform more closely aligns the overall fulfillment of objectives between stakeholders such as physicians versus healthcare executives. CONCLUSIONS: Although the findings should be interpreted with caution due to the potential selection effects of participants, it seems that the value driven nature of newly proposed and/or introduced care payment reforms is more closely aligned with what stakeholders favor in some health systems, but not in others. Future studies, including the use of random samples, should examine the contextual factors that explain such differences in values and buy-in. JEL CLASSIFICATION: C90, C99, E61, I11, I18, O57.
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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 | 0.054 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| 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".