Custom-made health-care: an experimental investigation
Bibliographic record
Abstract
BACKGROUND: Physicians' financial interests might conflict with the best service to patients. It is essential to gain a thorough understanding of the effect of remuneration systems on physician behaviour. METHODS: We conducted a controlled laboratory experiment using a within-subject design to investigate physician behaviour underpayment heterogeneity. Each physician provided medical care to patients whose treatments were paid for under fee-for-service (FFS) or capitation (CAP). RESULTS: We observed that physicians customized their care in response to the payment system. FFS patients received considerably more medical care than did CAP patients with the same illness and treatment preference. Physicians over-served FFS patients and under-served CAP patients. After a CAP payment reduction, we observed neither a quantity reduction under CAP nor a spillover in FFS patients' treatment. CONCLUSIONS: The results suggest that, in our experimental model, fee regulation can be used to some extent to control physician spending since we did not identify a behavioural response to the CAP payment cut. Physicians did not recoup lost income by altering treatment behaviour toward CAP and/or FFS patients. Experimental economics is an excellent tool for ensuring the welfare of all those involved in the health system. Further research should investigate payment incentives as a means of developing health care teams that are more efficient.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".