Managing Medical Technology: Lessons for the United States from Quebec and France
Bibliographic record
Abstract
Important modifications to technology assessment, diffusion, adoption, and utilization must take place if the United States is to better employ medical technology and save resources so as to assure access for the uninsured and underinsured. The United States can learn from other health systems that are more successful in achieving these goals. The author selects for comparison the health systems of France and Quebec. The discussion focuses on the differences between the three systems in the management of medical technology on a range of policy-relevant dimensions, including health system structure, attitudes about planning versus market competition, government regulation, the balance between decentralization and centralization, the needs of the individual and those of the society, linkages between technology assessment and policy-making, and the importance of medical technology assessment for medical practice. Seven specific recommendations are made for better managing medical technology in the United States, drawing on what can be observed from the experiences of Quebec and France.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".