Using Cost-Effectiveness Analysis to Improve Health Care: Opportunities and Barriers
Bibliographic record
Abstract
As health costs in the U.S. soar past $1.5 trillion, much evidence indicates that the nation does not get good value for its money. It is widely agreed that we could do better by using cost-effective analysis (CEA) to help determine which health care services are most worthwhile. American policy makers, however, have largely avoided using CEA, and researchers have devoted little attention to understanding why this is so. By considering the economic, social, legal, and ethical factors that contribute to the situation, and how they can be negotiated in the future, this book offers a unique perspective. It traces the roots of EA in health and medicine, describes its promise for rational resource allocation, and discusses the nature of the opposition to it, using Medicare and the Oregon health plans as examples. In exploring the disconnection between the promise of CEA and the persistent failure of rational intentions, the book seeks to find common ground and practical solutions. It analyzes the prospects for change and presents a roadmap for getting there. It offers pragmatic advice for cost-effectiveness analysts, discussing ways in which they can better translate their research findings into the basis for action. The book also offers advice for policy makers and politicians, including lessons from Europe, Canada, and Australia, and underlines the need for leadership to establish the conditions for change. Available in OSO: http://www.oxschol.com/oso/public/content/publichealthepidemiology/9780195171860/toc.html
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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 | 0.232 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".