Has the time come for cost-effectiveness analysis in US health care?
Bibliographic record
Abstract
Cost-effectiveness analysis (CEA) is a powerful analytic tool for assessing the value of health care interventions but it is a method used sparingly in the US. Despite its growing acceptance internationally and its endorsement in the academic literature, most policy analysts have assumed that US decision makers will resist using CEA to inform coverage decisions. This study sought to clarify the extent to which CEA is understood and accepted by US decision makers, including regulators, private and public insurers, and purchasers, and to identify their points of difficulty with its use. We conducted half-day workshops with a sample of six California-based health care organizations that spanned a range of public and private perspectives regarding coverage of health care services. Each workshop included an overview of CEA methods, a priority-setting exercise that asked participants (acting as 'social decision makers') to rank condition treatment pairs prior to and following provision of cost-effectiveness information; and a facilitated discussion of obstacles and opportunities for using CEA in their own organizations. Pre and post-questionnaires inquired as to obstacles toward implementing CEA, attitudes toward rationing, and views on the use of CEA in Medicare and in private insurance coverage decision-making. In post-workshop surveys major obstacles identified included: fears of litigation, concerns about the quality and accuracy of studies that were commercially sponsored, and failure of CEAs to address shorter horizon cost implications. Over 90% of participants felt that CEA should be used as an input to Medicare coverage decisions and 75% supported its use in such decisions by private insurance plans. Despite the wide acceptance of CEA, at the conclusion of the workshop, 40% of the sample remained uncomfortable with support of 'rationing' per se. We suggest that how cost-effectiveness analysis is framed will have important implications for its acceptability to US decision makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".