CAN ECONOMIC EVALUATION GUIDELINES IMPROVE EFFICIENCY IN RESOURCEALLOCATION?
Bibliographic record
Abstract
The use of economic evaluation in decision making appears to have increased over the past few years and economic evaluation is looked upon as another measure to help contain costs and improve efficiency in an evidence-based decision-making environment. Following the examples of Australia and the Canadian Province of Ontario, four European Union (EU) countries (Finland, the Netherlands, Portugal, and the United Kingdom) have recently introduced economic evaluation guidelines. In addition to the Australian and Canadian guidelines, which constitute a hurdle to reimbursement, the paradigm that seems to be evolving in the four EU countries follows a similar route. Finland and the Netherlands seem to be moving toward the notion of a fourth hurdle to reimbursement, whereas the National Institute for Clinical Excellence in England and Wales was in principle meant to influence practice, although in reality this essentially acts as a hurdle to reimbursement, requiring a different data set to that used by regulatory authorities. Whereas the Portuguese guidelines were developed to assist in preparing economic submissions to support reimbursement decisions, they are unclear about when such evidence will be required and also discuss the dissemination of economic evidence to broader audiences. The introduction of these guidelines poses a number of challenges to policy makers, the implications of which are analyzed in the paper: a) to ensure that economic evaluations are carried out scientifically without industrial or political bias; b) to define an acceptable methodology that would increase their credibility; and c) to address certain practical issues ranging from deciding how to use economic evaluations in policy making to setting up new institutions or improving the coordination and dissemination of evidence. The variation in the use of economic evaluation guidelines in the four EU countries highlights the differences in national pharmaceutical policies and is in line with policy makers' continuous attempts to contain costs. While the paper critically discusses the guidelines, it also points out that a series of methodologic issues need to be addressed if economic criteria are to be introduced in policy making with the aim to improve resource allocation. The paper concludes that economic evaluation as a discipline is beginning to impact on policy, whereas the consistent use of economic evaluation results is, in principle, being adopted by policy makers but needs to go a step further to reach practitioners.
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 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 | 0.568 | 0.840 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.035 | 0.052 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.027 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".