MétaCan
Menu
Back to cohort
Record W2142954384 · doi:10.1017/s0266462300103216

CAN ECONOMIC EVALUATION GUIDELINES IMPROVE EFFICIENCY IN RESOURCEALLOCATION?

2000· article· en· W2142954384 on OpenAlexaboutno aff
Panos Kanavos, Paul Trueman, Amy Bosilevac

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsReimbursementEuropean unionCredibilityExcellenceEconomic evaluationEvidence-based policyPortuguesePoliticsPublic economicsBusinessPolitical scienceEconomic growthEconomicsMedicineHealth careEconomic policyLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.169
GPT teacher head0.509
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Technology Assessment in Health CareSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207