MétaCan
Menu
Back to cohort
Record W2104769168 · doi:10.1586/14737167.2013.814962

Harmonization of reimbursement and regulatory approval processes: a systematic review of international experiences

2013· review· en· W2104769168 on OpenAlexafffund
Bernice Tsoi, Lisa Masucci, Kaitryn Campbell, Michael Drummond, Daria O’Reilly, Ron Goeree

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2013
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsPrograms for Assessment of Technology in Health Research InstituteMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsHarmonizationReimbursementRestructuringProcess (computing)BusinessRisk analysis (engineering)Process managementManagement sciencePublic economicsHealth careComputer scienceEconomicsPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

A considerable degree of overlap exists between reimbursement and regulatory approval of health technologies, and harmonization of certain aspects is both possible and feasible. Various models to harmonization have been suggested in which a number of practical attempts have been drawn from. Based on a review of the literature, approaches can be categorized into those focused on reducing uncertainty and developing economies of scale in the evidentiary requirements; and/or aligning timeframes and logistical aspects of the review process. These strategies can further be classified based on the expected level of structural and organizational change required to implement them into the existing processes. Passive processes require less modification, whereas active processes are associated with greater restructuring. Attempts so far at harmonization have raised numerous legal and practical issues and these must be considered when introducing a more harmonized framework into the existing regulatory and reimbursement arrangements.

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)0.034
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0340.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0100.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.348
GPT teacher head0.618
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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

Citations31
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueExpert Review of Pharmacoeconomics & Outcomes ResearchSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207