Defining the Value of Health Technologies in Latin America: Developments in Value Frameworks to Inform the Allocation of Healthcare Resources
Bibliographic record
Abstract
OBJECTIVES: The recent development of value frameworks to inform healthcare resource allocation responds to a demand to make the decision-making process more inclusive and explicit. The objectives of the 2018 Latin American (LAtam) Health Technology Assessment International (HTAi) Policy Forum were to explore the current international experiences and to discuss the potential application of value frameworks in Latin America. METHODS: A background paper, presentations, and group discussions of Policy Forum members (43 participants, 12 LAtam countries represented) at the 2018 HTAi Policy Forum meeting informed this paper. RESULTS: Participants agreed that HTA and decision making based on more comprehensive and inclusive value frameworks could improve health system effectiveness, efficiency, sustainability, and equity; promote transparency in the decision process; sustain a more comprehensive assessment of technologies; and facilitate stakeholder participation as well as accountability of decisions. Criteria that were identified as essential to be included in a value framework for LAtam were burden of illness and severity of the disease, effectiveness and safety of the technology, quality of the evidence, cost-effectiveness, and budget impact. Potential challenges identified for the application of value frameworks in LAtam, included scarcity of human resources and delays in the assessment process. CONCLUSIONS: Forum participants agreed that the next steps should be to identify appropriate processes and methodologies, adapted to the context of each country, regarding the application of value frameworks to improve the link between HTA and decision making.
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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 | 0.113 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.035 | 0.028 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".