Health technology assessment processes: a North-South comparison of the evaluation and recommendation of health technologies in Canada and Chile
Bibliographic record
Abstract
Purpose Health systems are progressively stressed by health spending, which is partially explained by the increase in the cost of health technologies. Countries have defined processes to prioritize interventions to be covered. This study aims to compare for the first time health technology assessment (HTA) processes in Canada and Chile, to explain the factors driving these decisions. Design/methodology/approach This is a health policy analysis comparing HTA processes in Canada and Chile. An analysis of publicly available documents in Canada (for CADTH) and Chile (for the Ministry of Health (MoH)) was carried out. A recognized political science framework (the 3-I framework) was used to explain the similarities and differences in both countries. The comparison of processes was disaggregated into eligibility and evaluation processes. Findings CADTH has different programmes for different types of drugs (with two separate expert committees), whereas the MoH has a unified process. Although CADTH’s recommendations have a federal scope, the final coverage is a provincial decision. In Chile, the recommendation has a national scope. In both cases, past recommendations influence the scope of the evaluation. Pharmaceutical companies and patient associations are important interest groups in both countries. Whereas manufacturers and tumour groups are able to submit applications to CADTH, the Chilean MoH prioritizes applications submitted by patient associations. Originality/value Institutions, interests and ideas play important roles in driving HTA decisions in Canada and Chile, which is demonstrated in this novel analysis. This paper provides a unique comparison to highly relevant policy processes in HTA, which is often a research area dominated by effectiveness and cost-effectiveness studies.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".