Threats to the value of Health Technology Assessment: Qualitative evidence from Canada and Poland
Bibliographic record
Abstract
BACKGROUND: Health Technology Assessment is used to support the process of drug appraisal and reimbursement decisions in a variety of health systems. Examples can be found in mature Western countries, such as Canada, and in emerging economies of Central and Eastern Europe, such as Poland. The value of HTA in the process is influenced by the evidence used and the stakeholders involved. METHODS: Qualitative interviews with 29 members of two appraisal committees were held in Canada and Poland between July 2017 and March 2018. An a priori thematic framework was applied and supplemented with emergent themes. RESULTS: We report on the results of a core emergent theme - threats identified by respondents to the value of HTA in the formulary process. We classified these into internal threats that arise due to undue influence on the individuals involved in appraisal, and external threats that arise due to undue influence on the production of evidence. DISCUSSION: Findings align with previous evidence regarding political and corporate pressures on the process, and a perception of declining quality of evidence. We contribute to the discussion by highlighting the importance of motivation of experts involved in the appraisal process. CONCLUSIONS: The recognition of internal and external threats lays the groundwork for a discussion of policies used to mitigate them. We offer suggestions about potential policy responses.
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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 | 0.090 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".