Use of Patient Preferences in Health Technology Assessment: Perspectives of Canadian, Belgian and German HTA Representatives
Bibliographic record
Abstract
OBJECTIVE: Patient preferences can be informative for health technology assessment (HTA) and payer decision making. However, applications may be different per country. The aim of this study therefore was to investigate HTA representatives' opinions on whether and how to incorporate patient preferences in HTA in their respective countries. METHODS: Three country-specific focus groups were conducted with three to seven HTA representatives from Germany, Belgium, and Canada. A predefined focus group guide was used that covered topics relating to how patient preferences can be used in HTA, namely HTA stage, weight, impact, and quality, as well as a case example of gene therapy. Transcripts were analyzed using NVivo 12 following thematic analysis. RESULTS: Across all HTA bodies, an interest in the use of patient preferences was observed for scientific advice and value assessments, but not through incorporation in quality-adjusted life-years and multi-criteria decision analysis. HTA representatives found it difficult to determine the weight patient preferences may receive in decision making, but thought it could have an impact on payer decision making if the study is of acceptable quality. CONCLUSIONS: In the near future it may be impossible to achieve structural integration of patient preferences with other evidence in HTA (e.g., in cost-effectiveness analysis), but HTA bodies are willing to incorporate patient preferences in other HTA sections as supportive evidence. To allow for that use, future work should focus on meeting HTA and payer needs when conducting patient preference studies and on education of HTA and payer representatives regarding these studies.
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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 | 0.065 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".