HTA community perspectives on the use of patient preference information: lessons learned from a survey with members of HTA bodies
Bibliographic record
Abstract
This research sought to assess whether and how patient preference (PP) data are currently used within health technology assessment (HTA) bodies and affiliated organizations involved in technology/drug appraisals and assessments. An exploratory survey was developed by the PP Project Subcommittee of the HTA International Patient and Citizen Involvement Interest Group to gain insight into the use, impact, and role of PP data in HTA, as well as the perceived barriers to its incorporation. Forty members of HTA bodies and affiliated organizations from twelve countries completed the online survey. PP data were reported to be formally considered as part of the HTA evidence review process by 82.5 percent of the respondents, while 39.4 percent reported that most of the appraisals and assessments within their organization in the past year had submitted PP data. The leading reason for why PP data were not submitted in most assessments was time/resource constraints followed by lack of clarity on PP data impact. Participants reported that PP data had a moderate level of influence on the deliberative process and outcome of the decision, but a higher level of influence on the decision's quality. Most (81.8 percent) felt patient advocacy groups should be primarily responsible for generating and submitting this type of evidence. Insights from the survey confirm the use of PP data in HTA but reveal barriers to its broader and more meaningful integration. Encouragingly, participants believe obstacles can be overcome, paving the way for a second phase of research involving in-depth collaborative workshops with HTA representatives.
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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 | 0.149 | 0.284 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".