Stakeholder perspectives on the current status and potential barriers of patient involvement in health technology assessment (HTA) across Europe
Bibliographic record
Abstract
Abstract Background There are wide variations in the practices of patient involvement in health technology assessment (HTA) in Europe. The field is lacking a consensus on good practices, leading to divergent processes, methods, and evaluation of patient involvement. To identify potential good practice approaches and current gaps, a structured online survey was conducted among HTA stakeholders, including HTA practitioners, patient stakeholders, industry representatives, and others who had experienced patient involvement in HTA. Methods The questionnaire was co-created by HTA experts, patient stakeholders, and industry representatives and disseminated between 29 April and 14 September 2022. Results Responses (n = 168) were submitted from thirty-two European countries by HTA practitioners (n = 33), patient stakeholders (n = 75), industry stakeholders (n = 42), providers (n = 5), academics (n = 7), and others (n = 6). The responses indicated that “allowing access to treatments that have demonstrated value”is the principle rationale for conducting HTA. In terms of the importance of patient involvement, there was consensus across stakeholder groups that “patients have insights and information [that] no other stakeholder has” and that patient involvement is important “to inform HTA which evidence is most patient-relevant”. Shortcomings were identified in the lack of systematic and transparent processes, an unsatisfactory level of information and guidance, and minimal communication and collaboration. Conclusions The diverse stakeholders who responded highlighted the need for improving specific aspects of patient involvement practices, including better guidance and information, a more consistent flow of communication between the HTA body and participating patient stakeholders, and the need to develop and implement a consensus on good practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.107 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".