Use of Evidence-Informed Deliberative Processes by Health Technology Assessment Agencies Around the Globe
Bibliographic record
Abstract
BACKGROUND: Evidence-informed deliberative processes (EDPs) were recently introduced to guide health technology assessment (HTA) agencies to improve their processes towards more legitimate decision-making. The EDP framework provides guidance that covers the HTA process, ie, contextual factors, installation of an appraisal committee, selecting health technologies and criteria, assessment, appraisal, and communication and appeal. The aims of this study were to identify the level of use of EDPs by HTA agencies, identify their needs for guidance, and to learn about best practices. METHODS: A questionnaire for an online survey was developed based on the EDP framework, consisting of elements that reflect each part of the framework. The survey was sent to members of the International Network of Agencies for Health Technology Assessment (INAHTA). Two weeks following the invitation, a reminder was sent. The data collection took place between September-December 2018. RESULTS: Contact persons from 27 member agencies filled out the survey (response rate: 54%), of which 25 completed all questions. We found that contextual factors to support HTA development and the critical elements regarding conducting and reporting on HTA are overall in place. Respondents indicated that guidance was needed for specific elements related to selecting technologies and criteria, appraisal, and communication and appeal. With regard to best practices, the Canadian Agency for Drugs and Technologies and the National Institute for Health and Care Excellence (NICE, UK) were most often mentioned. CONCLUSION: This is the first survey among HTA agencies regarding the use of EDPs and provides useful information for further developing a practical guide for HTA agencies around the globe. The results could support HTA agencies in improving their processes towards more legitimate decision-making, as they could serve as a baseline measurement for future monitoring and evaluation.
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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 | 0.366 | 0.516 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".