The potential of the hospital-based Health Technology Assessment: Results of a world-wide survey
Bibliographic record
Abstract
OBJECTIVES: Hospital-Based Health Technology Assessment (HB-HTA) is a heterogeneous phenomenon constantly evolving to respond to the needs of decision-makers at the hospital level. In 2023, The HB-HTA Interest Group of Health Technology Assessment International (HTAi) surveyed HB-HTA activities with the aim to provide an updated description of the actual scenario. METHODS: An online survey was conducted to gather data on the main characteristics of hospitals, HB-HTA activities, outputs, role in the decision-making processes, dissemination and training activities, and their interaction and collaboration with other stakeholders and HTA-related regulations. Finally, the survey collected feedback on the perception of and current barriers to HB-HTA. Three categories of responders were identified: Both hospitals performing and not performing HTA and policymakers. RESULTS: = 41) conducted HB-HTA, whereas eighteen consisted of hospitals not performing HTA, and twenty-eight were policy makers. HB-HTA was performed mainly in hospitals with >500 beds. HB-HTA units were organized in 40 percent of cases as an "independent group." The survey showed that HTA units could contribute to all the steps of the decision-making processes, whereas the impact of the assessments on the decisions was mainly perceived as a medium. Furthermore, HB-HTA was not seen as a duplication of effort, even without specific regulations. CONCLUSIONS: The survey highlighted the role of HB-HTA in hospital decision-making supporting the vision of HB-HTA as one of the actors in the HTA ecosystem, the success of which depends on collaboration with other stakeholders.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".