PD03 Uptake Of Health Technology Assessment In Hospitals: A Scoping Review
Bibliographic record
Abstract
Introduction The use of Health Technology Assessment (HTA) in hospitals contributes to decision-making, professional training, greater interaction with technical to scientific knowledge, resource-saving, and partnerships. Hospitals are strategic for the field of clinical management and quality of care and are open to partnerships with national and international agencies and groups. Despite the hospital-based (HB)-HTA movement, the incipient application of HTA to the hospital decision-making process is related to incipient planning, and there is still much room for progress worldwide. The aim of this study was to analyze the uptake level of HB-HTA in diverse contexts. Methods A scoping review was conducted according to the methodology of the Joanna Briggs Institute, whose data analysis model consisted of the combination of Donabedian’s structure, process, and outcome categories and the dimensions of the project Adopting Hospital Based Health Technology Assessment in European Union (AdHopHTA). Results We identified 270 studies, and after removing duplicates and reading full texts, 36 references met the eligibility criteria. Thirty-six hospitals were identified, of which there were 24 largescale hospitals with extra bed capacity. Twenty-three hospitals were affiliated with universities. Canada stood out with five university hospitals, four of which with public funding. Half of the identified hospitals had HB-HTA units (18/36). Hospitals with full uptake level of HTA corresponded to 75 percent of the sample (27/36), and the remainder had partially uptake level of HTA, or 25 percent of the hospitals in the review (9/36). There were no hospitals with incipient uptake level of HTA. Conclusions Measuring the uptake level of HTA in hospitals contributed to understanding how their participation has occurred in the field of HB-HTA. This study revealed the importance of identifying factors such as sustainability, growth, and evolution of HB-HTA in countries with and without a tradition in this field.
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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 | 0.049 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.037 | 0.043 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".