Effects and repercussions of local/hospital-based health technology assessment (HTA): a systematic review
Bibliographic record
Abstract
BACKGROUND: Health technology assessment (HTA) is increasingly performed at the local or hospital level where the costs, impacts, and benefits of health technologies can be directly assessed. Although local/hospital-based HTA has been implemented for more than two decades in some jurisdictions, little is known about its effects and impact on hospital budget, clinical practices, and patient outcomes. We conducted a mixed-methods systematic review that aimed to synthesize current evidence regarding the effects and impact of local/hospital-based HTA. METHODS: We identified articles through PubMed and Embase and by citation tracking of included studies. We selected qualitative, quantitative, or mixed-methods studies with empirical data about the effects or impact of local/hospital-based HTA on decision-making, budget, or perceptions of stakeholders. We extracted the following information from included studies: country, methodological approach, and use of conceptual framework; local/hospital HTA approach and activities described; reported effects and impacts of local/hospital-based HTA; factors facilitating/hampering the use of hospital-based HTA recommendations; and perceptions of stakeholders concerning local/hospital HTA. Due to the great heterogeneity among studies, we conducted a narrative synthesis of their results. RESULTS: A total of 18 studies met the inclusion criteria. We reported the results according to the four approaches for performing HTA proposed by the Hospital Based HTA Interest Sub-Group: ambassador model, mini-HTA, internal committee, and HTA unit. Results showed that each of these approaches for performing HTA corresponds to specific needs and structures and has its strengths and limitations. Overall, studies showed positive impacts related to local/hospital-based HTA on hospital decisions and budgets, as well as positive perceptions from managers and clinicians. CONCLUSIONS: Local/hospital-based HTA could influence decision-making on several aspects. It is difficult to evaluate the real impacts of local HTA at the different levels of health care given the relatively small number of evaluations with quantitative data and the lack of clear comparators. Further research is necessary to explore the conditions under which local/hospital-based HTA results and recommendations can impact hospital policies, clinical decisions, and quality of care and optimize the use of scarce resources.
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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 | 0.061 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".