International experience of using report forms for hospital-based health technology asssessment
Bibliographic record
Abstract
Hospital-based health technology assessment (HB-HTA) is one of the components of public management and support hospital managers to science-based decision-making regarding the implementation of new and innovative health technologies (HT). HB-HTA includes the processes and methods used to make HTA reports in and for hospitals. Ukraine has already started implementing hospital-based HTA, but a number of steps are still needed to fully implement HTA at the hospitals. One of them is the choice of HB-HTA report form, which will be appropriate to use in the practical activities of hospitals in Ukraine. The purpose of the study is to analyse the international experience of using different HB-HTA report form with the aim of selecting the optimal HB-HTA report form for hospitals in Ukraine. There were analysed different forms of HTA reports in Ukraine regulatory framework, international scientific literature, the AdHopHTA handbook, which included the following countries: Norway, Finland, Turkey, Spain, Austria, Italy, Estonia, Denmark, Switzerland, in international HTA database INAHTA, Canadian and Kazakhstan report databases. The methods of content analysis, systematization and generalization were used. Making managerial decisions about investment or disinvestment in health technology requires information that meets the hospital stakeholders needs. Such scientifically based information is provided by HB-HTA report form. There are a wide range of HB-HTA reports forms in terms of content, subject matter, structure and resources (time and staff). Each is based on the EUnetHTA HTA core model. For introducing a hospital-based HTA, the countries of the world most often choose the mini-HTA form. Mini-HTA supports hospital managers to make science-based strategic decisions of the introduction of new treatment methods, new indications for the use of existing technology, medical equipment, medical devices, or stopping the usage of health technology. The choice between performing a mini-HTA or a more comprehensive hospital-based HTA often requires a balance between the quality and thoroughness and the necessary speed of assessment in a specific situation. From the authors' point of view, the most applicable to Ukrainian context is the mini-HTA form. Adapting mini-HTA and approving it as a recommended report form at the state level will provide an opportunity to more widely HTA usage by hospitals in Ukraine.
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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 | 0.152 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".