HEALTH TECHNOLOGY ASSESSMENT METHODS GUIDELINES FOR MEDICAL DEVICES: HOW CAN WE ADDRESS THE GAPS? THE INTERNATIONAL FEDERATION OF MEDICAL AND BIOLOGICAL ENGINEERING PERSPECTIVE
Bibliographic record
Abstract
OBJECTIVES: Current health technology assessment (HTA) methods guidelines for medical devices may benefit from contributions by biomedical and clinical engineers. Our study aims to: (i) review and identify gaps in the current HTA guidelines on medical devices, (ii) propose recommendations to optimize the impact of HTA for medical devices, and (iii) reach a consensus among biomedical engineers on these recommendations. METHODS: A gray literature search of HTA agency Web sites for assessment methods guidelines on devices was conducted. The International Federation of Medical and Biological Engineers (IFMBE) then convened a structured focus group, with experts from different fields, to identify potential gaps in the current HTA guidelines, and to develop recommendations to fill these perceived gaps. The thirty recommendations generated from the focus group were circulated in a Delphi survey to eighty-five biomedical and clinical engineers. RESULTS: Thirty-two panelists, from seventeen countries, participated in the Delphi survey. The responses showed a strong agreement on twenty-seven of thirty recommendations. Some uncertainties remain about the methods to accurately assess the effectiveness and safety, and interoperability of a medical device with other devices or within the clinical setting. CONCLUSIONS: As medical devices differ from drug therapies, current HTA methods may not accurately reflect the conclusions of their assessment. Recommendations informed by the focus group discussions and Delphi survey responses aimed to address the perceived gaps, and to provide a more integrated approach in medical device assessments in combining engineering with other perspectives, such as clinical, economic, patient, human factors, ethical, and environmental.
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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 | 0.369 | 0.503 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.024 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".