Generating Appropriate and Reliable Evidence for Value Assessment of Medical Devices: An ISPOR Medical Devices and Diagnostics Special Interest Group Report
Bibliographic record
Abstract
Abstract Health technology assessment (HTA) methods have become an important health policy tool to assess value. Yet recommendations for what constitutes appropriate and reliable evidence and methodologies for assessment of medical devices are still debated because methods to evaluate pharmaceuticals are often, and incorrectly, the starting points for device assessments. The study aims to: (i) propose recommendations on appropriate methodologies to assess the evidence on medical devices, (ii) identify assessment methods that can be used to measure device value, and (iii) suggest key areas for future work. ISPOR's Medical Devices and Diagnostics Special Interest Group conducted a comprehensive search of databases and gray literature on evidence development and value assessment on medical devices. The literature search was supplemented with hand searching from high impact journals in the related field. The ten-person expert working group obtained written comments through multiple rounds of review from internal and external stakeholders. Recommendations were made to encourage and guide future research. Multicriteria decision analysis was identified as a useful approach to assess the value of treatment. Consideration should be given to resource-use measures; valid and reliable functional status questionnaires; and general and disease-specific, health-related, quality-of-life measures in economic evaluations of device use. For future work, best practices for value framework design should take into consideration those factors that influence the value of medical devices. Integration of value-based evidence data in an evidence-generation and -synthesis process is needed to support market access and adoption decisions. Methodological recommendations for measuring value can be challenging when the selection of domains and assessment of value are not device-specific.
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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 | 0.415 | 0.530 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".