Use of real-world data and evidence for medical devices: a qualitative study of key informant interviews
Bibliographic record
Abstract
INTRODUCTION: Health Canada is committed to the modernization of the use of real-world data (RWD) and evidence (RWE) to support regulatory decisions. As such, telephone interviews with stakeholders, including government decision makers, health technology assessment (HTA) producers, industry, and patients, to understand their experiences with and perspectives on how to enhance RWE use for medical devices were performed. METHODS: Thirty-four semi-structured telephone interviews with forty key informants were conducted. Transcripts were reviewed independently by one individual to identify, define, and categorize key concepts and were verified by a second reviewer. KEY FINDINGS: There are expectations for Health Canada to provide a framework and guidance on RWE use, identify relevant outcomes for data collection and criteria for data quality, conduct post-market surveillance more systematically, and partner with HTA organizations to develop methods for RWE generation. Stakeholders interviewed support the RWE use for regulatory decisions and HTA recommendations. Moreover, robust scientific methods for RWE generation will be critical to ensure that relevant questions are asked and rigorous statistical analyses are done to answer them. Patients are likely to consent to share their anonymized or de-identified medical information for nonprofit purposes. CONCLUSIONS: Key concepts from the interviews centered on the current and future RWE use for medical devices, considerations for the organizational, medical, scientific, and legal aspects and privacy issues of RWD collection or RWE generation, and options to implement the use of RWD and RWE. Our study findings will help inform the development of an RWE framework for regulatory decisions and HTA recommendations.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".