Hospital-based real-world evidence in health technology assessment: insights from a scoping review of European, Australian and North American guidance and expert interviews
Bibliographic record
Abstract
OBJECTIVES: Hospital data can inform decision-makers with real-time evidence, yet it remains underutilised. This study aims to compare international health technology assessment (HTA) and regulatory real-world evidence (RWE) guidance, focusing on their applicability to hospital data. STUDY DESIGN, SETTING AND PARTICIPANTS: We used a two-step sequential qualitative design: a scoping review and semi-structured interviews with HTA experts. We searched for RWE guidance for HTA in 12 countries: the UK, Germany, Italy, Spain, France, Finland, the Netherlands, Portugal, Denmark, Canada, the US and Australia, along with the European Medicines Agency and Food and Drug Administration. The expert interviews aimed to validate document selection and assess their applicability to hospital data. We analysed the interviews thematically. RESULTS: We identified 19 guidance documents providing recommendations for RWE. Of these, four documents explicitly provided recommendations tailored to hospital data, while two others did so implicitly. The scope, definition and applications of RWE vary among guidance. Recommendations across all agencies mainly address the clinical-effectiveness domain, with limited guidance on quality-of-life and patient-reported outcomes, and none on real-world cost. The interviews identified seven themes playing a role in using hospital data: data-related, generalisability, ethical/legal, organisational, communication, governance and technology-related. Barriers included data availability, access, timeliness, quality, validation and heterogeneity. HTA experts emphasised the need for standardised policies. CONCLUSIONS: There is a lack of harmonisation in assessing RWE among HTA and regulatory agencies. The available RWE guidance documents provide limited guidance on real-world hospital data. Considering their unique nature and to unlock their potential for HTA, we emphasise the need for more in-depth guidance tailored to the hospital context.
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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 | 0.533 | 0.632 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.033 | 0.041 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.008 | 0.007 |
| 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; 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".