Early access schemes for innovative health technologies: the views of international stakeholders
Bibliographic record
Abstract
OBJECTIVES: Early access schemes (EASs) are approaches used by payers to balance and facilitate earlier patient access to innovative health technologies while evidence generation is ongoing. Schemes require investment from payers and are associated with significant risk since not all technologies will be routinely reimbursed. The purpose of this study was to gain the perspectives of policy experts about the key challenges for EASs and potential solutions for their optimal design and implementation. METHODS: Two virtual workshops were convened including (i) UK-based policy experts (England, Wales, and Scotland) and (ii) representatives from multiple healthcare systems (England, France, Sweden, Canada, Poland, and Norway). Participants were encouraged to share their experiences with EASs in their healthcare system and highlight key challenges for policy makers. Discussions were transcribed and analyzed using framework analysis. RESULTS: Participants agreed that EASs have value when targeted toward innovative technologies with the potential for significant clinical benefit in an area of high unmet need. Participants discussed potential solutions to the challenges faced by payers implementing EASs, including defining eligibility criteria, supporting evidence generation, and approaches to reimbursement. CONCLUSIONS: Participants agreed that EASs are one possible solution for their healthcare systems and have the potential to deliver significant clinical value to patients. However, widespread adoption of EASs is limited due to concerns about the risks for patients and healthcare budgets, further solutions are needed to deliver EASs for targeted therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".