Accelerating patient access to oncology medicines with multiple indications in Europe
Bibliographic record
Abstract
Background: In recent years, innovation in oncology has created new challenges for pricing and reimbursement systems. Oncology medicines with multiple indications face a number of access challenges: (1) the number of assessments and administrative burden; (2) aligning price to different values of the same product; (3) managing clinical uncertainty at time of launch; and (4) managing budget uncertainty. These challenges impact a range of stakeholders and can result in delayed patient access to life-saving treatments. Consequently, countries have taken steps to facilitate patient access.Methods: Drawing on the experience across Europe we have reviewed different mechanisms countries have adopted that address these challenges. These include approaches aimed directly at the issue, multi-year-multi-indication (MYMI) agreements (BE, NL), and other approaches to manage access: flexible access agreements for new indications with clinical uncertainty (UK); development of a new agreement for each new indication (IT); and immediate access for new indications and bundled assessments (DE).Results: MYMI agreements are valuable where existing rules mean that every indication faces the same upfront evaluation process that delays patient access. They are also useful in managing budget impact and uncertainty. Other approaches that adopt an indication-specific approach helps manage clinical uncertainty at the time of launch and realise different values for the same product. They can help align price to value, even though indication-based pricing does not exist. Bundled assessments reduce the administrative burden for stakeholders, and the benefits of immediate reimbursement is that patient access is not delayed.Conclusion: The challenges for medicines with multiple indications impact a range of stakeholders and can result in delayed patient access to life-saving treatments. MYMI agreements have created a more pragmatic approach to HTA for medicines with multiple indications to ensure both fast and broad patient access. Continued innovation in oncology will require further innovative approaches in pricing and reimbursement. It is important that policymakers, payers and manufacturers engage in early discussions and are willing to find new solutions to help accelerate patient access to innovative therapies.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".