When health technology assessment (HTA) systems were created, they were designed for a different era. The model assumed large, randomised trials, broad populations, and incremental innovation. Today, personalised medicine is rewriting that script. We are moving from drugs for thousands to therapies for dozens, sometimes even one. Evidence models that once defined rigour now face the risk of excluding the very innovations that could transform care.
It’s not about whether the HTA should change, but rather, the method. The goal remains the same: ensuring that public resources are used responsibly. What must change is how we define value, measure uncertainty, and manage evidence over time.
The Scale Problem
Traditional HTA frameworks reward statistical certainty. Large numbers make outcomes predictable and cost-effectiveness models stable. But in personalised medicine, patient numbers are often small, and long-term outcomes take years to emerge. The result is a structural tension between innovation and proof. Regulators and payers find themselves evaluating therapies that are scientifically compelling but statistically thin.
This is not a flaw in the therapies; it’s a flaw in the model. Expecting 1990s evidence from 2020s science makes progress impossible. HTA needs to learn to handle uncertainty, not reject it, via conditional reimbursement, real-world follow-up, and adaptive re-evaluation.
Adaptive Assessment
Some countries are experimenting with adaptive pathways, linking reimbursement to ongoing evidence generation. This model allows access while data matures. It turns the reimbursement process into a learning system rather than a binary decision.
In oncology, this approach has allowed patients to access promising targeted therapies earlier while generating real-world data to refine value estimates. The lesson is simple: flexibility does not mean recklessness. It means recognising that evidence evolves.
The Broader Value Question
Personalised medicine also challenges the narrowness of current cost-effectiveness models. Traditional metrics like cost per QALY struggle to capture societal and diagnostic benefits — early detection, reduced uncertainty, and long-term system learning. These effects are real but rarely modelled.
Reform must therefore broaden the definition of value to include innovation spillovers and patient-centred outcomes. HTA should reward therapies that reduce future system costs through precision, not just those that deliver immediate gains.
Shared Accountability
The future of assessment will require new partnerships. Regulators, HTA bodies, and manufacturers must collaborate earlier to align data expectations. Joint scientific advice, parallel submissions, and shared data infrastructures can reduce duplication and accelerate decisions.
Patient organisations will also play a growing role. Their lived experience provides context that no model can replicate. Integrating their perspectives ensures that “value” reflects what matters to people, not just what fits into spreadsheets.
The Bottom Line
Personalised medicine demands personalised assessment. HTA must shift from a culture of exclusion to a culture of learning. The systems that succeed will be those that embrace uncertainty intelligently, value outcomes broadly, and foster collaboration across stakeholders.
Evidence-based does not have to mean evidence-delayed.
Key Takeaways
- HTA models must evolve to accommodate smaller populations and adaptive evidence.
- Flexibility in reimbursement does not compromise rigour.
- Broader definitions of value are essential for precision therapies.
- Collaboration across regulators, payers, and patients accelerates progress.
- Managing uncertainty is now a strategic capability.
Try This
Map one of your assets against current HTA expectations. Identify which evidence requirements are designed for large-scale trials. Next, suggest flexible solutions, like physical follow-ups or phased submissions, for contemporary science.
Closing Thought
Share this with colleagues in access or policy. Reforming HTA is not about lowering standards; it’s about keeping innovation accessible. The future of assessment must match the future of medicine.
