Why Early HTA Should Shape MedTech Development

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The most valuable health-economic model may be the one that changes the development programme before the pivotal study begins.

Odelle Technology | November 2026

Core argument: health economics should not simply judge value at the end of development. Used early, it can help determine what to build, for whom, at what performance level, at what price and with what evidence.

Health economics is often brought in too late

A familiar MedTech sequence runs like this: define the product, lock the intended use, choose the clinical endpoints, design the pivotal study, build the commercial plan – and then ask the health economist whether the resulting technology is likely to be cost-effective.

That sequence can be expensive. By the time a conventional reimbursement model is commissioned, some of the decisions that determine value may already be difficult to change: the target population, comparator, clinical pathway, evidence package, performance specification and expected price.

Early health technology assessment (early HTA) turns the sequence around. Rather than waiting for mature clinical evidence, it uses the best available evidence, explicit assumptions, stakeholder input and decision-analytic modelling to support development decisions while the technology is still changeable.^[1][2]

This is not an attempt to make a reimbursement decision years too early. It is a different decision problem. The question is not yet, “Will a payer reimburse this finished product?” It is, “What would need to be true for this technology to create enough value to justify further development?”

AT A GLANCE Early HTA can define a development envelope before pivotal evidence is locked: where value sits, how much performance is enough, what price is plausible, and which uncertainty deserves the next research pound or euro.

Early HTA changes the questions

Development-focused HTA has been described as distinct from reimbursement-focused HTA because developers face multiple markets and decision makers, incomplete evidence and constrained analytical resources. A practical toolkit therefore combines literature review, stakeholder consultation and decision-analytic modelling, increasing in sophistication as the technology and evidence base mature.^[3]

For an early-stage medical device, diagnostic, digital technology or precision-medicine programme, the most useful questions are often commercial and scientific at the same time:

Is the unmet clinical and economic need large enough to justify development?

Which patient subgroup has the greatest potential value?

What minimum clinical performance would be required?

What target price or cost envelope is plausible?

Which comparator should shape the evidence programme?

Which uncertainty is most likely to change the investment or adoption decision?

What evidence should be generated now, and what can reasonably wait?

Those are not downstream reimbursement questions. They are upstream R&D questions.

Start with headroom, not false precision

One of the simplest and most useful early economic methods is headroom analysis. Its purpose is to estimate the maximum value available to an innovation under a defined decision context – for example, the highest price at which an intervention could remain cost-effective, or the minimum performance that would be needed for the value proposition to work.^[4]

The point is not to pretend that an immature technology has a precise ICER. It is to establish boundaries. If there is very little economic headroom even under favourable assumptions, a company may need to rethink the indication, pathway, product design or development programme. If there is substantial headroom, the analysis can help define the performance and pricing targets that later evidence must support.

Worked example: what headroom analysis can reveal

A 2026 early value assessment provides a useful illustration. In a biomarker-guided treatment model for treatment-resistant hypertension, the authors used threshold and scenario analyses to show not only whether value might exist, but the price and performance envelope within which further development could remain economically plausible. The figures below redraw selected published results; they are an illustration of the method, not forecasts for other technologies.

Figure 1. The modelled scenario used an annual treatment cost of €3,000; threshold analysis estimated a maximum annual price of €5,045 while remaining cost-effective under the stated assumptions. Source: Abu-Zahra et al. (2026).

Figure 2. When treatment and testing costs were included, the reported ICER was €9,875/QALY; a second scenario reported €8,498/QALY. Both were below the €20,000/QALY willingness-to-pay threshold used in the study. Source: Abu-Zahra et al. (2026).

Interpretation matters. The headroom analysis deliberately uses idealised assumptions to identify an upper bound of potential value. These numbers should not be read as a prediction that a future product will achieve these outcomes. Their value is in defining what would need to be true for continued development to make economic sense.

What an early model can change

Early economic question	Development decision	Potential consequence
Where is value concentrated?	Target population / indication	Narrow, broaden or sequence the intended use
How much effect is enough?	Performance specification	Set a minimum clinically and economically relevant target
What price can the pathway sustain?	Target price / cost of goods	Avoid a product-cost structure incompatible with adoption
Compared with what?	Comparator and trial design	Collect evidence against the decision-relevant alternative
What could overturn the decision?	Evidence generation	Prioritise the uncertainties that matter most
Is the opportunity large enough?	Go / no-go / redesign	Stop weak programmes earlier or redirect development

A negative answer can be one of the most valuable outputs

There is a tendency to judge an economic model by whether it produces a favourable result. At the development stage, that is the wrong test.

A well-constructed early HTA may show that a technology cannot plausibly generate sufficient value at the expected price, that the benefit is concentrated in a narrower subgroup, or that the required performance is beyond the likely capability of the product. That can be commercially uncomfortable – but economically useful.

The value of that conclusion is the cost of the development programme it prevents, redirects or improves. Reviews of early HTA frameworks explicitly position the approach as a way to optimise the value proposition and inform go/no-go decisions at preclinical and early clinical stages.^[5]

For boards and investors, this changes the role of health economics. It becomes part of capital allocation, not merely a document required for market access.

Evidence generation is an investment problem

Early-stage evidence is always incomplete. The aim should not be to eliminate uncertainty before development – that is impossible. The aim is to identify which uncertainties matter enough to justify spending money to reduce them.

That means separating evidence that is scientifically interesting from evidence that is decision-changing. A parameter deserves priority when a plausible change in that parameter could alter the preferred target population, price, product specification, development decision or adoption case.

This is where early health economic modelling can be especially powerful. By making the decision structure explicit, it can show whether the next research pound, euro or dollar is better spent on clinical effectiveness, diagnostic accuracy, workflow time, resource use, quality of life, adverse events, uptake, durability or another source of uncertainty.

The same principle sits behind development-focused HTA: evidence generation should be aligned with the decisions that developers, health systems and ultimately patients need to make.^[6]

Why this matters particularly for MedTech and diagnostics

Medical technologies are rarely just products. Their value can depend on where they sit in the pathway, who uses them, how quickly operators learn, which patients are selected, whether they replace or add to existing care, and what downstream capacity they release or consume.

Diagnostics add another layer: value depends not only on test accuracy but on what happens after the result. A more accurate test may still create little value if it does not change management. A modest test can be valuable if it moves the right patients into a better pathway at the right time.

That makes early economic evaluation particularly relevant to technologies whose design is still flexible. The 2023 systematic review of headroom analysis found applications across medicines, diagnostics and screening tests, procedures and systems, and medical devices – evidence that the approach is not confined to pharmaceuticals.^[4]

For MedTech market access teams, the implication is important: the reimbursement strategy should not be a late-stage overlay on a finished technology. The likely payer decision, care pathway and economic value proposition should feed back into development.

The model must be fit for the decision – not impressive for its own sake

There is also a danger in the opposite direction: building an elaborate model too early. When evidence is sparse, complexity can create a misleading sense of certainty.

Recent methodological work on the SMART tool argues for decision models that are as simple as possible, but not simpler: modelling choices should be justified against the decision context, and their consequences for validity and transparency should be explicit.^[7]

That principle is crucial for early HTA. A concept-stage model may need only a small number of states and transparent threshold analyses. As the product matures, the model can incorporate better clinical data, real resource use, adverse events, implementation effects, heterogeneity and longer-term outcomes.

Transparency matters as much as sophistication. CHEERS 2022 provides reporting guidance for health economic evaluations, emphasising clarity about the decision context, analytic approach and findings. It is reporting guidance rather than a scoring system for methodological quality, but the underlying expectation of transparent decision analysis is highly relevant.^[8]

The rule: use the simplest model that can still change the decision. Add complexity when new evidence or a new decision requires it – not because complexity looks more rigorous.

Early use and evidence generation are becoming part of the policy landscape

The direction of travel is visible beyond academic methodology. NICE now describes an “early-use” HealthTech approach, previously called early value assessment, for promising innovations that address NHS need but still require further evidence. Depending on the case, guidance may support use while evidence is generated or recommend research use only.^[9]

That is not the same activity as development-focused early HTA inside a company. But it reinforces a broader point: value assessment and evidence generation are increasingly being treated as lifecycle activities rather than a single reimbursement event at the end of development.

A practical early-HTA sequence for MedTech developers

A useful programme does not require a full reimbursement dossier on day one. It should evolve with the technology.

1. Define the decision. Clarify the target population, current pathway, comparator, decision maker, perspective, time horizon and the development decision that the analysis must inform.

2. Quantify the headroom. Estimate the maximum potential clinical and economic value. Test price, performance, prevalence, workflow and other threshold conditions.

3. Map uncertainty to evidence. Identify the assumptions most capable of changing the decision. Turn them into an evidence-generation plan, not a generic research wish list.

4. Iterate as the product matures. Replace assumptions with observed evidence, refine the pathway, add implementation and resource-use data, and progressively align the model with the intended HTA and reimbursement environment.

The questions boards should ask before the pivotal trial

If the technology works as hoped, is the available value large enough to justify development?

Which patient segment creates the strongest clinical and economic proposition?

What is the minimum effect or performance threshold that makes the product worth adopting?

What price and delivery model remain compatible with value for the health system?

Which comparator will the real decision maker use – and are we collecting evidence against it?

Which one or two uncertainties could still destroy the business case?

Are the planned endpoints capable of demonstrating the outcomes that matter to adoption and reimbursement?

If those questions cannot be answered until after the pivotal study, health economics has arrived too late.

Health economics should help decide what gets built

The conventional image of health economics is an end-stage discipline: calculate costs and QALYs, derive an ICER, prepare the reimbursement argument and defend it to the payer.

That work remains essential. But for MedTech, diagnostics, digital health and precision medicine, some of the highest-value economic work may happen much earlier.

Early health technology assessment can help developers define where value is most likely to exist, what performance is worth pursuing, what price is plausible and which evidence is worth buying. It can expose weak propositions while they are still cheap to change – and strengthen good propositions before expensive development decisions become irreversible.

The goal is not to predict the final reimbursement decision with immature evidence. It is to make better development decisions under uncertainty.

The strongest question may therefore be the simplest one: why wait until the trial is designed to ask whether the technology is being designed for value?

The strategic shift is simple: move health economics upstream far enough that it can still change the product, the evidence programme and the investment decision. Once those choices are fixed, much of the economic value of early HTA has already been lost.

Frequently asked questions

What is early health technology assessment?

Early health technology assessment, or early HTA, applies HTA thinking and methods during technology development. It can combine literature review, stakeholder input and health economic modelling to inform product design, evidence generation, pricing and go/no-go decisions before a mature reimbursement dossier exists.

What is headroom analysis?

Headroom analysis estimates the maximum potential value available to an innovation under a defined set of assumptions. It can be used to explore the highest value-compatible price or the minimum performance needed for a technology to remain potentially cost-effective.

When should a MedTech company start health economic modelling?

Potentially before pivotal trial design. The model should be proportionate to the decision and evidence available. Early models can be simple and threshold-based; they should become more detailed as clinical and resource-use evidence accumulates.

Can early HTA replace a final cost-effectiveness analysis?

No. Early HTA supports development decisions under uncertainty. A mature reimbursement assessment may still require substantially stronger clinical, economic and implementation evidence. The advantage is that early HTA can help make sure the later evidence programme is collecting the right evidence in the first place.

References

1. Grutters JPC, Bouttell J, Abrishami P, et al. Defining early health technology assessment: building consensus using Delphi technique. International Journal of Technology Assessment in Health Care. 2025;41(1):e34. View source

2. IJzerman MJ, Koffijberg H, Fenwick E, Krahn M. Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature. PharmacoEconomics. 2017;35(7):727-740. View source

3. Bouttell J, Briggs A, Hawkins N. A toolkit of methods of development-focused health technology assessment. International Journal of Technology Assessment in Health Care. 2021;37(1):e84. View source

4. Boudewijns EA, Otten TM, Gobianidze M, Ramaekers BLT, van Schayck OCP, Joore MA. Headroom Analysis for Early Economic Evaluation: A Systematic Review. Applied Health Economics and Health Policy. 2023;21(2):195-204. View source

5. Rodriguez Llorian E, Waliji LA, Dragojlovic N, Michaux KD, Nagase F, Lynd LD. Frameworks for Health Technology Assessment at an Early Stage of Product Development: A Review and Roadmap to Guide Applications. Value in Health. 2023;26(8):1258-1269. View source

6. Bouttell J, Briggs A, Hawkins N. The thorny issue of value alignment: how development-focused health technology assessment can help find win-win situations for patients and healthcare systems and commercial investors. International Journal of Technology Assessment in Health Care. 2021;37(1):e57. View source

7. Abu-Zahra T, Grimm SE, Scholte M, Joore M. Can We Make Health Economic Decision Models as Simple as Possible, But Not Simpler? Introducing SMART tool. PharmacoEconomics. 2025;43(10):1235-1250. View source

8. Husereau D, Drummond M, Augustovski F, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMJ. 2022;376:e067975. View source

9. National Institute for Health and Care Excellence. NICE HealthTech programme manual: Early-use HealthTech guidance assessments. Published 2025; updated 2025. View source

10. Abu-Zahra T, Grimm S, Scholte M, Casas AI, Saliba-Gustafsson P, Parini P, Joore M. An Early Value Assessment of Biomarker-Guided Precision Medicine for Treatment-Resistant Hypertension: A Headroom and Threshold Analysis. Applied Health Economics and Health Policy. 2026;24:1093-1108. View source

11. Barton P, Bryan S, Robinson S. Modelling in the economic evaluation of health care: selecting the appropriate approach. Journal of Health Services Research & Policy. 2004;9(2):110-118. View source