Why early HTA, health-economic modelling and payer dialogue should begin before the pivotal trial is finalised

A medical device, diagnostic or digital-health technology can be safe, technically successful and supported by encouraging clinical studies and still fail to secure reimbursement or routine adoption.

This apparent contradiction often arises because the evidence programme has been designed to answer the regulatory question:

Is the technology sufficiently safe, and does it perform as intended?

That is essential, but it is not the same as answering:

Is it better than the care currently provided?
For which patients should it be funded?
Which outcomes matter to patients, clinicians and healthcare systems?
Is the effect sufficiently durable?
Does the technology change clinical management?
Is it affordable to the organisation expected to purchase it?
Does an appropriate code, tariff or funding mechanism exist?
What uncertainty would prevent a payer from supporting its use?

The scientific literature on early health technology assessment, early economic modelling, HTA scientific advice, real-world evidence and lifecycle HTA increasingly supports a different approach.

Evidence for reimbursement should be considered while the technology, target population, comparator, trial and commercial proposition can still be changed, not after development has finished.

What is a payer?

The term “payer” is often used too loosely.

A payer is an organisation responsible for allocating pooled healthcare funds or determining whether healthcare services and technologies will be covered. Depending on the country and setting, this may include:

a national or regional health authority;
a statutory health-insurance fund;
a private insurer;
an NHS commissioner or integrated care organisation;
a government reimbursement body;
or another organisation controlling a healthcare budget.

However, reimbursement rarely depends on one organisation alone.

An HTA body may assess comparative clinical effectiveness and economic value without setting the final price. A national payer may approve coverage while a separate authority establishes the tariff. A hospital may be permitted to use the technology but receive no additional payment for purchasing it. Procurement may negotiate the contract, while clinicians determine whether the technology is actually adopted.

A scientifically defensible reimbursement analysis must therefore distinguish between:

regulators, who assess safety and performance;
HTA assessors, who evaluate comparative value and uncertainty;
payers and commissioners, who determine coverage or funding;
pricing and tariff authorities, which establish payment;
providers and hospital budget holders, which incur local costs;
procurement teams, which contract for products;
and clinicians, whose decisions determine actual use.

“Speaking to the payer” should not mean finding one broadly relevant insurance executive and requesting an opinion.

It means identifying the people and organisations with the specific decision rights relevant to the technology.

The scientific foundation: early HTA

An international HTAi working group recently defined early HTA as an assessment undertaken to inform development, research or investment decisions by evaluating the potential value of a health technology and the uncertainty surrounding that value.

The consensus process emphasised that early HTA is not limited to an early cost-effectiveness calculation. It may examine:

clinical need;
technology design;
safety and effectiveness;
positioning within the care pathway;
organisational effects;
ethical and social issues;
affordability;
commercial viability;
future research;
and likely adoption.

The definition was developed through an international Delphi process, making it one of the strongest current methodological foundations for this work (Grutters et al., 2025).[1]

Earlier work by IJzerman, Steuten and colleagues described early HTA as a group of methods used to inform developers and other stakeholders about the potential value of technologies still in development, including methods for quantifying and managing uncertainty.[2,3]

A review of early-HTA frameworks subsequently found that the process may combine:

literature review;
clinical pathway analysis;
health-economic modelling;
stakeholder preference research;
uncertainty analysis;
and iterative reassessment at different stages of development.

The authors concluded that early HTA can help optimise a technology’s value proposition and support development and go/no-go decisions, but also noted that the methodology must be adapted to the maturity of the technology and the particular decision being considered (Rodriguez Llorian et al., 2023).[4]

This is an important point.

A prototype should not be assessed as though it were a completed reimbursement submission. The purpose at that stage may be to determine:

whether the clinical problem is sufficiently important;
whether the proposed mechanism could create meaningful value;
which care setting should be targeted;
and what evidence would justify continued investment.

At proof of concept, the questions change. Before the pivotal trial, they change again.

Early Technology Review: connecting evidence development with payer expectations

Levin and colleagues proposed an Early Technology Review process specifically intended to connect early evidence assessment with the requirements of payers and health professionals.[5]

Their work started from a recurring problem in medical-device development: studies may be designed predominantly around regulatory requirements, with insufficient attention to the comparative evidence later required for coverage and adoption.

The process combined:

structured review of the available evidence;
PICO analysis of population, intervention, comparator and outcomes;
identification of evidence gaps;
health-economic considerations;
and engagement with payers and clinicians.

The objective was not to obtain an informal prediction of reimbursement.

It was to determine whether the proposed evidence programme was likely to answer the questions that future decision-makers would ask.

A 2026 follow-up proposed that these reviews should be sequential, taking place during prototype development, proof of concept and later development stages rather than as a single assessment.[6]

This reflects the reality of healthcare innovation. The product, evidence, clinical pathway and commercial assumptions all change as development progresses.

Why medical devices require particular care

Economic evaluation methods originally developed largely around pharmaceuticals cannot always be transferred to devices without modification.

Drummond, Griffin and Tarricone highlighted several distinctive features of medical devices:

outcomes may depend on clinician experience;
learning curves can affect comparative results;
technologies may undergo repeated incremental modifications;
prices may change rapidly;
outcomes may vary between centres;
and the device may alter the organisation and location of care.[7]

Tarricone and colleagues subsequently examined economic evaluations of transcatheter aortic valve implantation and implantable cardioverter-defibrillators.

They found that important device characteristics were frequently inadequately handled. For example, learning effects, dynamic pricing and organisational consequences were often recognised but rarely incorporated fully into analyses.[8]

The implication is not that medical devices cannot be evaluated economically.

It is that the model must represent the way the device is actually introduced and used.

A technology may appear expensive when assessed only as an additional product cost but economically attractive when its effects on theatre time, length of stay, complications, staff requirements or downstream treatment are included.

The reverse can also occur. A technology may appear cost-saving at health-system level while remaining unaffordable to the hospital expected to purchase it.

A scientific Odelle Evidence-to-Reimbursement process

Odelle’s approach can be formalised as an eight-stage process.

Stage 1: Define the decision problem

The first step is not a broad literature search.

It is a precise description of the decision that the developer ultimately wants the health system to make.

The core clinical structure is PICO:

Population
Intervention
Comparator
Outcomes

For reimbursement, Odelle extends this into a PICO-plus framework that also defines:

the intended care setting;
the clinical decision changed by the technology;
the relevant payer or commissioner;
the provider expected to purchase it;
the budget that bears the cost;
the likely coding and payment mechanism;
the proposed price;
and the uncertainty currently preventing reimbursement or adoption.

This avoids one of the most common market-access errors: conducting technically good research against a comparator that the payer does not recognise.

Stage 2: Establish the evidence baseline

A reproducible review should examine:

clinical effectiveness;
safety;
durability;
patient-reported outcomes;
quality of life;
clinical utility;
resource utilisation;
economic evidence;
implementation;
and generalisability to the intended healthcare setting.

Formal sources may include MEDLINE, Embase, the Cochrane Library, clinical-trial registries, HTA reports, regulatory assessments, guidelines, registries and routinely collected healthcare data.

Google Scholar and ResearchGate can help identify newly indexed papers, conference material, preprints and author-posted manuscripts, but they should supplement rather than replace controlled bibliographic searching.

The result should not be a collection of everything ever published about the technology.

It should be an evidence-to-decision map showing:

which claims are supported;
which claims depend on indirect evidence;
which outcomes remain unmeasured;
which studies are at material risk of bias;
which findings may not transfer across healthcare systems;
and which gaps could prevent reimbursement.

Unfavourable and null findings must be included. A selective review may support marketing, but it cannot support a credible payer or HTA strategy.

Stage 3: Construct an early economic model

The purpose of an early model is not to manufacture a favourable cost-effectiveness result from immature data.

Its purpose is to make the decision logic and uncertainty explicit.

The model can ask:

What clinical effect would be required for the technology to provide value?
Which patient subgroup has the greatest potential benefit?
What price could the pathway sustain?
How long must the benefit last?
Which complications or procedures would need to be avoided?
Which assumptions have the greatest influence on the result?
What additional research would reduce decision uncertainty most efficiently?

Grutters and colleagues argue that early health-economic models should be used as exploratory tools to improve technologies and care pathways, not merely as late-stage reimbursement calculators.[9]

Appropriate methods may include:

Headroom analysis

This estimates the maximum additional cost that might be justified if the technology delivered its proposed benefit.

Threshold analysis

This identifies the minimum treatment effect, reduction in resource use or duration of benefit required for the technology to become economically credible.

Sensitivity and scenario analysis

These test whether the result remains plausible under alternative assumptions concerning price, uptake, effectiveness, learning effects and healthcare setting.

Value-of-information analysis

This examines whether additional research is worth undertaking and which uncertainty should be prioritised.

The model should inform the clinical and commercial plan, rather than being developed after the plan is already fixed.

Stage 4: Conduct structured payer and clinician research

Stakeholder engagement should be approached as structured qualitative research.

Participants should be selected because they represent relevant decision roles, potentially including:

payer or commissioner advisers;
HTA experts;
hospital finance leaders;
coding specialists;
procurement professionals;
clinical leaders;
and patient representatives.

The interviews should address explicit uncertainties:

Is the proposed population credible?
Is the comparator relevant?
Would the selected endpoint influence a funding decision?
Is the follow-up sufficient?
What evidence would be regarded as indirect?
Where would the cost fall?
Would an existing payment mechanism cover it?
What affordability or implementation barriers would remain?
What result would cause the decision-maker to reject the proposition?
Could conditional coverage or a managed evidence programme be considered?

A systematic review of early-dialogue frameworks found considerable variation among HTA organisations, but supported the value of early advice in helping developers align research with health-system evidence needs.[10]

Clinician involvement is also scientifically important. A systematic review found that clinicians can contribute throughout medical-device development, including need identification, prototype development, evaluation and implementation planning.[11]

However, payer and clinician interviews must be interpreted correctly.

They are not clinical-effectiveness evidence, and an interviewee cannot normally commit an entire organisation to a future decision.

They provide evidence about:

decision criteria;
evidentiary expectations;
practical objections;
pathway feasibility;
and likely implementation barriers.

Findings should be coded systematically, compared across participants and triangulated against published methods, coverage policies and reimbursement rules.

Stage 5: Correct the evidence-development programme

The combined evidence review, model and stakeholder research should lead to specific changes where required.

These may concern:

target population;
inclusion and exclusion criteria;
comparator;
primary endpoint;
quality-of-life measurement;
follow-up;
sample size;
collection of resource-use data;
subgroup analysis;
registry architecture;
clinical sites;
or post-market evidence.

This is the point at which early HTA produces value.

A report that merely identifies problems is incomplete. The output should change the development decision.

Stage 6: Connect value to payment

An economically attractive technology can still lack a viable payment route.

Odelle therefore examines:

applicable procedure and product codes;
inpatient and outpatient tariffs;
additional-payment mechanisms;
national and regional HTA;
local commissioning;
insurance benefit categories;
hospital procurement;
and innovation-funding routes.

The analysis must identify who incurs the cost and who receives the benefit.

For example, a device may reduce readmissions for the payer but require an immediate unfunded investment by the hospital. A diagnostic may reduce unnecessary treatment in another departmental budget. A digital intervention may create savings after the contract period has ended.

This budget misalignment cannot be resolved by reporting a favourable cost per QALY alone.

It may require:

a different payment mechanism;
a bundled contract;
risk sharing;
central funding;
a pathway redesign;
or coverage linked to further evidence.

Stage 7: Use real-world evidence deliberately

Real-world evidence should begin with a defined payer or HTA question.

Facey and colleagues found that stakeholders often lacked clarity about which payer questions real-world data could answer and how the credibility of the resulting evidence should be judged.[12]

Useful real-world questions may include:

How durable is the treatment effect?
Are outcomes reproduced outside specialist centres?
What happens in patients underrepresented in trials?
Are rare adverse events emerging?
Does the technology change clinical management?
What resources are actually used?
Does hospital performance improve after implementation?

The evidence source must be selected to answer the question—not because a registry or database happens to be available.

Stage 8: Maintain a lifecycle assessment

Evidence and reimbursement conditions do not remain static.

New comparators enter the market. Prices change. Devices are modified. Clinical learning improves outcomes. Guidelines and payment rules evolve.

Lifecycle HTA has been defined as a systematic use of sequential HTA activities where the evidence, technology or context may change meaningfully over time.[13]

Living HTA applies similar principles to continuing evidence surveillance and reassessment.[14]

For Odelle, this means establishing:

scheduled literature surveillance;
trial-registry monitoring;
competitor evidence tracking;
HTA and payer-decision monitoring;
coding and tariff surveillance;
and predefined triggers for revisiting the strategy.

Not every new paper should trigger a full rewrite.

A reassessment is warranted when new information could materially change:

the patient population;
comparator;
treatment effect;
safety;
economic model;
coding;
reimbursement route;
price;
or adoption recommendation.

How Odelle helps

Odelle has been applying many of these principles in practical market-access work across MedTech, diagnostics, digital health, biotechnology and pharmaceuticals.

The formal Odelle Evidence-to-Reimbursement Review would combine:

Decision definition
The population, comparator, outcome, setting, payer, budget holder and required funding decision.
Evidence and claims audit
What is established, uncertain, unsupported or potentially overstated.
Early economic analysis
Headroom, thresholds, affordability, resource use and the principal drivers of value.
Structured payer and clinician research
Testing whether the evidence proposition corresponds to real decision requirements.
Country-specific payment mapping
Connecting evidence with coding, HTA, coverage, tariff, procurement and provider economics.
Corrected evidence-development programme
Specific changes to trials, endpoints, patient selection, registries and economic-data collection.
Managed-access options
Pilot programmes, reference centres, coverage with evidence development and risk-sharing structures where appropriate.
Lifecycle surveillance
Updating the assessment when new evidence or reimbursement developments are material.

Coverage with evidence development may be useful when a technology is promising but important uncertainty remains. European research has shown, however, that these schemes work properly only when the uncertainty, research plan, funding conditions and reassessment process are explicitly connected.[15]

Odelle should not promise that payer engagement guarantees reimbursement.

It should promise something more scientifically defensible:

The evidence, economic argument and payment strategy will be tested against the questions that real healthcare decision-makers are likely to ask—while there is still time to improve them.

Conclusion

The pivotal trial is not merely a regulatory exercise. It is one of the largest investments a healthcare-technology company will make in its future reimbursement proposition.

If the wrong population, comparator or outcomes are selected, that investment may produce evidence that is scientifically respectable but commercially unusable.

Early HTA provides the scientific framework for assessing potential value and uncertainty during development.

Early economic modelling makes the assumptions, thresholds and research priorities explicit.

Structured payer and clinician dialogue tests whether the proposition reflects real decision requirements.

Medical-device HTA methods address learning curves, organisational effects, incremental innovation and dynamic pricing.

Real-world evidence and lifecycle HTA provide a route for resolving uncertainty after early adoption.

Odelle connects these academic methods with the practical mechanisms that determine whether funded use occurs:

evidence, claims, payer requirements, coding, payment, hospital affordability, implementation and adoption.

The central question is therefore not:

Does the company have enough evidence?

It is:

Has the company generated the right evidence, for the right population, against the right comparator, to support a real reimbursement and purchasing decision?

References

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. https://doi.org/10.1017/S0266462325100123
IJzerman MJ, Steuten LMG. Early assessment of medical technologies to inform product development and market access: a review of methods and applications. Applied Health Economics and Health Policy. 2011;9(5):331–347. https://doi.org/10.2165/11593380-000000000-00000
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. https://doi.org/10.1007/s40273-017-0509-1
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. https://doi.org/10.1016/j.jval.2023.03.009
Levin L, Sheldon M, McDonough RS, et al. Early technology review: towards an expedited pathway. International Journal of Technology Assessment in Health Care. 2024;40(1):e13. https://doi.org/10.1017/S0266462324000047
Levin L, McDonough RS, Cheatle M, Kaafarani H, Korjian S, Kuntz RE. Early technology review during prototype development and at proof of concept: the case for developing a sequential versus a single-stage approach to early evidence development for health technologies. International Journal of Technology Assessment in Health Care. 2026;42(1):e40. https://doi.org/10.1017/S0266462326103596
Drummond M, Griffin A, Tarricone R. Economic evaluation for devices and drugs—same or different? Value in Health. 2009;12(4):402–404. https://doi.org/10.1111/j.1524-4733.2008.00476_1.x
Tarricone R, Callea G, Ogorevc M, Prevolnik Rupel V. Improving the methods for the economic evaluation of medical devices. Health Economics. 2017;26(Suppl 1):70–92. https://doi.org/10.1002/hec.3471
Grutters JPC, Govers T, Nijboer J, Tummers M, van der Wilt GJ, Rovers MM. Problems and promises of health technologies: the role of early health economic modeling. International Journal of Health Policy and Management. 2019;8(10):575–582. https://doi.org/10.15171/ijhpm.2019.36
Ibargoyen-Roteta N, Galnares-Cordero L, Benguria-Arrate G, et al. A systematic review of the early dialogue frameworks used within health technology assessment and their actual adoption from HTA agencies. Frontiers in Public Health. 2022;10:942230. https://doi.org/10.3389/fpubh.2022.942230
Smith V, Warty R, Nair A, et al. Defining the clinician’s role in early health technology assessment during medical device innovation: a systematic review. BMC Health Services Research. 2019;19:514. https://doi.org/10.1186/s12913-019-4305-9
Facey KM, Rannanheimo P, Batchelor L, Borchardt M, de Cock J. Real-world evidence to support payer/HTA decisions about highly innovative technologies in the EU: actions for stakeholders. International Journal of Technology Assessment in Health Care. 2020;36(4):459–468. https://doi.org/10.1017/S026646232000063X
Pichler FB, Boysen M, Mittmann N, et al. Lifecycle HTA: promising applications and a framework for implementation. International Journal of Technology Assessment in Health Care. 2024;40(1):e50. https://doi.org/10.1017/S0266462324000187
Thokala P, Srivastava T, Smith R, et al. Living health technology assessment: issues, challenges and opportunities. PharmacoEconomics. 2023;41(3):227–237. https://doi.org/10.1007/s40273-022-01229-4
Drummond M, Federici C, Reckers-Droog V, et al. Coverage with evidence development for medical devices in Europe: can practice meet theory? Health Economics. 2022;31(Suppl 1):179–194. https://doi.org/10.1002/hec.4478
Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. 4th ed. Oxford: Oxford University Press; 2015. Publisher record

How to Design Clinical Evidence That Payers Can Use 2026