What Is HEOR and Why Every Medtech Startup Needs It Before Launch

Most medtech founders meet health economics and outcomes research the same way: a hospital value analysis committee asks for a budget impact model, and nobody on the team has one. By then the device is cleared, the sales team is hired, and the evidence window that would have produced a credible answer closed eighteen months ago.

HEOR is not an academic add-on. It is the discipline that answers the question every buyer asks after the regulator has said yes: what does this cost, what does it change, and who captures the saving. Clearance proves your device is safe and effective. HEOR proves it is worth paying for. Those are different burdens of proof, judged by different people, on different timelines.

What HEOR actually covers

Health economics and outcomes research bundles several related activities that founders often treat as one thing.

Clinical outcomes research measures what happens to patients in real use, not just in the pivotal study. Length of stay, readmissions, complication rates, time to diagnosis, avoided procedures. These are the endpoints payers care about, and they are frequently not the endpoints your regulatory submission needed.

Economic modelling translates those outcomes into money. A cost-effectiveness analysis expresses the trade-off as cost per unit of health gained, usually a quality-adjusted life year. A budget impact model answers a narrower and often more persuasive question: if this hospital or this plan adopts the device for its eligible population, what happens to the budget in year one, year two, year three.

Patient-reported outcomes and real-world evidence fill the gaps that trials leave. HTA bodies in Europe increasingly expect both. So do sophisticated US integrated delivery networks.

Value communication is the last piece: the dossier, the value story, the one-page economic summary a sales rep can leave behind. Good analysis that nobody can read does not move a purchasing decision.

Why it has to start before launch

The expensive mistake is sequencing. Teams run the clinical study the regulator needs, get cleared, then discover that the study collected no economic endpoints, used a comparator payers do not recognise, and enrolled a population narrower than the one they want to sell into.

Adding economic endpoints to a study you are already running costs very little. Reconstructing them afterwards means a second study, new sites, and another two years. If you are still planning your submission strategy, read our guide to FDA regulatory pathways alongside your evidence plan rather than after it.

The second reason is that coverage takes longer than clearance. In the US there is no automatic route from an FDA decision to payment. Coding, coverage and payment are three separate processes, each with its own evidence appetite. We wrote about that lag in Medicare coverage after FDA clearance, and HEOR is the main lever a small company has to shorten it.

What HTA bodies and payers actually ask for

Outside the US, the ask is explicit. NICE in England publishes a programme manual describing how HealthTech is assessed, including early-use assessments for promising technologies with incomplete evidence. Germany's DiGA fast track gives digital applications a provisional listing on the condition that a positive healthcare effect is demonstrated within twelve months. France's HAS assessment works the same way: a structured dossier, a named comparator, a defined population.

In each of those systems the questions are predictable. Who is the comparator, and is it current standard of care rather than a straw man. What is the incremental benefit, stated numerically. What is the uncertainty around that benefit. What does adoption cost the system, including training, integration and disposables. Who bears the cost and who receives the benefit, because if those are different budgets the case is harder.

US payers ask the same questions in less formal language. A value analysis committee wants a budget impact model with assumptions it can argue with. A commercial plan wants to know whether the device displaces a service it already pays for.

For a market-by-market view of what each system expects, our medical device market access guide sets out the sequence, and international reimbursement pathways compares the German and French routes directly.

A proportionate HEOR plan for a small company

You do not need a health economics department. You need four things, in order.

A target product profile that states the claim you intend to make and the population you intend to make it in. Everything downstream is built from this. If the claim changes, the evidence changes.

An early economic model, built in a spreadsheet, long before you have data. Populate it with literature estimates and your own assumptions. Its purpose is not precision. Its purpose is to tell you which variable the value case is most sensitive to, so you can measure that variable in your study. Very often the answer surprises the founding team.

Endpoints embedded in the clinical work you are already funding. Resource use, staff time, downstream procedures, readmissions. Cheap to collect prospectively, expensive to reconstruct.

A post-launch real-world evidence plan, including registry participation or a structured data agreement with your first sites. Provisional listings in Europe and coverage-with-evidence arrangements in the US both run on this.

Where founders get it wrong

Three patterns repeat. Modelling the wrong comparator, usually because the team benchmarked against an older technology rather than what clinicians currently do. Claiming a societal saving when the buyer only controls a departmental budget. And treating cost-effectiveness as a marketing exercise rather than an analysis that has to survive an independent reviewer who is looking for the weak assumption.

The last one matters most now. Independent assessors are more active than they were three years ago, and a value story that cannot withstand scrutiny is worse than none, because it becomes the thing reviewers cite.

What to do next

Write your target product profile this quarter. Build a rough budget impact model before your next clinical protocol is locked. Identify, for each launch market, which body decides value and what format it expects.

MedTech Compass scores markets on reimbursement friendliness and pathway complexity together, so you can see where an economic dossier is the binding constraint and where regulatory timing is. If your documentation and change control also need structuring, DevicePath handles the regulatory side.

Evidence is cheaper when it is planned and expensive when it is retrofitted. That is the whole argument for starting HEOR before launch.

Sources

1. ISPOR — About Health Economics and Outcomes Research: https://www.ispor.org/heor-resources/about-heor 2. NICE — HealthTech programme manual, early-use assessments: https://www.nice.org.uk/process/pmg48/chapter/early-use-healthtech-guidance-assessments 3. NICE — Health technology evaluations manual (PMG36): https://www.nice.org.uk/process/pmg36 4. BfArM — The Fast-Track Process for Digital Health Applications (DiGA): https://www.bfarm.de/SharedDocs/Downloads/EN/MedicalDevices/DiGA_Guide.html 5. HAS — Digital medical devices assessment: https://www.has-sante.fr/jcms/p_3118283/en/digital-medical-devices 6. CMS — Medicare Coverage Database: https://www.cms.gov/medicare-coverage-database/search.aspx

This article is general information about evidence and reimbursement practice, not legal, regulatory or financial advice.