SciTransfer
HTx · Project

Smarter Tools to Decide Which Health Treatments Actually Work and Are Worth Paying For

healthTestedTRL 5
htx-h2020.eu

Imagine you're a health insurance company or a government agency deciding whether to pay for an expensive new cancer drug. Right now, those decisions rely heavily on clinical trial data — but clinical trials only test treatments on carefully selected patients in ideal conditions. HTx built statistical models, AI tools, and a web application that combine clinical trial evidence with real-world patient data, so decision-makers can see how treatments actually perform in everyday hospitals, for specific types of patients, and at what cost. Think of it as moving from reading restaurant reviews to checking your own friends' actual dining experiences before choosing where to eat.

By the numbers
EUR 9.6M
EU funding for development of next-generation HTA tools
17
consortium partners across Europe
11
countries represented in testing and validation
39
total project deliverables produced
11
demonstration deliverables including software and reports
The business problem

What needed solving

Health systems across Europe spend billions on new treatments without reliable tools to predict whether those treatments will actually work for real patients outside clinical trials. Current Health Technology Assessment methods rely too heavily on controlled trial data, leaving payers and insurers guessing about real-world cost-effectiveness — especially for personalised and combination therapies. This leads to either overpaying for treatments that underperform or blocking access to genuinely effective therapies.

The solution

What was built

HTx produced open-access statistical prediction models for forecasting treatment health outcomes and costs in real-world settings, a suite of network meta-analysis models for estimating real-world effectiveness, econometric models for cost-effectiveness analysis using real-world data, AI-assisted decision-making tools for clinicians and patients, a web application for selecting patient-reported outcome measures (PROMs), and policy sandbox testing of shared decision-making and payment models. All validated through clinical case studies including myelodysplastic syndrome.

Audience

Who needs this

National HTA agencies and reimbursement authorities deciding on drug coverageHealth insurance companies evaluating cost-effectiveness of new treatmentsPharmaceutical companies preparing HTA dossiers for market accessHealth data analytics firms building clinical decision-support platformsHospital networks implementing evidence-based treatment protocols
Business applications

Who can put this to work

Health Insurance & Payers
enterprise
Target: Health insurance companies and national reimbursement agencies

If you are a health insurer struggling to decide which new treatments deserve coverage — this project developed statistical prediction models and cost-effectiveness analysis tools that combine clinical trial data with real-world patient outcomes. These open-access models help you forecast treatment costs and health results for specific patient groups, reducing the risk of paying for therapies that don't deliver value outside controlled trial settings.

Pharmaceutical & Biotech
enterprise
Target: Pharma companies seeking faster market access for complex therapies

If you are a pharmaceutical company facing long delays getting your personalised treatments approved for reimbursement — HTx developed methods that integrate real-world data with RCT evidence, aligned with EUnetHTA guidelines across 11 countries. These tools help you build stronger, evidence-based dossiers that address what HTA bodies actually need, potentially shortening time-to-market for complex combination therapies.

Health IT & Analytics
any
Target: Health data analytics companies and clinical decision support vendors

If you are a health IT company building clinical decision-support tools — HTx produced open-access software for network meta-analysis, treatment outcome prediction, and a web application for selecting patient-reported outcome measures (PROMs). With 39 deliverables including AI-assisted decision-making reports, these validated methods and code can be integrated into your platforms to offer evidence-based treatment comparison features.

Frequently asked

Quick answers

What would it cost to implement these HTA tools in our organisation?

The statistical models and network meta-analysis software were developed as open-access code, meaning the software itself is freely available. Implementation costs would depend on integration complexity, data infrastructure, and staff training. With 17 consortium partners having tested the methods across 11 countries, deployment pathways are relatively well-documented.

Can these tools work at the scale of a national health system?

The project was designed for European-wide application, tested across 11 countries, and developed in close collaboration with EUnetHTA — the official European network for health technology assessment. The transferability of results into all EU Member Countries, especially Central and Eastern European countries, was an explicit project goal.

What is the IP situation — can we use or license these tools?

Key deliverables include open-access software code for the statistical prediction model and network meta-analysis suite. The web application for selecting PROMs was also developed as a deliverable. Specific licensing terms should be confirmed with the coordinator at Universiteit Utrecht, but the open-access designation suggests permissive use.

How does this align with current EU health regulations?

HTx was built in direct collaboration with EUnetHTA and aimed to translate its methods into existing European HTA guidelines. With the EU's new Health Technology Assessment Regulation (effective 2025), the tools and methods developed here are directly relevant to the standardised HTA processes now being rolled out across Member States.

What evidence exists that these tools actually work?

The project conducted concrete case studies including myelodysplastic syndrome (MDS), producing reports on data synthesis from RCTs and real-world data for MDS patients. Policy sandbox events were used to test shared decision-making methods and payment models in realistic conditions.

Can these tools integrate with our existing health data systems?

The deliverables include econometric models for estimating treatment effects, risks, costs, and benefits from real-world data, plus a web application for PROMs selection. The open-access software code is designed to work with standard health datasets. Integration with proprietary systems would require technical adaptation by your team.

Consortium

Who built it

The HTx consortium of 17 partners across 11 countries is heavily academic, with 10 universities forming the core — which is typical for methodology-focused health research. The 2 industry partners and 2 SMEs (12% industry ratio) suggest the tools were built by researchers for the HTA community rather than for direct commercial sale. The coordinator, Universiteit Utrecht in the Netherlands, is a well-established health economics research hub. The geographic spread across Western, Northern, and some Eastern European countries (including Bulgaria and Hungary) supports the project's stated goal of EU-wide transferability. For a business looking to adopt these tools, the academic strength means high methodological rigour, but you may need to invest in translating the outputs into production-grade software.

How to reach the team

Universiteit Utrecht, Netherlands — likely the health economics or public health department. SciTransfer can help identify the right contact.

Order a report on this consortiumSee UNIVERSITEIT UTRECHT profile →
Next steps

Talk to the team behind this work.

Want to explore how HTx's open-access HTA tools could improve your reimbursement decisions or market access strategy? SciTransfer can arrange a direct introduction to the research team and help translate their methods for your business context.

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