Across health economics and outcomes research (HEOR), expectations for transparency and reproducibility are increasing. As a result, open-source models (OSMs) are gaining attention as a potential way to improve both collaboration and efficiency. The principle is simple: share health economic models openly, complete with code, assumptions, and licensing, soothers can review, reuse, and improve them.

Yet while the promise of open-source modelling is clear, openness alone does not guarantee that a model is ready for real-world decision-making. In a discipline where small methodological choices can shift results and influence policy decisions, the question is not simply how to find open models, but how to ensure they are credible, usable, and fit for purpose.

What open-source models offer

Health economic models are the backbone of evidence-based decision making in healthcare systems. They inform pricing, reimbursement, and value demonstration for new therapies across different countries and healthcare settings. By making these models public, the OSM movement aims to:

In theory, OSMs can drive faster, more consistent, and more reproducible HEOR outputs, helping improve both transparency and efficiency. However, the transition from open access to practical application is not always straightforward.

The gap between access and application

In practice, many OSMs fall short of their potential. Published models vary widely in quality, completeness, and documentation. Without clear information, a promising model can quickly bring more problems than benefits.

For organisations seeking to reuse these models, particularly pharmaceutical companies preparing evidence, this variability can introduce significant uncertainty.

A model that functions technically may still fall short of the standards required for informed decision-making. Missing documentation, unclear licensing, or a lack of validation can limit a model’s usefulness and introduce risk.

In our exploration of the current OSM landscape, one theme emerged consistently: there is strong interest in a more centralised hosting platform for open models, but also a clear need for stronger quality assurance. Information about licensing terms, validation status, version control, and model governance is often incomplete, making it difficult to determine whether a model is suitable for adaptation or reuse.

This variability highlights an important point: transparency alone does not guarantee reliability.

Why validation still matters

Transparency is valuable, but trust in modelling comes from validation. OSMs require scrutiny to confirm that their structure, assumptions, and outputs align with accepted standards of economic modelling.

Independent validation helps teams answer critical questions:

• Are the model’s equations correctly implemented?
• Do the results make logical sense? Are they plausible?
• Are inputs and assumptions consistent with current evidence and data sources?
• Is it suitable for adaptation to a new setting or decision context?

What we’ve learned from OSMs

As part of a recent internal initiative, Symmetron explored and reviewed the OSM landscape, identifying models across multiple disease areas and platforms. This exercise highlighted both what is common with OSMs and where they frequently fall short in practice. Many open models are hosted on platforms such as GitHub or institutional repositories, often alongside academic publications. These include a range of model types, from Markov models and decision trees to more complex partitioned survival and simulation frameworks.

Common issues we observed included:

Alongside these challenges, we also saw examples of good OSM practice:

• Clear documentation of model structure and assumptions
• Explicit licensing terms
• Transparent validation reporting
• Version control and maintenance records

These models were much easier to understand, assess, and consider for reuse.

Symmetron’s perspective

Symmetron’s experience lies not only in model development, but in model critique and validation, ensuring that economic models, whether de novo or open-source, meet decision-makers’ standards. Our review of the OSM field found a consistent theme: open models are valuable learning and starting tools, but their effective use depends on robust validation.

Beyond validation, our team also works with existing open-source frameworks to adapt them for HTA submissions, extend them to new decision contexts, and align them with regulatory expectations. If your organisation is exploring open-source models, whether for internal analysis, teaching, or to accelerate development, we can help ensure those models are credible, transparent, and ready for scrutiny.

Read our Model Validation blog to learn how Symmetron supports clients in assessing and strengthening health economic models for real-world use.

Final thoughts

Open-source modelling represents an important shift in HEOR, bringing opportunities for greater transparency, collaboration, and efficiency. But openness alone does not guarantee reliability. Ensuring models are credible, well-documented, and validated will determine whether open-source approaches become trusted tools for healthcare decision-making.

As open-source modelling grows, collaboration between academia, industry, and consultancies will be essential. If your team is exploring OSMs or considering contributing to the community, we would welcome the conversation here.

‍

‍

‍

‍

‍