Decision-analytic models play a vital role in healthcare, helping researchers, policymakers, and clinicians evaluate the cost-effectiveness and value of new interventions. Yet, building these models can be complex, especially in early-stage research or data-limited contexts such as drug repurposing or rare diseases.
To address this challenge, researchers in the REPO4EU consortium have developed SMART, a structured, step-by-step tool designed to guide users through making, reporting, and justifying simplified modelling choices tailored to specific decision contexts.
Teebah Abu-Zahra, Health Economics Researcher at Maastricht UMC+, led the development of the tool alongside other colleagues involved in REPO4EU. In this interview, she explains the motivation behind SMART, how it can support innovators and decision-makers, and what role it plays in advancing drug repurposing and precision medicine.
Hi Teebah! Can you tell us, in a few words, what is SMART?
SMART is a practical tool built to guide researchers through the process of making, reporting, and justifying simplified healthcare modelling choices, in a step-by-step structured framework. It helps users understand the trade-offs between the model’s simplicity, and its transparency and validity in each model feature. Overall, SMART promotes fit-for-purpose modelling that is appropriate for the specific decision context and the constraints that come with it.
What prompted the need to create this tool?
In short, SMART was born out of the need to make decision models more accessible, more transparent, and more useful for those who need it most, especially when time and data are in short supply.
When we first started this work, our goal was to develop practical guidance for researchers who aren't health economics experts but still need to use decision-analytic models to inform important healthcare decisions.
We were thinking not only of scientists and innovators working on drug repurposing research in academic settings or SMES, but of anyone who, despite having limited time, data or technical expertise, still needs to assess the value of their healthcare innovation.
Why is this important within a healthcare innovation context?
There is currently no clear accessible guidance tailored to developing decision models in contexts such as designing an early-stage intervention, testing a repurposed drug, or working in a resource-constrained setting.
Decision-analytic models are powerful tools in health economics, used to compare healthcare interventions based on their potential cost-effectiveness. They support decisions at all stages of development, from research prioritization to pricing and reimbursement. But building these models isn’t straightforward.
As we looked through the existing literature and resources, we saw a growing gap in supporting decision models development in contexts where simplicity isn’t just a preference, but a necessity.
Think, for example, of precision mechanism-based drug repurposing, where hundreds of drug-indication pairs need to be screened for potential value, often without clinical or cost data. Or rare diseases and health system planning in low- and middle-income countries, where timelines are tight and data is sparse. In these scenarios, simple early-stage models can provide immense value, by highlighting unmet medical needs, estimating societal value and guiding early pricing discussions.
Who are the end users of SMART?
We believe SMART will be valuable to a wide range of users — from health economic decision modellers all the way to regulatory bodies, health technology assessment agencies, peer reviewers, policymakers, clinicians, and even other healthcare decision makers.
Apart from enabling reviewers and users to better assess whether a model is suitable to inform healthcare decisions, we also see value in using SMART as a training and educational tool for non-experts in health economic decision modelling, including scientists, innovators and public health professionals.
Now that it’s been officially released, we’re excited to see how SMART will be applied in real-world settings to support more transparent, efficient and fit-for-purpose modelling.
How has it been received by the healthcare community so far? Have you had any feedback from peers who have used the tool yet?
Yes! One of my colleagues is already using SMART in her own modelling research paper. She’s conducting an early value assessment (EVA) to evaluate the potential cost-effectiveness of seven digital technologies in addition to standard care in the United Kingdom; she said she found SMART very helpful to ensure transparent reporting and justification of the simple modelling choices.
We are also currently applying SMART in a REPO4EU pilot study involving patients with thyroid cancer. We can already see the value of our tool in guiding the development of a health economic decision model within a context of limited data and urgent need for new therapeutic options, as seen in patients with anaplastic thyroid cancer and poorly differentiated thyroid cancer.
What was your role in the development of SMART? And who else was involved in the process?
I developed SMART from its initial concept through to design and implementation, with the continuous support and guidance from Manuela Joore and Sabine Grimm, both also from Maastricht UMC+. My primary responsibilities included identifying gaps in existing health economic modelling guidance, shaping the framework and content of the tool and conducting interviews and workshops to help guide the SMART development.
The whole process also benefited from the contributions of Mirre Scholte from Radboud UMC and others from the REPO4EU consortium, including Prof. Joe Loscalzo and Adam Raymakers from Harvard Medical School. Together we identified key features of health economic decision models based on reviewing key literature, established guidelines, and our collective expertise.
To facilitate consistent use of SMART, we also developed a glossary and applied the tool in an illustrative case for planning a health economic decision model for a repurposed drug for treatment-resistant hypertension.
While the core development was conducted at KEMTA (the Clinical Epidemiology & Medical Technology Assessment at Maastricht UMC+) we also obtained extensive feedback through in-depth interviews and expert workshops with health economic decision modellers and model users, which helped us massively in refining the tool and inform key development decisions.
How will SMART contribute towards the development of the REPO4EU Platform and the overall mission of the project?
With the support of the Egnosis team, SMART will be integrated into the REPO4EU Platform to make it directly accessible to researchers and innovators engaged in drug repurposing research.
With the help of SMART, alongside other essential HTA resources on the platform, we aim to empower these researchers to develop health economic decision models to assess the value of their innovations independently and efficiently. This, in turn, promotes a more transparent and systematic approach to health economic evaluation in mechanism-based drug repurposing and precision medicine development, ultimately supporting improved decision-making and resource allocation for developing new therapeutic options that address unmet patient needs.
The SMART tool
Can we make health economic decision models as simple as possible, but not simpler?
Sounds interesting?
Follow the link to access version 1.0 of the SMART tool