Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countries

Abstract

This study aimed to investigate the transferability of novel artificial intelligence (AI) methods for prediction modelling of diabetes based on real-world data (RWD) between early and late adopters of emerging health technologies from the perspective of developers and health technology assessment (HTA) experts. A two-step approach was used. Developers of the new AI methods within HTx consortium completed a survey about the benefits, usability, barriers associated with implementing the new prediction models in routine HTA practices. Then, HTA experts from Central and Eastern European (CEE) countries participated in a focus group discussion. Developers generally expressed optimism regarding the transferability of the methods, while acknowledging potential disparities across CEE countries. Key benefits that were identified included enhanced understanding of diabetes, improved cost-effectiveness modelling, and refined patient stratification, all of which could contribute to clinical and reimbursement decisions across various jurisdictions. The focus group underscored the value of real-world data for diabetes prediction modelling, serving as a beneficial resource for both clinicians and HTA agencies. However, there was a recognized need to clarify the processes of integrating randomized clinical trial data with real-world data. For the other stakeholders, the advancement of the methodology will improve the diagnosis and therapy during the process of decision making. Experts from CEE countries recognized the potential of artificial intelligence-based methods employing real-world data for diabetes modelling. These methods are seen as instrumental in elucidating the heterogeneous nature of the disease, supporting clinician decision-making and holding promises for HTA purposes.

Keywords:

• Transferability
• health technology assessment
• Central and Easter European Countries
• diabetes
• artificial intelligence

Author contributions

KT, ZK, SK, BN, GP participated in design, methodology, analysis, writing and approving the manuscript, GG, FS, JT, MH participated in development methods, application of methods, analysis, writing and approving, MD, MK, ZM, ZP, TT, MP, OP, IL, AS, AlS, MM, RH, PB, MS, TD participated in interview, focus group, interpretation, final approval of manuscript. All authors read and approve the final version.

Consent form

All participants in the study agreed to be interviewed and participate as co-authors.

Disclosure statement

The authors reported no potential conflicts of interest.

Data availability

All data from the interview and focus group discussion are published in the manuscript. There are no additional data available.

Additional information

Funding

The HTx project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 825162. This dissemination reflects only the authors’ view, and the commission is not responsible for any use that may be made of the information it contains. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

This work was funded by the HORIZON EUROPE European Research Council.