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.