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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.

Propionic acidemia (PA) is a rare metabolic disorder affecting amino acid metabolism. Liver transplantation improves some outcomes, but the impact on long-term survival remains unclear. A systematic literature review and survival analysis, identifying 94 PA patients who underwent transplantation, revealed a survival probability of 62% at age 33; while median survival was estimated at 40 years. These findings highlight a substantial survival deficit of PA patients compared to the general population despite liver transplantation.

Despite the availability of affordable pharmaceuticals treating cardiovascular diseases (CVDs), many of the risk factors remain poorly controlled. Fixed-dose combinations (FDCs), a form of incremental innovation, have already demonstrated improvements over combinations of single medicines in adherence and hard clinical endpoints. Nevertheless, there are many barriers related to the wider use of FDCs in CVDs. Our aim was to identify these barriers and explore system-level facilitators from a multi-stakeholder perspective. Identified barriers include (i) hurdles in evidence generation for manufacturers, (ii) limited acceptance of adherence as an endpoint by clinical guideline developers and policymakers, (iii) limited options for a price premium for incremental innovation for healthcare payers, (iv) limited availability of real-world evidence, and (v) methodological issues to measure improved adherence. Initiatives to standardize and link healthcare databases in European countries, movements towards improved patient centricity in healthcare, and extended value assessment provide opportunities to capture the benefits of FDCs. Still, there is an emerging need to facilitate the generalizability of sporadic clinical evidence across different FDCs and to improve adherence measures. Finally, healthcare payers need to be convinced to pay a fair premium price for the added value of FDCs to incentivize incremental innovation in CVD treatment.