The abstract from QoLEAD’s health-economic team “Conceptualizing a Health-Economic Model to Assess the Impact of Artificial Intelligence-based Technology in Dementia Care” has been accepted for discussion at the lolaHESG (Lowlands Health Economists’ Study Group) Conference 2025 this May! Congrats to Jinjing Fu, Ron Handels, Erik Buskens and many others in the QoLEAD consortium who have contributed to this project 🙌
Why is this important?
AI-based technologies hold great promise in dementia care—for example, by reducing caregiver burden and improving the quality of life for people living with dementia. However, these innovations also come with significant costs: hardware, software, maintenance, training for staff and informal caregivers.
Without appropriate evaluation, it’s difficult to determine whether the added value of such technologies justifies the cost. Our work aims to assess the potential health-economic impact of AI-based technologies in dementia care and to explore scenarios where these technologies may be cost-effective. This is crucial for informing product development and implementation strategies.
What’s this study about?
Following several rounds of discussion with experts in eHealth, artificial intelligence, industrial design, cognitive neuropsychology, virtual agents and social robots, and dementia care within the QoLEAD consortium, we consolidated their insights to develop a conceptual model. This model evaluates how AI-based technologies may affect lifetime quality of life and care use from a health-economic perspective.
The model includes:
• Mapping the impact pathways—how the technologies influence resource use and quality of life over time
• Structuring the model to reflect both dementia progression and technology’s impact pathways
• Designing a strategy to incorporate potential evidence of effectiveness and estimate long-term outcomes
We’ll be presenting and discussing this work at lolaHESG 2025 and continue refining the model throughout the year. The current model was initially designed to reflect the specific features of AI-based technologies being developed within QoLEAD, but we anticipate it can be adapted to other non-pharmacological interventions as well. We’re especially looking forward to the constructive and critical input from fellow health economists at lolaHESG to strengthen our modeling approach.