A recent modelling study by ESiOR Oy and VTT Technical Research Centre of Finland assessed the cost-effectiveness of a machine learning-based risk prediction tool for Alzheimer’s disease (AD) in Finland. This tool, which is still under development, aims to support early diagnosis and improve long-term health outcomes for AD patients.
Key Findings:
Greater health benefits at lower costs – The assessment tool increased quality-adjusted life years (QALYs) in the model while simultaneously reducing costs.
Risk assessment is cost-effective when integrated with other services – In the model, using the risk assessment tool alongside other social and healthcare services was cost-effective when disease-modifying AD treatments, which will soon be available, were in use.
The tool can improve AD treatment – The tool can significantly enhance the early detection of Alzheimer’s disease, enabling timely interventions and comprehensive care.
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