Predictive Cost-Effectiveness Evaluation of Using a Machine Learning-Based Alzheimer’s Disease Risk Prediction Tool for Users of Social and Health Services Aged over 65 in Finland


Haikonen-Salo L1, Jalkanen K1, Ihalainen J2, Forsberg MM2, Soini E3
1ESiOR Oy, Kuopio, Northern Savo, Finland, 2VTT Technical Research Centre of Finland, Kuopio, Northern Savo, Finland, 3ESiOR Oy, Kuopio, 15, Finland

OBJECTIVES: Cost-effectiveness of using a machine learning-based risk identification tool for Alzheimer's disease (AD) was evaluated from the perspective of Finnish public payer. The aim was to find out how accurate the real-world data-based AD risk prediction model should be when it is used on unselected persons over 65 years of age in connection with the use of social and healthcare.

METHODS: The cost-effectiveness of using the AD risk prediction model was evaluated by means of a state transition model with different accuracy (50–90%) and usage time scenarios (1–5 years before current practice diagnosis) and compared to a situation where the risk prediction model was not used. Based on the risk prediction model, enhanced follow-up was started for those at high risk of developing AD, thanks to which AD could be diagnosed in time in more cases and a new innovative drug treatment could be started, potentially slowing down the course of AD. The time horizon of the model was 15 years, and a 3%/year discounting was used. Probabilistic and one-way sensitivity analyses were done.

RESULTS: Using the risk prediction model produced more quality-adjusted life years at lower costs in almost all scenarios. Also, in those scenarios where the costs were higher with the use of the risk prediction model, risk prediction was highly likely to be cost-effective. Cost-effectiveness improved because of the better accuracy of the risk prediction model. The biggest uncertainty drivers were the costs of using the risk prediction model and the success in follow-up for making earlier diagnoses.

CONCLUSIONS: The use of the AD risk prediction model has a very high probability of being cost-effective in an unselected population of users of social and health services over 65 years of age. Future studies should investigate the actual benefits of earlier diagnosis after risk prediction.

Conference/Value in Health Info

2024-05, ISPOR 2024, Atlanta, GA, USA

Value in Health, Volume 27, Issue 6, S1 (June 2024)




Economic Evaluation, Medical Technologies, Methodological & Statistical Research, Study Approaches

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics, Cost-comparison, Effectiveness, Utility, Benefit Analysis, Decision Modeling & Simulation, Medical Devices


Biologics & Biosimilars, Geriatrics, Medical Devices, Neurological Disorders, Personalized & Precision Medicine

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