Nudging Forward: Behavioral Economics and Artificial Intelligence as Catalysts in HEOR

Author(s)

Swami S1, Lakhsmi R2, Srivastava T1
1ConnectHEOR, London, UK, 2ConnectHEOR, Delhi, India

OBJECTIVES: This study introduces fundamental behavioral economics (BE) concepts and provides insights on how an integrated approach of BE and artificial intelligence (AI) can enhance HEOR and healthcare decision making.

METHODS: The study reviews existing key BE concepts such as nudge, loss aversion, present bias, framing effects, default effect, social norms etc. and AI applications in healthcare. Nudge is a way of influencing people's behavior by altering the way choices are presented to them, without restricting the options available to them nor making any considerable changes to their costs or benefits. Social norms are behavioral expectations or rules within a group of people, while default options are pre-set courses of action that take effect if you don't actively make a different decision.

RESULTS: AI utilizes big data analytics to predict health outcomes by analyzing extensive healthcare data. But AI may inadvertently introduce biases in predictions due to data quality or algorithmic design. BE principles can be applied to identify and correct such biases. Incorporating psychological and cognitive insights into AI processes can enhance the accuracy of predictive models. BE can help in personalization and minimizing hallucinations. AI algorithms can be employed in predicting patient outcomes like adherence, treatment uptake, etc. By identifying those at a risk of non-adherence, behavioral nudges like personalized reminders highlighting the benefits of adherence can be sent to patients. Another example is in preventive care, using lifestyle and genetic data, AI algorithms can predict the risk for chronic diseases among individuals. Such individuals can be incentivized to take proactive health measures (e.g., lifestyle change or vaccination) using social norm or default effect.

CONCLUSIONS: The integration of AI’s analysis and BE’s understanding of human behavior in HEOR holds substantial promise for enhancing healthcare delivery and outcomes potentially leading to more personalized, transparent and cost-effective healthcare solutions.

Conference/Value in Health Info

2024-11, ISPOR Europe 2024, Barcelona, Spain

Value in Health, Volume 27, Issue 12, S2 (December 2024)

Code

MSR176

Topic

Methodological & Statistical Research

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics

Disease

No Additional Disease & Conditions/Specialized Treatment Areas

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