OBJECTIVES: Health economic models (HEMs) are used to inform decision-making in healthcare, including resource allocation and policy development. However, the construction and validation of these models can be time-consuming and resource-intensive. Artificial intelligence (AI) has the potential to revolutionize the development of health economic models. AI techniques, such as machine learning and natural language processing, can help to streamline the development of HEMs by automating data collection and analysis, by enabling faster and more accurate analysis of complex data sets, by identifying patterns and trends that may not be evident to human analysts, by streamlining the model development process and improving the accuracy and precision of models. This poster will illustrate some uses of AI for the development of HEMs.

METHODS: One potential application of AI in HEMs is in the development of cost-effectiveness analyses. AI can be used to analyze large amounts of data on treatment outcomes and costs, and to identify the most cost-effective interventions. AI can also be used to forecast future trends in healthcare costs and utilization, enabling decision-makers to plan for resource allocation and policy development.

AI can also be used to optimize model structure and parameters. Machine learning algorithms can be used to identify the most important factors influencing the effectiveness and cost of a healthcare intervention, and to optimize the weighting of these factors in the final model. This can improve the accuracy and precision of the model and allow for a more detailed and nuanced understanding of the cost-effectiveness of the intervention.

RESULTS: and CONCLUSIONS: The use of AI in the development of HEMs has the potential to significantly improve the accuracy and efficiency of decision-making in healthcare. Further research is needed to explore the full potential of AI in this area and to identify best practices for its use in health economic modeling.