Presentation (CTI)

OBJECTIVES: Pharmacoeconomics and Health Economics and outcomes research (HEOR) play a critical role in evaluating the value, cost effectiveness and outcomes of health interventions. However, traditional pharmacoeconomic approaches often face challenges to data complexity, limited scalability and time intensive analyses. The objective of this review is to examine the role of artificial intelligence (AI) in pharmacoeconomics and HEOR, highlighting it's applications, potential benefits and key challenges.
METHODS: A narrative literature review was conducted using publicly available peer reviewed articles, review studies and policy reports focusing on the use of artificial intelligence in pharmacoeconomics and HEOR. Relevant literature discussing machine learning, natural language processing, predictive analytics and real world evidence generation in healthcare economics was identified and qualitatively reviewed to summarise current trends and applications.
RESULTS: The reviewed literature suggests that AI based approaches can support multiple HEOR activities, including cost predictions, health outcome forecasting patient satisfaction and analysis of large datasets derived from electronic health records, insurance claims amd published literature. AI driven models enable faster data processing and identification of complex patterns that may not be easily captured through conventional analytical methods. These capabilities have the potential to support value based healthcare decision making and more efficient resource allocation. However, several challenges were consistently identified, including concerns regarding data quality, lack of transparency in algorithmic decision making, ethical consideration and limited regulatory guidance. In low and middle income countries such as india fragmented health data systems and infrastructural limitations further constrain implementation.
CONCLUSIONS: Artificial Intelligence has the potential to complement traditional pharmacoeconomic and HEOR methodologies by enhancing analytical efficiency and decision support. While AI cannot replace established economic evaluation frameworks, it's responsible integration supported by standardised guidelines, ethical oversight and interdisciplinary collaboration can strengthen evidence based healthcare decision making and improve healthcare value.