53a7bb081a034df5681-157d 20260825055215486 chitu:chitu chitkarauniversitypublications@chitkara.edu.in WEB-FORM Journal of Pharmaceutical Technology, Research and Management JPTRM 23212217 23212225 10.15415/jptrm https://jptrm.chitkara.edu.in/ 06 29 2026 06 29 2026 14 1 10.15415/jptrm.2026.141 https://jptrm.chitkara.edu.in/2026/volume-14-and-issue-1/ Artificial Intelligence in Health Economics and Outcomes Research (HEOR): Advancing Cost-Effectiveness Analysis, Reimbursement Decision-Making, and Healthcare Policy Saksham Sharma Chitkara College of Pharmacy, Chitkara University, Rajpura-140401, Punjab, India. Shushank Mahajan Chitkara College of Pharmacy, Chitkara University, Rajpura-140401, Punjab, India. Sanjana Mehta Chitkara College of Pharmacy, Chitkara University, Rajpura-140401, Punjab, India. Shrey Partap Singh Chitkara College of Pharmacy, Chitkara University, Rajpura-140401, Punjab, India. Sakshi Sharma Chitkara College of Pharmacy, Chitkara University, Rajpura-140401, Punjab, India. Samrat Chauhan Chitkara College of Pharmacy, Chitkara University, Rajpura-140401, Punjab, India. Sanchit Dhankhar Chitkara College of Pharmacy, Chitkara University, Rajpura-140401, Punjab, India. Background: The application of artificial intelligence (AI) to health economics and outcomes research (HEOR) will change the game in how large, heterogeneous, real-world healthcare datasets can be analyzed. Purpose: To provide an overview of AI in HEOR, with an emphasis on predictive analytics, real-world evidence creation, cost-effectiveness modelling, and clinical decision support. Methods: A narrative review approach was used to summarize existing evidence about the use of machine learning (ML), natural language processing (NLP), and deep learning (DL) techniques in the context of HEOR. Results: AI-driven approaches facilitate higher-quality data analysis, a faster research process, and increased accuracy in predicting health-related and economic outcomes. ML models predict clinical outcomes based on electronic medical records, whereas NLP provides for the interpretation of clinical findings from free-text notes. These technologies are directly related to cost-effectiveness analysis and reimbursement policies. Conclusion: Although AI has many benefits in terms of its impact on HEOR research, there are still some unresolved issues, such as data security issues, biases in AI algorithms, and the lack of regulatory frameworks. 6 29 2026 6 29 2026 1 20 10.15415/jptrm.2026.141001 https://jptrm.chitkara.edu.in/2026/artificial-intelligence-in-health-economics-and-outcomes-research-heor-advancing-cost-effectiveness-analysis-reimbursement-decision-making-and-healthcare-policy/