Abstract
INTRODUCTION: Health systems modeling is increasingly used to address complex health challenges and inform policy. Despite its growing importance, the field remains dynamic, with evolving research themes, and global contributions. This study maps the evolution of the field, identifies leading publications, authors, institutions, and countries, and highlights emerging themes to guide future research and collaboration. METHODS: A bibliometric analysis was conducted on March 10, 2023, using the Web of Science (WoS) Core Collection for 1992-2023. The search string was "health system*" AND "modelling" OR "modeling." Records were analyzed with Biblioshiny and VOSviewer to compute publication trends, authorship patterns, institutional and country-level contributions, international collaboration, and thematic developments. RESULTS: A total of 2,023 records were retrieved. The annual publication growth rate was 7.53%, with an average of 9.35 co-authors per article and 37.67% international co-authorship. Leading journals included The Lancet and PLOS One, while prominent authors were Blakely T. and Hay S.I. Key contributing institutions were the Tehran University of Medical Sciences and the University of Washington. The United States and the United Kingdom were the most productive countries. Thematic analysis revealed prominent and emerging topics such as "health systems," "modeling," "predictive modeling," and "systems dynamics" suggesting promising directions for future research. DISCUSSION: Findings indicate a dynamic and expanding research landscape with strong international collaboration and concentrated contributions from high-impact journals, established authors, and leading institutions. The study highlights epidemiology and predictive modeling as promising directions for future research and identifies opportunities for international collaboration and publication. The analysis is limited by reliance on a single database (WoS); further studies should integrate additional databases to improve coverage and deepen the findings. The results can inform decisions on collaboration opportunities, suitable publication venues, and key research gaps in health systems modeling.