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.