Global collaboration fuels rapid expansion in health systems modeling

At the global level, the study reveals that the United States and the United Kingdom remain the dominant producers of research in this area. Their institutions, such as the University of Washington and Tehran University of Medical Sciences, serve as major nodes in international collaboration networks. The United States alone accounts for a large share of global output, with strong co-authorship patterns linking it to Asia, Europe, and Africa.

A new bibliometric analysis provides the most comprehensive mapping to date of global research on health systems modeling. The study, titled “Trends and Developments in Health Systems Modeling: A Bibliometric Analysis,” was published in Frontiers in Digital Health. The findings show an accelerating growth trajectory in the field, driven by international collaboration, data-driven innovation, and the rising importance of systems-based approaches to healthcare planning and policy.

The authors conducted an in-depth analysis of publications indexed in the Web of Science Core Collection, covering more than three decades of research between 1992 and 2023. Using bibliometric and science-mapping techniques, they identified major contributors, key institutions, and evolving thematic clusters that are shaping the future of modeling for healthcare systems worldwide.

A field in rapid expansion

The study highlights that health systems modeling has transformed from a niche research area into a central component of health policy, planning, and management. Between 1992 and 2023, the field recorded a consistent annual growth rate of 7.53 percent, culminating in a peak of 351 publications in 2021. Each paper attracted an average of nearly 23 citations, reflecting both academic and practical relevance.

The findings confirm that research output surged particularly after 2020, likely influenced by the COVID-19 pandemic, which underscored the need for modeling tools to optimize resources, predict disease spread, and evaluate intervention strategies.

At the global level, the study reveals that the United States and the United Kingdom remain the dominant producers of research in this area. Their institutions, such as the University of Washington and Tehran University of Medical Sciences, serve as major nodes in international collaboration networks. The United States alone accounts for a large share of global output, with strong co-authorship patterns linking it to Asia, Europe, and Africa.

International collaboration contributes to over one-third (37.67 percent) of all publications, demonstrating that health systems modeling has evolved into a truly global research domain. The study also identifies The Lancet and PLOS One as the most influential publication venues, underscoring how multidisciplinary engagement, spanning epidemiology, economics, and computer science, has driven innovation in this field.

Data-driven models meet systems thinking

The research categorizes the evolution of health systems modeling into two major methodological streams, data-driven modeling and system-oriented modeling, which are now converging in modern applications.

Data-driven approaches include statistical and machine learning models that draw on large datasets, such as electronic health records, demographic surveys, and real-time epidemiological data. These models enable researchers to forecast health outcomes, detect disease clusters, and evaluate treatment effectiveness.

System-oriented models, on the other hand, emphasize the dynamics of healthcare systems as interdependent networks. Methods such as system dynamics, agent-based modeling, and discrete-event simulation allow policymakers to test how changes in policy, resource allocation, or patient flow affect performance and efficiency across entire systems.

The bibliometric analysis reveals a growing overlap between these two traditions, forming what the authors describe as a hybrid modeling paradigm. By integrating predictive analytics within system simulation environments, researchers are creating comprehensive decision-support tools for managing health crises, optimizing workforce distribution, and designing sustainable healthcare policies.

Emerging keywords in the literature, such as “predictive modeling,” “systems dynamics,” and “machine learning”, demonstrate how computational advances have expanded the scope and granularity of health systems analysis. The combination of these methods is expected to enhance resilience in healthcare systems, especially in the face of pandemics and resource constraints.

Mapping collaboration, influence, and future directions

The study’s performance analysis identifies several highly cited researchers, including T. Blakely and S. I. Hay, who have shaped the trajectory of health systems modeling through influential global burden of disease studies and simulation-based policy evaluations.

The institutional landscape shows that academic centers in the United States, the United Kingdom, China, Iran, and Australia have emerged as core hubs of innovation, often leading multi-country partnerships. Collaboration networks are particularly dense across North America and Europe, but new research clusters are now emerging in Africa and Asia, suggesting a more equitable distribution of research capacity over time.

The thematic evolution of the field also reveals distinct stages. Early work in the 1990s focused on epidemiological modeling and resource optimization. In the 2000s and 2010s, attention shifted to systems thinking, simulation, and disease burden modeling. The most recent decade has been characterized by the fusion of artificial intelligence, big data analytics, and system simulation, marking a transition toward real-time, adaptive decision-making frameworks.

This evolution, according to the authors, reflects not only technological progress but also a paradigm shift in healthcare governance, from reactive management to anticipatory, data-driven planning. By identifying influential publications and keyword clusters, the study provides a roadmap for researchers and policymakers to locate emerging opportunities for cross-disciplinary collaboration.

Bridging research, policy, and practice

The study also sheds light on the practical importance of health systems modeling for evidence-based policymaking. Predictive and simulation models are now being used to guide pandemic preparedness, chronic disease management, and health workforce allocation. These models help simulate the impact of policy interventions before implementation, reducing risks and improving efficiency.

The authors note that the convergence of data science and systems modeling has turned these tools into essential instruments for achieving universal health coverage and sustainable development goals. However, they also highlight critical gaps, particularly in data accessibility, capacity building, and methodological standardization, that continue to limit the scalability of modeling efforts in low- and middle-income countries.

The research calls for greater inclusion of diverse regional data to improve the global applicability of models and advocates for cross-sector partnerships between academia, government, and private health technology firms. Such collaborations, the authors argue, will be vital for integrating modeling outputs into real-world decision-making environments.