20.500.12556/DKUM-95837 Evolutionary game theory use in healthcare a synthetic knowledge synthesis Background: Evolutionary game theory (EGT), originating from Darwinian competition studies, offers a powerful framework for understanding complex healthcare interactions where multiple stakeholders with conflicting interests evolve strategies over time. Unlike traditional game theory, EGT accounts for bounded rationality and strategic evolution through imitation and selection. Aims and objectives: In our study, we use Synthetic Knowledge Synthesis (SKS) that integrates descriptive bibliometrics and bibliometric mapping to systematically analyze the application of EGT in healthcare. The SKS aimed to identify prolific research topics, suitable publishing venues, and productive institutions/countries for collaboration and funding. Data was harvested from the Scopus bibliographic database, encompassing 539 publications from 2000 to June 2025, Results: Production dynamics is revealing an exponential growth in scholarly output since 2019, with peak productivity in 2024. Descriptive bibliometrics showed China as the most prolific country (376 publications), followed by the United States and the United Kingdom. Key institutions are predominantly Chinese, and top journals include PLoS One and Frontiers in Public Health. Funding is primarily from Chinese entities like the National Natural Science Foundation of China. Bibliometric mapping identified five key research themes: game theory in cancer research, evolution game-based simulation of supply management, evolutionary game theory in epidemics, evolutionary games in trustworthy connected public health, and evolutionary games in collaborative governance. Conclusions: Despite EGT’s utility, significant research gaps exist in methodological robustness, data availability, contextual modelling, and interdisciplinary translation. Future research should focus on integrating machine learning, longitudinal data, and explicit ethical frameworks to enhance EGT’s practical application in adaptive, patient-centred healthcare systems evolutionary games theory healthcare complex healthcare systems synthetic knowledge synthesis thematic analysis evolucijska teorija iger kompleksni zdravstveni sistem sintetična sinteza znanja true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/information © 2025 by the authors Članek v reviji 2025-10-29 15:30:36 2025-10-29 15:30:37 2025-10-30 03:15:36 0000-00-00 00:00:00 2025 0 0 16 str. issue 10, [article no.] 874 Vol. 16 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-07-29 2025-10-06 2025-10-08 004.8 254115075 10.3390/info16100874 2078-2489 information-16-00874-v2.pdf information-16-00874-v2.pdf 1 6A6F4DC7ABB342594CC8016A07A08395 6444749a8060b17aed7c134f2559a2b5ccb3068a94e16ec02631abd5511cddaf dcaf3b71-b4d3-11f0-852c-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=200261 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0