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Review

Evolutionary Game Theory Use in Healthcare: A Synthetic Knowledge Synthesis

by
Peter Kokol
1,2,*,
Jernej Završnik
2,3,
Helena Blažun Vošner
2,3 and
Bojan Žlahtič
1
1
Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
2
Community Healthcare Center Dr. Adolf Drolc Maribor, 2000 Maribor, Slovenia
3
Alma Mater Europaea, 2000 Maribor, Slovenia
*
Author to whom correspondence should be addressed.
Information 2025, 16(10), 874; https://doi.org/10.3390/info16100874
Submission received: 29 July 2025 / Revised: 14 September 2025 / Accepted: 6 October 2025 / Published: 8 October 2025
Browse Figures
Versions Notes

Abstract

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.

1. Introduction

Evolutionary game theory (EGT) [1] is a powerful tool for understanding behaviour in complex healthcare settings, particularly where multiple stakeholders or agents interact with potentially conflicting interests and evolve over time [2]. EGT originates from the study on Darwinian competition in biology published in 1971 [3]. Unlike traditional game theory, EGT accounts for bounded rationality and how strategies evolve over time through imitation and natural selection [4]. Interesting health topics where EGT is applied are modelling antibiotic resistance [5], developing cancer treatment strategies [6], informing public health interventions [7], analyzing doctor–patient interactions [8], human social interactions in macro-level public health [9], and many other applications as described in the rest of this paper.
The existing literature on the application of evolutionary game theory (EGT) in healthcare highlights several specific challenges. These include the complexities of multidisciplinary and multiprofessional collaboration, as well as significant ethical issues and security concerns. Despite these identified challenges, a comprehensive synthesis of the research has not yet been published. To the best of our knowledge, the current body of literature has not yet addressed the field in a holistic manner, particularly in the context of the aforementioned challenges. This represents a significant gap in the academic discourse, as a unified perspective is critical for advancing the effective and responsible application of EGT in healthcare.
To bridge this research gap, we conducted a Synthetic Knowledge Synthesis (SKS), as detailed in [10,11]. SKS utilizes a triangulated approach to knowledge synthesis, integrating descriptive bibliometrics and bibliometric mapping.
This synthesis aimed to
  • Identify the most prolific research topics and themes.
  • Pinpoint suitable publishing venues for researchers to stay informed and disseminate their research work on evolutionary games in healthcare.
  • Discover productive institutions and countries for potential collaborations, as well as identify possible funding bodies.
The aim of the present study is not to inform experts in evolutionary game theory (EGT) or to advance the fundamental theoretical underpinnings of the field. Instead, it is aimed at interested healthcare professionals, including researchers, practitioners, and managers. The primary objectives are threefold: to provide a comprehensive overview of EGT employment in healthcare, to present an overview of the current landscape of EGT healthcare applications, and to encourage its practical use within the healthcare domain.

2. Materials and Methods

While SKS framework is extensively presented elsewhere, the description below focuses specifically on its application within the present research. The research publications corpus was retrieved from the Scopus bibliographic database (Elsevier, Amsterdam, The Netherlands) [12] using this advanced search command:
TITLE-ABS-KEY (“evolution* game*” AND (health* OR medic* OR nursi*))
Descriptive bibliometrics were performed using Scopus’s built-in functions. Bibliometric mapping was executed using VOSViewer software version 1.6.20 (Leiden University, Leiden, The Netherlands). The following four steps were undertaken:
  • Corpus Harvesting: The literature search was conducted on 2 June 2025.
  • Descriptive Bibliometric Analysis: This involved analyzing country and institutional productivity, production trends in the literature, journal analytics, and identifying funding bodies and document types.
  • Bibliometric Mapping: Author keywords were mapped to visualize their relationships.
  • Thematic Analysis: The inductive thematic analysis was performed on the bibliometric map by examining the proximity and links between author keywords to discern underlying research themes. Deductive analysis was used to identify most popular EGT strategies/techniques and applications in the healthcare field.

3. Results

3.1. Descriptive Bibliometrics

A comprehensive search identified a total of 539 publications. The distribution of these publications by type was as follows: 418 articles, 66 conference papers, 25 conference reviews, 15 reviews, 6 book chapters, 3 books, and 8 other publication types. One publication was subsequently retracted. The first publication presenting evolutionary game theory employment in the healthcare domain emerged in 2000. This inaugural publication addressed critical issues related to healthcare information security, confidentiality, and privacy in response to the increasing adoption of Electronic Patient Records [13]. Following this initial publication, the scholarly output remained limited with no more than three publications per year until 2009. From 2009 onwards, a linear growth trend in publications was observed, transitioning to an exponential growth phase beginning in 2019. Peak productivity was recorded in 2024 with 98 publications (Figure 1).
China is the most prolific country, contributing 376 publications, which constitutes over two-thirds of the total output. Following China, the most productive countries (with n ≥ 10 publications) are as follows:
  • United States (n = 65);
  • United Kingdom (n = 39);
  • Italy (n = 107);
  • India (n = 49);
  • Hong Kong (n = 11);
  • Iran (n = 11);
  • Canada (n = 10);
  • Japan (n = 10).
A substantial majority of these nations possess robust economies, exhibit high Healthcare Index scores [14], and consistently rank among the top-tier countries, in general, and medical sciences according to the Scimago Country Rank (Elsevier, Amsterdam, The Netherlands).
The most productive institutions, each contributing eight or more publications, are predominantly located in China, with one exception:
  • Jiangsu University (n = 17);
  • Ministry of Education of the People’s Republic of China (n = 14);
  • Wuhan University (n = 10);
  • Beijing Institute of Technology (n = 8);
  • Bangladesh University of Engineering and Technology (n = 8);
  • Nanjing University of Information Science & Technology (n = 8);
  • Tongji University (n = 8).
The journals that have published more than ten papers are as follows:
  • Plos One (n = 26);
  • Frontiers in Public Health (n = 21);
  • International Journal of Environmental Research and Public Health (n = 17);
  • Sustainability (n = 16);
  • Scientific Reports (n = 12);
  • Chaos Solitons and Fractals (n = 11);
  • Journal of Theoretical Biology (n = 11).
These journals are recognized international publications consistently ranked within the first quartile of their respective categories by Scopus CiteScore [15]. Consequently, they represent valuable venues for researchers seeking relevant studies and for disseminating their own findings.
Analysis of funding sources reveals that the most prolific sponsors, defined as those supporting over 10 publications, are predominantly Chinese entities. These include
  • National Natural Science Foundation of China (n = 120);
  • Ministry of Science and Technology of the People’s Republic of China (n = 76);
  • National Office for Philosophy and Social Sciences (n = 45);
  • Ministry of Education of the People’s Republic of China (n = 32);
  • Fundamental Research Funds for the Central Universities (n = 12).
The first non-Chinese sponsor, UK Research and Innovation (n = 8), is positioned seventh in terms of productivity. The observed funding rate of 25% for published papers is consistent with established benchmarks across various research domains [16]. Being informed about prolific funding agencies is crucial for research institutions seeking to secure competitive grants enabling them to facilitate the recruitment of leading scholars, procurement of advanced technological and research infrastructure, participation in scientific collaborations, and the dissemination of their institutional research outcomes at high-tier conferences.
In summary, the descriptive SKS analysis reveals a significant geographical concentration of this research. China is the most prolific country with 376 publications, followed by the United States and the United Kingdom. Key institutions and funding bodies are also predominantly Chinese, indicating a regional hub of expertise and investment in this domain.

3.2. Most Cited Publications

In this section we present the most cited research publications published in the period of years 2020–2025, which represent widely acknowledged seminal works that have significantly influenced the research on the use of evolutionary games in healthcare and introduced recent influential concepts, ideas, innovative methodologies, techniques, and applications (Table 1).
The most cited papers showed that EGT is a powerful tool for modelling complex healthcare systems where individual behaviours and interactions drive collective outcomes. In the above studies, EGT was applied in a range of public health and technological challenges. In the context of the COVID-19 pandemic, it was used to analyze how a population’s cultural norms, or “tightness-looseness”, influence disease spread. The findings suggest that “tight” cultures, characterized by strict social norms and coordination, have a clear advantage, resulting in fewer cases and deaths compared to “loose” cultures. Another study applied EGT to model how individual compliance with public health measures like lockdowns can change over time. It suggests that government financial aid can increase adherence to these measures, thereby reducing the pandemic’s duration and economic impact. In the realm of zoonotic diseases, EGT was used to model the transmission dynamics of Monkeypox (mpox) across both human and animal populations. By combining EGT with network structures, researchers can identify key drivers of disease spread, such as super-spreaders, and assess the effectiveness of targeted interventions. This approach provides a deeper understanding of how individual behaviours and network topology influence the persistence and transmission of infectious diseases. Beyond public health, EGT addressed technological challenges like the recycling of household medical devices. The study showed that when governments use a dynamic system of punishments and subsidies, they can establish a stable equilibrium that encourages recycling by enterprises in contrast to static policies which are less effective. EGT can also be instrumental in optimizing the performance of the Internet of Things (IoT) and blockchain integration. By modelling the strategic behaviour of IoT devices as miners, EGT helps to develop algorithms for selecting the most efficient cloud mining pools, thus addressing the high computational and energy costs associated with blockchain technology.
The methods employed in these studies were grounded in mathematical and computational modelling. The COVID-19 study on cultural norms used ordinary least squares regression to analyze the relationship between cultural tightness and disease rates, while controlling for various confounding factors like demographics and climate. It also incorporated a formal evolutionary game theoretic model to simulate the cooperation and survival rates of different cultural groups under a collective threat. The study on lockdown compliance integrates compartmental epidemiological models (which track populations through different stages of a disease) with EGT to simulate how individual choices affect collective outcomes. The research on Monkeypox transmission combined network structures (such as scale-free and random regular networks) with EGT to analyze disease dynamics. This interdisciplinary approach highlighted the importance of considering both social behaviour and network topology. Finally, the paper on blockchain and IoT used a centralized evolutionary game-based pool selection algorithm to maximize system utility. It also proposed a distributed reinforcement learning algorithm, named the ‘WoLF-PHC’, to manage the non-cooperative relationships among multiple miners. These diverse methodologies demonstrated the versatility of EGT in providing a quantitative framework for analyzing complex systems and informing strategic decision making. However, the EGT was applied in a variety of healthcare scenarios, as presented in the next sections.

3.3. Synthetic Knowledge Synthesis

SKS was performed with the help of bibliometric mapping, executed by VOSviewer software [12]. Altogether, 539 author keywords were identified. According to Zipf’s law [17], 47 were included in the mapping analysis. According to Souza et al. [18], the suitable number of keywords for keyword networks analysis can be calculated following Zipf’s law, namely taking the square root of the number of all keywords. Thus, 47 keywords approximate the square root of 539, representing all author keywords emerging in three or more publications. The resulting author keywords landscape is shown in Figure 2. Thematic analysis resulted in 11 categories and five research themes, as shown in Table 2.
The identified themes and categories are presented in more detail below.
  • Game theory in cancer research
    ○
    Game theory in cancer research. Wolf et al. presented an essay in which they suggest that cancer progression is an evolutionary competition between different cell types and can be analyzed as an evolutionary game [19]. West et al. introduce a prisoners dilemma game model of three competing cell populations, which recapitulates prostate-specific antigen data from clinical trials The model enables one to design and quantify different treatment strategies [20]. Wu et al. designed a statistical physics model combining metabolites into interaction networks. By integrating concepts from the ecosystem and evolutionary game theory, one can model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and extrinsic influences [21]. Szasz [22] used evolution game theory to explain Peto’s paradox in epidemiologic observations of the six degrees of tumour prevalence.
    ○
    Cooperation and evolution in game theory. Cagan and Page used evolutionary game theory for cancer modelling, aiming to explain how cancerous mutations spread through healthy tissue and how intercellular cooperation persists in tumour cell populations using more realistic spatial models [23].
  • Evolution game-based simulation of supply management
    ○
    Evolution game-based simulation of public health emergency. Chain et al. analyzed the role of behaviour and the decisions of public and governments in the COVID-19 panic buying event from the perspective of evolutionary games [24]. Ngonghala et al. [25] analyzed the human choice parameters to self-isolate during pandemics. Rui et al. used evolutionary games to analyze the more general problem of public health emergencies involving local government, social organizations, and the public [26], and Fan et al. analyzed the combination of punishment and reward mechanisms in the problem of how to mobilize the enthusiasm of residents, communities, and governments [27]. Kabir et al. used a novel exportation–importation epidemic model mimicking behavioural dynamics to study the impact of quarantine policies, healthcare facilities, socio-economic costs, and the public counter-compliance effect under the evolutionary game theory by considering a source country of a contagious disease and a neighbouring disease-free country [28]. Yang et al. [29] analyzed the evolution of cooperation in public goods distribution decision making reflecting intergenerational conflicts.
    ○
    Use of evolution-stable strategy for decision making in the food supply chain. The replicated dynamic equation-based evolutionary games were employed to study evolutionarily stable strategies of suppliers and producers for assuring food safety [30] and quality control [31].
    ○
    Numerical simulation of supply chain management using tripartite evolutionary game. Peng et al. employed a tripartite evolutionary game model that simulates the interaction of interests between food raw material suppliers, food manufacturers, and consumers to identify the key factors that influence the decision making of each participant [32]. Tripartite games were also used to analyze the influence of blockchain technology on the evolutionary stability strategies for financial institutions, core enterprises, and small to medium-size enterprises [33] and cold chain supply for fresh agricultural products [34]. In another study, Zhang et al. used tripartite evolutionary games to analyze the influence of the digital twin service on environmental, social, and governance evaluations and analytically investigate the long-term behaviour of sustainability concerned stakeholders in the vaccine logistics supply chain [35].
  • Evolutionary game theory in epidemics
    ○
    Evolutionary game theory for COVID-19 vaccination management. Jia et al. employed the stochastic evolutionary game model in combination with the Moran process to analyze epidemic prevention and control strategies to maximize the expected and super-expected benefits, taking into account vaccination, cultural differences, and irrational emotions [7]. A similar model was studied by Dashtable and Mirzalel, focusing on behavioural changes based on vaccination, hospitalization, and recovery status and by Lee et al., focusing [36] on vaccine hesitancy and vaccination campaigns.
    ○
    Evolutionary game theory in SIR development. An epidemiological SIR model was proposed combining social strategies, individual risk perception, and viral spreading to study different strategy adoptions [2]. To characterize the SIR mechanism, authors constructed a networked SIR model that introduces an evolutionary game framework. Behavioural effects that significantly influence disease dynamics within the coupled disease–behaviour system are captured through sensitivity analysis [37]. Zhang et al. [38] used evolutionary-based SIR modelling to study the behavioural exchanges on different governmental decision making strategies.
  • Evolutionary games in trustworthy,connected public health
    ○
    Simulation analysis using blockchain in trustworthy Internet of Things. Mai et al. [39] and Yao and Guizani [40] used a centralized evolutionary game pool selection algorithm to maximize the privacy, security, and utility of healthcare IoT network-based blockchain and mining pool architectures.
    ○
    Regulation of privacy protection. The regulation of privacy protection becomes a significant problem in mobile healthcare systems. Those problems can be analyzed using an evolutionary games approach. In this manner, Zhu et al. analyzed the influence of economic factors in the privacy protection of mHealth systems [41]; Jiang et al. [42] and Hu et al. [43] studied the secure access to big medical data. In a different kind of context, Zhu et al. [44] analyzed the interaction mechanisms of four parties (patients, medical institutions, smart medical platforms, and governments) in maintaining privacy of smart medical care. Chen and Su [45] used an asymmetric evolutionary model to manage conflicts between doctors and patients.
    ○
    Evolutionary games in public health. Chen and Zhu [46] constructed an evolutionary four-party game model (pharmaceutical enterprises, testing agencies, government regulators, and drug wholesale enterprises) that incorporates rent-seeking dynamics together with a reward–punishment mechanism to analyze the strategies of four players in achieving the integrity of pharmaceutical enterprises in a manner to maintain public health, social stability, and national security. Ma and liu [47] developed a tripartite evolutionary game model (government, whistleblowers, and the public) analyzing the interaction between these subjects under the uncertainty of risk perception to achieve early warnings for public health emergencies.
  • Evolutionary games in collaborative governance
    ○
    Evolutionary games/prospect theory in collaborative governance of public health emergencies. Zhao et al. [48] analyzed the evolutionary paths of stability points, dual-stable states, and unstable states under different government engagement policies during strikes causing public health emergencies. While co-operation is crucial in preventing and controlling emergencies, Xu et al. [49] proposed an evolutionary tripartite game (government, enterprises, and the public) to analyze different factors in combination with different conditions to support the decision making of players. On the other hand, Shan and Pi [50] used a tripartite game (public, merchants, and government) to respond to panic buying events. Yuan et al. [51] used two-sided, government-owned nonprofit organizations and a hospital game model to explore and influence factors in medical supply distribution in medical emergencies.
    ○
    Four-party evolutionary games used in collaborative governance. Due to the influence from enterprises, local governments might relax environmental regulations, posing threats to public health. Hu et al. [52] used a four-party evolutionary game model (enterprises, local governments, central government, and the media) to seek equilibrium collaborative governance solutions. In another interesting application, a four-party game (government regulatory agency, We Media, vaccine industry groups, and the public) was used in the development of a dual regulatory system of vaccine quality in assessing the stability points of each players’ strategy [53]. Chen and Zhu [46] used evolutionary games/prospect theory to assess the collaborative governance in rent-seeking dynamics and reward–punishment measures between the pharmaceutical industry, drug testing agencies, government regulators, and drug selling entities. Zang et al. [54] devised an evolutionary game model to analyze integrative coordination mechanisms adopted by humanitarian business partnership (humanitarian organizations, business corporations, and impact of public engagement) to prevent corruption and counterfeit products.

3.4. EGT Strategies/Techniques and Application Areas

Using deductive thematic analysis as part of SKS, we first defined two codes, namely EGT Strategies and techniques and EGT application areas. With the help of VOSViewer, we generated an author keywords list and assigned each keyword occurring in at least three publications into one of the two predetermined codes. The results of the deductive analysis are shown in Table 3 and Table 4. Cooperation, simulation, tripartite game, stable strategy, and complex network analysis were the most popular strategies or techniques. The most popular application areas were government regulation, supervision and intervention, COVID-19 management, vaccination effectiveness analysis, the use of blockchain in healthcare, and collaborative governance.

3.5. Research Gaps

Recent cutting edge general EGT research showed that more complex EGT strategies might lead to better outcomes. Sheng et al. [55] showed that strategy evaluation based on higher order networks can reveal different cooperation/competition outcomes in comparison to using simpler networks. Wang et al.’s [56] use of temporal networks resulted in new theoretical insights into the subtle effects of network temporality in utility games. Betz et al. [57] combined community structure and empirical environmental feedback to explore the dynamics of community–environment interactions. The model resulted in rich dynamical behaviour and offered insights into understanding how the presence of a community structure impacts eco-evolutionary dynamics. Dubois [58] and Weyerer et al. [59] employed empirical/theoretical bland and mutualistic systems to obtain insight into the combination of heterogeneity, network, and empirical dynamics. Similar EGT strategies have been used in the analysis of short-term evolution shifts in the gut microbiome [60] and cognitive bias as part of evolutionary process [61]. Co-evolution was introduced to EGT to analyze bounded reasoning [62] and biodiversity change [63].
Despite the utility of EGT’s potential in healthcare demonstrated in our synthesis, most of the applications used simplified replicator dynamics or assumed fixed strategy sets and static populations and did not employ the advanced strategies presented above. In addition to this general research gap, the analysis of the publications reviewed in the SKS part of the synthesis revealed additional, more specific research gaps: fundamental methodological challenges in model construction, particularly concerning the accurate representation of complex biological and social interactions [2,23]; the persistent issues of data scarcity, quality, and parameter estimation [25]; significant barriers in real-world contextual and behavioural modelling; technical and implementation challenges in translating theoretical EGT models into actionable clinical tools and validated public health policies [44,47]; and limitations in interpretability, generalizability, difficulty in interdisciplinary communication, and the absence of robust empirical validation frameworks [29,37]. Addressing ethical, governance, and regulatory considerations, such as algorithmic bias and equity in model outcomes, also remains paramount [45,51]. Furthermore, the analysis of the whole retrieved corpus pointed to several more under-researched areas like information asymmetry [64], dynamic human behaviour and adaptive learning [65,66], and changing environment [67].

3.6. Methodological and Theoretical Limitations

Many models are based on oversimplified assumptions like static payoffs and perfect rationality, failing to capture the dynamic nature of healthcare systems where parameters (e.g., disease prevalence, resource availability) constantly evolve [64,68]. Furthermore models often remain theoretical without real-world testing [69] or existing frameworks struggle to incorporate heterogeneous stakeholders (e.g., patients, providers, and insurers) with conflicting incentives [69].
Additionally, models rarely account for regional disparities in resource access or patient behaviour. Some urban–rural healthcare competition studies show that efficiency gains from cooperation depend heavily on unaddressed contextual variables like income levels or education [9,70]. Evolutionary games also assume gradual strategy shifts, but real-world crises (e.g., pandemics) trigger rapid behavioural changes [71]. EGT also faces technical, implementation, and regulatory challenges. Due to the exponential rise in using IoT and artificial intelligence in healthcare, few studies address interoperability with existing systems or data privacy constraints [71]. Current regulatory frameworks are modelled as static inputs; however, policies evolve in response to stakeholder strategies, creating feedback loops that are rarely incorporated [68]. Furthermore, fraud detection systems or third-party entities may collude with hospitals systems. In addition to the above, EGT research faces interdisciplinary translation problems, for example, cancer therapy models using evolutionary games (e.g., adaptive therapy) are mathematically robust but face implementation barriers due to clinicians’ limited familiarity with game-theoretic concepts [68]. Furthermore, gamified health interventions often prioritize engagement over clinical outcomes. For instance, serious games for chronic disease management rarely integrate evolutionary game mechanics tailored to patient motivation profiles [72]. But, by addressing the above research gaps in a multidisciplinary and multiprofessional way, EGT might still become a powerful and useful analytical tool in developing component, adaptive, personalized, patient-centred, and equitable healthcare systems.

4. Future Research Directions

Based on the above described gaps and using a bibliometric-based approach to asses trends [73] and comparing the EGT research landscapes from the period 2021–2023 and 2024–2025, the following future research directions have been identified:
  • Incorporating machine learning into EGT design and execution to enable dynamic parameter settings.
  • Integrating data from longitudinal studies and regulatory feedback mechanisms into EGT construction and validation, forming advanced ecosystems.
  • Developing shared ontologies between health professionals, data scientists, and game theorists.
  • Solvig scalability issues with investment in computationally efficient and scalable EGT algorithms and platforms.
  • Developing explicit ethical frameworks and methodologies to integrate ethical considerations, such as fairness, equity, and patient autonomy, directly into the design, parameterization, and evaluation of EGT models.
  • Developing data-driven EGT models.
  • Incorporating cognitive biases, heuristics, social influences, and similar avenues.
  • Developing multi-scale and adaptive EGT models.
  • Developing explainable and interpretable EGT Models.

5. Conclusions

This Synthetic Knowledge Synthesis underscores the escalating relevance of evolutionary game theory (EGT) as a vital analytical tool in comprehending and addressing complex dynamics within healthcare systems. Our analysis reveals the robust and rapidly expanding body of literature, with an exponential growth trajectory since 2019, heavily driven by research from China. We identified key research themes, including EGT’s application in cancer research, supply chain management, epidemics, trustworthy public health systems, and collaborative governance, demonstrating its versatile utility across diverse healthcare challenges.
Despite EGT’s proven potential, its broader impact is currently constrained by several critical research gaps. These include fundamental methodological limitations, such as oversimplified assumptions and the need for more realistic contextual and behavioural modelling, particularly during rapidly evolving crises. Furthermore, challenges in technical implementation, governance, and interdisciplinary translation hinder the conversion of theoretical EGT models into actionable clinical and public health solutions. Bridging these gaps necessitates addressing issues of data scarcity, interpretability, and the absence of robust empirical validation frameworks.
To unlock EGT’s full transformative potential in fostering adaptive, personalized, and equitable healthcare systems, future research must adopt a multidisciplinary and multiprofessional approach. Key directions include integrating machine learning for dynamic parameter settings, incorporating longitudinal data and regulatory feedback loops, fostering shared ontologies among diverse professionals, investing in computationally efficient algorithms to solve scalability issues, and developing explicit ethical frameworks to guide model design and evaluation. By proactively addressing these areas, EGT can evolve into an even more powerful instrument for navigating the intricate landscape of modern healthcare.

Author Contributions

Writing—review and editing, Writing—original draft, Supervision, Conceptualization: P.K., H.B.V., J.Z. and B.Ž.; Data analysis, Methodology development, Visualization: P.K. and B.Ž.; Writing—review and editing, Supervision: P.K., H.B.V., J.Z. and B.Ž. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Information 16 00874 g001
Figure 1. Publication trends in evolutionary game theory and healthcare.
Figure 1. Publication trends in evolutionary game theory and healthcare.
Information 16 00874 g001
Information 16 00874 g002
Figure 2. The author keywords-based EGT landscape.
Figure 2. The author keywords-based EGT landscape.
Information 16 00874 g002
Table 1. Most cited publications in the period 2020–2025.
Table 1. Most cited publications in the period 2020–2025.
PublicationNumber of Citations
M., Gelfand, Michèle, J.C., Jackson, Joshua Conrad, X., Pan, Xinyue, D.S., Nau, Dana S., D., Pieper, Dylan, E.E., Denison, Emmy E., M.M., Dagher, Munqith M., P.A.M., van Lange, Paul A.M., C.Y., Chiu, Chi Yue, M., Wang, Mo; The relationship between cultural tightness–looseness and COVID-19 cases and deaths: a global analysis; (2021) The Lancet Planetary Health, 5 (3), pp. e135–e144394
Z., Liu, Zheng, L., Lang, Lingling, L., Li, Lingling, Y., Zhao, Yuanjun, L., Shi, Lihua; Evolutionary game analysis on the recycling strategy of household medical device enterprises under government dynamic rewards and punishments; (2021) Mathematical Biosciences and Engineering, 18 (5), pp. 6434–6451110
K.M., Kabir, K. M.Ariful, J., Tanimoto, J; Evolutionary game theory modelling to represent the behavioural dynamics of economic shutdowns and shield immunity in the COVID-19 pandemic: Economic shutdowns and shield immunity. (2020) Royal Society Open Science, 7 (9), art. no. 109594
M.A., Amaral, Marco A., M.M., Oliveira, Marcelo Mde, M.A., Javarone, Marco A.; An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics; (2021) Chaos, Solitons and Fractals, 143, art. no. 11061692
M, Tianle, H., Yao, Haipeng, N., Zhang, Ni, L., Xu, Lexi, M.M., Guizani, Mohsen Mokhtar, S.S., Guo, Song S.; Cloud Mining Pool Aided Blockchain-Enabled Internet of Things: An Evolutionary Game Approach; (2023) IEEE Transactions on Cloud Computing, 11 (1), pp. 692–70387
Table 2. Themes and thematic categories in EGT research.
Table 2. Themes and thematic categories in EGT research.
Cluster ColourRepresentative Keywords—The Number in Parentheses Represents the Number of Publications in Which a Keyword OccurredCategoriesThemes
Violet (6 author keywords)Game theory (28), cooperation (18), and cancer (5)Game theory in cancer research; cooperation and evolution in game theoryGame theory in cancer research
Green (11 author keywords)Tripartite evolutionary games (12), public health emergency (5), decision making (6), food supply chain (4), and supply chain management (4)Evolution game-based simulation of public health emergency; use of evolution-stable strategy for decision making in food supply chain; and numerical simulation of supply chain management using tripartite evolutionary gameEvolution game-based simulation of supply management
Evolutionary games and prospect theoryEvolutionary game theory (107), COVID (11), and vaccination (6)Evolutionary game theory for COVID-19 vaccination management; evolutionary game theory in SIR developmentEvolutionary game theory in epidemics
Red (14 author keywords)Evolutionary game (157), simulation analysis (10), complex network (9), blockchain (8), public health (6), and emergency management Simulation analysis of using blockchain in trustworthy Internet of Things; regulation of privacy protection; and evolutionary games in public healthEvolutionary games in trustworthy, connected public health
Blue (9 author keywords)Evolutionary games (15), collaborative governance (7), and public health emergencies (6) Evolutionary games/prospect theory in collaborative governance of public health emergencies; four-party evolutionary games use in collaborative governanceEvolutionary games in collaborative governance
Table 3. EGT strategies and techniques.
Table 3. EGT strategies and techniques.
EGT Strategy/TechniqueN
Cooperation19
Simulation analysis17
Tripartite evolutionary game16
Evolutionary stable strategy14
Complex network14
System dynamics12
Prospect theory5
Fitness5
Sir model4
Prisoners dilemma4
Four-party evolutionary game4
Stochastic evolutionary game3
Replicator dynamics3
Moran process3
Information asymmetry3
Table 4. EGT applications.
Table 4. EGT applications.
ApplicationsN
Government regulation, supervision, and intervention14
COVID-1911
Regulation11
Vaccination9
Blockchain8
Collaborative governance7
Public health emergencies6
Decision making6
Cancer5
Public health5
Doctor–patient relationship4
Supply chain management4
Medical data sharing3
Moral hazard3
Food safety3
Value co-creation3
Defence medicine3
Online health community3
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Kokol, P.; Završnik, J.; Blažun Vošner, H.; Žlahtič, B. Evolutionary Game Theory Use in Healthcare: A Synthetic Knowledge Synthesis. Information 2025, 16, 874. https://doi.org/10.3390/info16100874

AMA Style

Kokol P, Završnik J, Blažun Vošner H, Žlahtič B. Evolutionary Game Theory Use in Healthcare: A Synthetic Knowledge Synthesis. Information. 2025; 16(10):874. https://doi.org/10.3390/info16100874

Chicago/Turabian Style

Kokol, Peter, Jernej Završnik, Helena Blažun Vošner, and Bojan Žlahtič. 2025. "Evolutionary Game Theory Use in Healthcare: A Synthetic Knowledge Synthesis" Information 16, no. 10: 874. https://doi.org/10.3390/info16100874

APA Style

Kokol, P., Završnik, J., Blažun Vošner, H., & Žlahtič, B. (2025). Evolutionary Game Theory Use in Healthcare: A Synthetic Knowledge Synthesis. Information, 16(10), 874. https://doi.org/10.3390/info16100874

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