Multi-Agent Models in Healthcare System Design

Introduction

The integration of multi-agent models (MAS) into healthcare system design signifies a significant advancement at the intersection of artificial intelligence (AI) and healthcare. This progression is rooted in the escalating complexities of healthcare systems influenced by technological advancements, demographic shifts, and heightened patient expectations (Brown & Miller, 2018). Traditional healthcare management models, predominantly characterised by hierarchical structures, have struggled to adapt to the dynamic and interconnected nature of contemporary healthcare environments. Consequently, the exploration of innovative solutions, including the application of MAS, has recently gained attention (Tariq, 2024).

The evolution of healthcare systems from isolated entities to interconnected networks necessitates dynamic and flexible approaches. This evolution is situated within the historical development of healthcare systems and acknowledges the challenges that have emerged over time. Traditional hierarchical models have proven inadequate for addressing the intricate relationships between various components of the healthcare environment (Jones et al., 2019). These limitations have spurred a re-evaluation of existing optimal models, leading to the exploration of MAS to introduce adaptability and responsiveness to healthcare system design (Raimi et al., 2022).

The introduction of MAS in healthcare is intertwined with the broader landscape of artificial intelligence and computational modelling. Historical milestones have played a pivotal role in shaping the trajectory of MAS applications in health care. Advances in agent-based modelling and decision support systems have laid the groundwork for the integration of MAS (Smith & White, 2020). The evolution of AI technologies and these advancements have provided the necessary foundation for exploring MAS within the healthcare domain.

Real-world examples highlight the practical implications of MAS in healthcare system designs. MAS have been utilised to optimise resource utilisation, staff scheduling, and bed allocation in hospital management. Johnson et al. (2017) demonstrated the effectiveness of MAS in dynamically allocating resources based on real-time patient needs, resulting in improved efficiency and reduced waiting times. This application exemplifies how MAS can address the complex and dynamic nature of healthcare resource management (Tariq & Ismail, 2024).

Patient care coordination represents another area in which MAS has demonstrated significant benefits. For instance, managing chronic diseases requires collaboration between various health care providers and diagnostic services. Implementing MAS in this context facilitates seamless communication and collaboration, leading to personalised and timely intervention (Anderson et al., 2018). The MAS serves as an enabler for enhanced patient care coordination in situations where traditional models may fall short.