Can agents simulate healthcare system resilience?
Enhancing healthcare infrastructure resilience through agent-based simulation methods
February 11, 2025
https://arxiv.org/pdf/2502.06636This paper proposes an agent-based model (ABM) to simulate healthcare system (HS) resilience, incorporating interdependencies with IT systems. It models disease spread, patient treatment pathways, hospital resource allocation, and the impact of cyberattacks on IT infrastructure supporting healthcare operations. This allows decision-makers to explore "what-if" scenarios and compare different strategies to enhance resilience in the face of compounding threats.
Key points for LLM-based multi-agent systems:
- Agent-based modeling is well-suited for complex systems: ABM's bottom-up approach allows modeling individual patient and hospital behavior, as well as IT system components, capturing emergent behavior at the system level. This aligns well with the modular nature of multi-agent systems and could benefit from the reasoning capabilities of LLMs.
- LLMs can enhance agent decision-making: While the paper uses simple rules for agent behavior, LLMs could provide more sophisticated decision-making within the simulation, for example, in patient triage, resource allocation, or cyberattack response.
- Simulation for strategic exploration: The ABM allows evaluating various contingency strategies, providing insights into their effectiveness. Coupled with LLMs, the simulator could enable more nuanced and adaptive strategies.
- Modeling interdependencies: The paper highlights the importance of modeling interdependencies between HS and IT systems, crucial for multi-agent applications where agents interact and rely on shared resources.
- Data-driven model refinement: While the paper uses parameterized data, it emphasizes the potential for using real-world data to refine the model. This aligns with the data-driven nature of LLMs.