Clinical Report: Development and Assessment of a Collaborative Multi-Agent System

Overview

This study presents an intelligent collaborative management system designed to enhance nursing in the operating room. The system demonstrated high efficacy in process optimization and received positive feedback from nursing staff regarding its usability and decision support capabilities.

Background

Nursing is essential to healthcare delivery but faces challenges such as documentation burdens and cognitive overload. The integration of artificial intelligence (AI) in nursing has the potential to transform workflows and improve patient care. This study explores the development of a multi-agent system aimed at alleviating these challenges and enhancing operational efficiency in the operating room.

Data Highlights

Key Findings

• The system achieved a 95.0% success rate in resolving resource conflicts.
• Nursing satisfaction averaged 4.32 on a 5-point Likert scale.
• Top satisfaction scores were noted in process optimization perception (4.40) and decision support value (4.35).
• The system integrates a dual-engine architecture combining knowledge-driven and data-driven approaches.
• Five specialized agents were designed to assist nursing staff while retaining nurses' decision-making authority.

Clinical Implications

The implementation of this multi-agent system can significantly enhance operational efficiency in the operating room, allowing nursing staff to focus more on patient-centered care. The positive feedback indicates that such systems can be effectively integrated into clinical workflows.

Conclusion

The study highlights the potential of AI-driven systems to support nursing roles in the operating room, improving both efficiency and satisfaction. This innovative approach may serve as a model for future developments in healthcare technology.

Related Resources & Content

1. npj Digital Medicine, 2026 -- Benchmarking large language model-based agent systems for clinical decision tasks
2. Springer, 2022 -- Optimizing Robotic Fleet Size and Composition to Balance Cost and Performance in Operating Room Mobile Service Robots
3. Frontiers in Medicine, 2026 -- Development and preliminary evaluation of an AI-enhanced three-dimensional integrated quality model for quality-sensitive indicators in operating room management
4. Springer, 2020 -- Incorporating Autonomous Navigation Systems in Healthcare: Key Guidelines and the ANTS-OR Model
5. AORN, 2025 -- Order 2025 Guidelines for Perioperative Practice
6. ScienceDirect, 2025 -- Machine learning surgery duration predictions compared to traditional methods: A systematic review
7. FDA -- Guidances with Digital Health Content
8. AORN Guidelines for Perioperative Practice
9. Machine learning surgery duration predictions compared to traditional methods: A systematic review - ScienceDirect
10. Guidances with Digital Health Content | FDA