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Related Experiment Videos

Construction and prototype effect evaluation of a multi-agent collaborative system for operating room nursing.

Yifang Li¹, Jingfei Zou², Ling Wang¹

¹Department of Operating Room, The Second Hospital of Nanjing, Nanjing, Jiangsu, China.

Frontiers in Digital Health

|June 19, 2026

Summary

A new intelligent collaborative management system enhances operating room nursing by optimizing processes and improving efficiency. The system, featuring AI-driven agents and knowledge graphs, achieved high success rates and positive clinical acceptance.

Keywords:

automated machine learning automatic knowledge construction auxiliary decision-making human-machine collaboration multi-agent system operating room management

Related Experiment Videos

Area of Science:

Healthcare Technology
Nursing Informatics
Artificial Intelligence in Medicine

Background:

Operating room management is complex, requiring efficient coordination among nursing teams.
Existing systems may lack advanced capabilities for real-time decision support and process optimization.
The integration of artificial intelligence offers potential solutions for enhancing collaborative workflows.

Purpose of the Study:

To develop an intelligent collaborative management system for operating room nursing.
To define the system's role in assisting nursing teams with intelligent functions.
To evaluate the system's effectiveness in optimizing processes, improving efficiency, and gaining clinical acceptance.

Main Methods:

Designed five position-mapped agents (scheduling, resource, early warning, quality control, interaction) based on nursing practice guidelines.
Integrated a Graph RAG knowledge engine and MindsDB AutoML prediction engine.
Developed automatic knowledge graph construction from nursing documents and a user-friendly interface; conducted simulated tests and surveys with 86 medical staff.

Main Results:

Achieved high success rates: 95.0% for resource conflicts, 90.0% for emergency insertions, 85.0% for equipment failures, and 90.0% for special coordination.
Average solution generation time ranged from 22.3-41.6 seconds.
Overall nursing satisfaction was high (4.32 ± 0.51), with strong scores for process optimization perception (4.40 ± 0.48) and decision support value (4.35 ± 0.52).

Conclusions:

The system effectively integrates knowledge-driven and data-driven intelligence for automatic knowledge graph updates.
It serves as an effective digital assistant for nursing collaboration in the operating room.
The nurse-centric, operating room-adapted design demonstrates wide clinical recognition and offers a human-machine collaboration solution for intelligent nursing management.