From Passive to Proactive: A Multi-Agent System with Dynamic Task Orchestration for Intelligent Medical Pre-Consultation

• URL: http://arxiv.org/abs/2511.01445v1
• Date: Mon, 03 Nov 2025 10:55:35 GMT
• Title: From Passive to Proactive: A Multi-Agent System with Dynamic Task Orchestration for Intelligent Medical Pre-Consultation
• Authors: ChengZhang Yu, YingRu He, Hongyan Cheng, nuo Cheng, Zhixing Liu, Dongxu Mu, Zhangrui Shen, Zhanpeng Jin,
• Abstract summary: This study introduces a hierarchical multi-agent framework that transforms passive medical AI systems into proactive inquiry agents.<n>We developed an eight-agent architecture with centralized control mechanisms that decomposes pre-consultation into four primary tasks.<n>The framework achieved 87.0% accuracy for primary department triage and 80.5% for secondary department classification.
• Score: 2.4425677941617194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Global healthcare systems face critical challenges from increasing patient volumes and limited consultation times, with primary care visits averaging under 5 minutes in many countries. While pre-consultation processes encompassing triage and structured history-taking offer potential solutions, they remain limited by passive interaction paradigms and context management challenges in existing AI systems. This study introduces a hierarchical multi-agent framework that transforms passive medical AI systems into proactive inquiry agents through autonomous task orchestration. We developed an eight-agent architecture with centralized control mechanisms that decomposes pre-consultation into four primary tasks: Triage ($T_1$), History of Present Illness collection ($T_2$), Past History collection ($T_3$), and Chief Complaint generation ($T_4$), with $T_1$--$T_3$ further divided into 13 domain-specific subtasks. Evaluated on 1,372 validated electronic health records from a Chinese medical platform across multiple foundation models (GPT-OSS 20B, Qwen3-8B, Phi4-14B), the framework achieved 87.0% accuracy for primary department triage and 80.5% for secondary department classification, with task completion rates reaching 98.2% using agent-driven scheduling versus 93.1% with sequential processing. Clinical quality scores from 18 physicians averaged 4.56 for Chief Complaints, 4.48 for History of Present Illness, and 4.69 for Past History on a 5-point scale, with consultations completed within 12.7 rounds for $T_2$ and 16.9 rounds for $T_3$. The model-agnostic architecture maintained high performance across different foundation models while preserving data privacy through local deployment, demonstrating the potential for autonomous AI systems to enhance pre-consultation efficiency and quality in clinical settings.

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