A multi-agent large language model (LLM) platform for structured medical anamnesis in outpatient care.

Abstract

Introduction: Medical anamnesis is fundamental to clinical decision-making, yet outpatient consultations often provide limited time for systematic information collection. This study investigates whether structured anamnesis acquisition can be redistributed to a pre-consultation phase using a framework-driven large language model (LLM) platform.

Methods: We developed Symptio, a proof-of-concept multi-agent LLM platform that operationalises the SOCRATES, Review of Systems, and SAMPLE frameworks as machine-interpretable constraints within a human-in-the-loop workflow. The system was evaluated in 100 simulated outpatient scenarios. Generated anamneses were compared with reference-standard representations using structural agreement and field-level performance metrics.

Results: Full structural agreement across all three frameworks was achieved in 87% of scenarios. Framework-specific agreement reached 96% for SOCRATES, 88% for Review of Systems, and 93% for SAMPLE. Micro-averaged precision, recall, and F1-score were 99.04%, 98.54%, and 98.79%, respectively. Although no high-severity errors were identified, 58 field-level discrepancies, including 28 medium-severity errors, were observed, supporting the need for physician review of all outputs.

Discussion: The findings demonstrate the technical feasibility of acquiring structured anamnesis in a framework-driven manner within a process-oriented multi-agent LLM architecture under controlled conditions. The proposed approach enables the relocation of routine information collection to a pre-consultation phase while preserving physician authority through human-in-the-loop validation. Further studies are required to evaluate clinical safety, workflow impact, and effectiveness in real-world settings.