Automated Clinical Problem Detection from SOAP Notes using a Collaborative Multi-Agent LLM Architecture

2025-08-29

AI Summary

This study addresses inaccurate clinical problem identification and insufficient interpretability in the Subjective (S) and Objective (O) sections of SOAP notes. To tackle these challenges, we propose a multi-agent collaborative diagnostic simulation framework that explicitly models clinical consultation logic through a hierarchical, iterative deliberation mechanism. A manager agent orchestrates task allocation, while domain-specialized agents jointly analyze heterogeneous clinical evidence and reach diagnostic consensus via structured negotiation. Leveraging large language models, the framework enables robust, interpretable multi-agent reasoning for integrating and weighing complex clinical evidence. Evaluated on 420 MIMIC-III SOAP notes, our approach achieves an average 9.2% improvement in F1-score over single-agent baselines for detecting heart failure, acute kidney injury, and sepsis. Crucially, it generates traceable, stepwise reasoning paths—enhancing the accuracy, robustness, and explainability of clinical decision support systems.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Accurate interpretation of clinical narratives is critical for patient care, but the complexity of these notes makes automation challenging. While Large Language Models (LLMs) show promise, single-model approaches can lack the robustness required for high-stakes clinical tasks. We introduce a collaborative multi-agent system (MAS) that models a clinical consultation team to address this gap. The system is tasked with identifying clinical problems by analyzing only the Subjective (S) and Objective (O) sections of SOAP notes, simulating the diagnostic reasoning process of synthesizing raw data into an assessment. A Manager agent orchestrates a dynamically assigned team of specialist agents who engage in a hierarchical, iterative debate to reach a consensus. We evaluated our MAS against a single-agent baseline on a curated dataset of 420 MIMIC-III notes. The dynamic multi-agent configuration demonstrated consistently improved performance in identifying congestive heart failure, acute kidney injury, and sepsis. Qualitative analysis of the agent debates reveals that this structure effectively surfaces and weighs conflicting evidence, though it can occasionally be susceptible to groupthink. By modeling a clinical team's reasoning process, our system offers a promising path toward more accurate, robust, and interpretable clinical decision support tools.

Problem

• Automating clinical problem detection from SOAP notes
• Overcoming limitations of single-model approaches in clinical tasks
• Improving accuracy in identifying specific medical conditions

Innovation

• Multi-agent LLM architecture for clinical analysis
• Hierarchical iterative debate for consensus building
• Dynamic specialist team orchestration for diagnostics