Automated Clinical Problem Detection from SOAP Notes using a Collaborative Multi-Agent LLM Architecture
This work addresses the problem of accurate clinical decision support for healthcare professionals, though it is incremental as it builds on existing LLM methods with a team-based approach.
The paper tackled the challenge of automating clinical problem detection from SOAP notes by introducing a collaborative multi-agent LLM system, which improved performance in identifying conditions like congestive heart failure, acute kidney injury, and sepsis on a dataset of 420 MIMIC-III notes compared to a single-agent baseline.
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.