CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
Clinical decision-making is inherently complex and fast-paced, particularly
in emergency departments (EDs) where critical, rapid and high-stakes decisions
are made. Clinical Decision Rules (CDRs) are standardized evidence-based tools
that combine signs, symptoms, and clinical variables into decision trees to
make consistent and accurate diagnoses. CDR usage is often hindered by the
clinician's cognitive load, limiting their ability to quickly recall and apply
the appropriate rules. We introduce CDR-Agent, a novel LLM-based system
designed to enhance ED decision-making by autonomously identifying and applying
the most appropriate CDRs based on unstructured clinical notes. To validate
CDR-Agent, we curated two novel ED datasets: synthetic and CDR-Bench, although
CDR-Agent is applicable to non ED clinics. CDR-Agent achieves a 56.3\%
(synthetic) and 8.7\% (CDR-Bench) accuracy gain relative to the standalone LLM
baseline in CDR selection. Moreover, CDR-Agent significantly reduces
computational overhead. Using these datasets, we demonstrated that CDR-Agent
not only selects relevant CDRs efficiently, but makes cautious yet effective
imaging decisions by minimizing unnecessary interventions while successfully
identifying most positively diagnosed cases, outperforming traditional LLM
prompting approaches. Code for our work can be found at:
https://github.com/zhenxianglance/medagent-cdr-agent
