A Multi-Agent clinical pre-consultation system for structuring noisy patient reported information into clinical reports and AI-ready data

In primary care and outpatient settings, clinically important patient information is often embedded in fragmented, ambiguous, repetitive, and noisy communication between physicians and patients. This limits physicians’ ability to obtain a clear pre-consultation overview of symptoms, history of present illness, and visit intent, while also preventing real world clinical dialogues from being reused in hospital information systems and medical artificial intelligence applications. To address this challenge, we developed PCRAgent, a centrally coordinated multi agent framework for pre-consultation clinical information organization, shifting information processing upstream from the consultation. Guided by physician inquiry logic, PCRAgent identifies, extracts, corrects, and standardizes patient-reported information from noisy consultations. Its coordinated modules including error detection, semantic editing, output control, contextual memory, and intent recognition enable robust parallel handling of spelling errors, repetitions, grammatical inconsistencies, medical ambiguities, and non-medical interference. A traceable edit list records intermediate corrections and context, allowing iterative refinement without redundant modifications. PCRAgent generates two complementary outputs. One is a Pre-Consultation Clinical Report for rapid physician review. The other is a Structured Clinical Conversation Dataset for hospital data construction and downstream AI applications. In evaluations using 220,000 strongly perturbed consultations, PCRAgent maintained high robustness, achieving a clinical information accuracy of 4.99 out of 5 and key element completeness of 5 out of 5, outperforming GPT4o. Expert review of Chinese and English dialogues confirmed high clinical accuracy of 4.85 out of 5 and high security of 4.79 out of 5. Multicenter validation in real world outpatient workflows further demonstrated practical utility. These results indicate that PCRAgent improves outpatient workflow efficiency, reduces physicians’ cognitive burden, ensures completeness of pre-consultation clinical information, supports more focused and accurate clinical decision-making, and enables high-quality reuse of clinical data for downstream medical artificial intelligence applications.