AI Insight
PCRAgent is a multi-agent artificial intelligence framework designed to convert noisy, unstructured clinical conversations between physicians and patients into organized pre-consultation medical records and reusable clinical datasets. The system uses coordinated modules for error detection, semantic editing, and intent recognition to handle common problems in clinical dialogues such as spelling errors, repetitions, and medical ambiguities. In testing with 220,000 perturbed consultations, PCRAgent achieved clinical information accuracy of 4.99 out of 5 and key element completeness of 5 out of 5, outperforming GPT-4o, with expert reviews confirming high clinical accuracy and safety scores.
Why it matters
This technology could significantly improve outpatient efficiency by reducing the time physicians spend organizing patient information before consultations and lowering cognitive burden. It also enables the systematic conversion of real-world clinical conversations into structured data that can be integrated into hospital information systems and used for training medical AI applications, addressing a major barrier to reusing clinical dialogue data.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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 preconsultation 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 preconsultation clinical information organization. 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 PreConsultation 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 220000 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 safety of 4.79 out of 5. Multicenter validation in real-world outpatient workflows further demonstrated practical utility. These findings indicate that PCRAgent can efficiently transform noisy and unstructured consultations into physician ready reports and AI ready structured data, improving outpatient efficiency, reducing cognitive burden, ensuring information completeness, supporting precise decision-making, and enabling high-quality reuse of clinical data.