Large Language Model Agent for Managing Patients With Suspected Hypertension
Yijun Wang, Wuping Tan, Siyi Cheng, Chen Peng, Peng Jin, Fanglin Qin, Long Tang, Tongjian Zhu, Bing Wu, Jinjun Liu, Jun Wang
Abstract
BACKGROUND: The effectiveness of Large Language Model agent frameworks for hypertension screening and personalized health management has not been fully studied. This study aimed to develop and evaluate a Large Language Model–based Agent, called the Cascade Framework, and assess its effectiveness in hypertension education and clinical decision support. METHODS: The Cascade Framework was developed utilizing the Dify platform, and its performance was tested via a robust 2-phase evaluation protocol from August 2024 to June 2025. The first phase involved systematic performance benchmarking of 6 configurations: 3 foundational Large Language Models (Chat Generative Pretrained Transformer [ChatGPT]-4o, ChatGPT-4oMini, and DeepSeek-V3) and their respective Cascade-enhanced versions. The second phase included an external validation in a cohort of patients with suspected hypertension. RESULTS: Cascade integration yielded significant performance improvements across all models. For ChatGPT-4o, educational outcomes improved (Accuracy: 3.87→4.10, P =0.02; Comprehensiveness: 4.07→4.32, P =0.16; Credibility: 3.79→4.03, P <0.001; Understandability: 3.90→3.96, P =0.005; Emotional Support: 3.87→4.01, P <0.001). Blood pressure classification accuracy rose from 62.5% to 87.0% ( P <0.001) and risk factor stratification from 60.4% to 98.6% ( P <0.001). Clinical decision-making improved, with accuracy of 72.0% to 92.5%. A similar trend of performance improvement was observed in the external validation cohort, where the 4o-Cascade model achieved increases in blood pressure classification accuracy (58.9%→95.3%), risk stratification accuracy (71.0%→90.7%), and clinical decision appropriateness (66.4%→92.5%), all with P <0.001 and surpassing the performance of the 3 physicians. CONCLUSIONS: Cascade Framework can improve the management of hypertension. Its extensible architecture allows integration with existing clinical workflows while providing transparent reasoning pathways.