Large Language Model Agent for Managing Patients With Suspected Hypertension

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.

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