aceLongCAF: A longitudinal context-constructive agentic framework for LLM-supported hypertension self-management
Objective Long-term hypertension self-management requires patients to make daily decisions and be cautious of when to seek medical attention. Although large language model (LLM)-based systems provide new opportunities for conversational self-management support, conventional question-answering conversational paradigms may be insufficient for providing accurate and personalized responses under conditions of fragmented patient expression, incomplete contextual reporting, and changing interaction intents. Therefore,…
In a 6-month real-world deployment, an LLM framework built the patients' longitudinal context and escalated safety-critical interactions to clinicians with high precision and recall.
The study reports feasibility in a deployment and does not show effects on clinical outcomes; the evaluation evidence is described as suggestive.
Evidence
- Peer-reviewedJournal of Biomedical Informatics2026-10-08
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Truvace Impact Record TRV-2026-1690, v1: “LongCAF: A longitudinal context-constructive agentic framework for LLM-supported hypertension self-management.” Truvace, 2026-10-17. /record/TRV-2026-1690 (accessed at citation time). sha256 686eb9ae3ea2622e…
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