Внедряване на агенти на ИИ (AI) за управление на сънна апнея в американска цифрова здравна платформа: архитектура, избор на ГЕМ (LLM) и бизнес стойност
Deploying AI Agents for Sleep Apnea Management in a U.S. Digital Health Platform: Architecture, LLM Selection, and Business Value

Summary/Abstract: Sleep apnea remains a major and frequently underdiagnosed public health challenge. Epidemiological estimates indicate that obstructive sleep apnea (OSA) affects nearly 936 million adults worldwide (Benjafield et al. 2019). Home Sleep Apnea Testing (HSAT) devices have expanded the possibilities for out-of-laboratory assessment and generate structured clinical data that may support intelligent workflow automation. This paper presents a reference technical architecture for deploying AI agents within a U.S. digital health platform specializing in sleep apnea care. The proposed system is built around a self-hosted large language model, specifically Llama 3.1 8B, served via vLLM on AWS GPU infrastructure and integrated with an existing Node.js/NestJS backend through a standardized REST API. To support a HIPAA-aligned deployment, the agent is restricted to structured patient data such as screening questionnaire results, AHI values, and CPAP adherence indicators and does not access raw physiological signals. The paper examines the infrastructure configuration, LLM selection criteria, and potential business value. Under the stated AWS pricing assumptions, the core GPU and model-storage infrastructure is estimated at approximately $295 per month; this figure is illustrative and does not represent total cost of ownership. The proposed architecture requires task-specific clinical, security, and regulatory validation before production use.

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