Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery

--The integration of voice-based AI agents in healthcare presents a transformative opportunity to bridge economic and accessibility gaps in digital health delivery. This paper explores the role of large language model (LLM)-powered voice assistants in enhancing preventive care and continuous patient monitoring, particularly in underserved populations. Drawing insights from the development and pilot study of Agent PULSE (Patient Understanding and Liaison Support Engine)--a collaborative initiative between IBM Research, Cleveland Clinic Foundation, and Morehouse School of Medicine--we present an economic model demonstrating how AI agents can provide cost-effective healthcare services where human intervention is economically unfeasible. Our pilot study with 33 inflammatory bowel disease patients revealed that 70% expressed acceptance of AI-driven monitoring, with 37% preferring it over traditional modalities. T echnical challenges, including real-time conversational AI processing, integration with healthcare systems, and privacy compliance, are analyzed alongside policy considerations surrounding regulation, bias mitigation, and patient autonomy. Our findings suggest that AI-driven voice agents not only enhance healthcare scalability and efficiency but also improve patient engagement and accessibility. For healthcare executives, our cost-utility analysis demonstrates huge potential savings for routine monitoring tasks, while technologists can leverage our framework to prioritize improvements yielding the highest patient impact. By addressing current limitations and aligning AI development with ethical and regulatory frameworks, voice-based AI agents can serve as a critical entry point for equitable, sustainable digital healthcare solutions. Healthcare systems worldwide face growing challenges in allocating limited medical resources to meet increasing demand [1], [2]. Traditional healthcare delivery models, centered on episodic patient-provider interactions, often result in significant gaps in continuous care, particularly in preventive health monitoring and chronic disease management [2], [3]. These shortcomings disproportionately affect vulnerable populations, including those with limited access to healthcare facilities [4], lower technological literacy [5], or socio-economic constraints [6]. The advent of Large Language Models (LLMs) and multi-modal AI has opened new avenues for digital health applications [7]-[10], notably in voice-based patient engagement [11], [12]. Unlike earlier rule-based conversational agents, modern AI-driven voice assistants can facilitate context-aware, adaptive, and natural conversations that dynamically adjust to user preferences, health literacy levels, and immediate needs [13]. V oice, as humanity's most intuitive mode of communication, reduces engagement barriers and broadens access to healthcare, especially for underserved communities [12], [14].