Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery (2507.16229v1)

Abstract: 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 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. Technical 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.

• The paper presents Agent PULSE, demonstrating that voice-based AI agents can provide cost-efficient monitoring and bridge healthcare disparities.
• It details a cost-utility analysis showing significant cost reductions and a 70% patient acceptance rate for AI-driven interventions.
• It outlines technical challenges and policy considerations, emphasizing natural language processing to optimize healthcare accessibility and equity.

Voice-Based AI Agents: Bridging Economic Gaps in Digital Health Delivery

This paper addresses the challenges of healthcare resource allocation and accessibility by exploring the potential of voice-based AI agents, powered by LLMs, to fill economic gaps in digital health delivery. The authors present Agent PULSE (Patient Understanding and Liaison Support Engine), a collaborative initiative between IBM Research, Cleveland Clinic Foundation, and Morehouse School of Medicine, as a case paper for demonstrating the cost-effectiveness and patient acceptance of AI-driven monitoring. The paper highlights the technical challenges, policy considerations, and opportunities for enhancing healthcare scalability, efficiency, and equity through voice-based AI agents.

Addressing Healthcare Inequities with Voice-Based AI

The paper begins by outlining the existing disparities in healthcare access, particularly for vulnerable populations with limited access to facilities, lower technological literacy, or socio-economic constraints. Traditional healthcare models often fall short in providing continuous care, preventive health monitoring, and chronic disease management, exacerbating these disparities. Voice-based AI agents offer a solution by leveraging the ubiquity of telephone technology and the advancements in LLMs to facilitate context-aware, adaptive, and natural conversations. Voice interaction reduces engagement barriers and broadens access to healthcare, especially for underserved communities.

The Natural Interface for Healthcare Interaction

The authors emphasize voice as the most intuitive form of human communication, overcoming the barriers posed by text and graphical user interfaces, especially for older adults and people with disabilities. LLMs enable natural language interactions, allowing patients to state their needs and receive personalized assistance without navigating complex menu-driven systems. This technology can replace traditional interactive voice response (IVR) systems, plagued by frustrating menu-driven interactions, with AI-driven systems that improve user experience and accessibility. The near-universal penetration of telephone technology makes voice-based systems particularly powerful in addressing healthcare disparities, reaching populations without specific devices, Internet access, or technological proficiency.

An Economic Model for AI-Driven Healthcare Interventions

The paper introduces an economic model to illustrate how AI-driven interventions can become economically viable in healthcare resource allocation. The model, based on cost-utility analysis, demonstrates how AI agents can enhance patient monitoring in scenarios where human medical expertise is either unavailable or economically unjustifiable. Key components of the model include:

• $C_h$: Cost of human-provided care
• $C_a$: Cost of AI-powered interventions
• $E = \frac{C_h - C_a}{C_h}\times 100\%$: Cost-efficiency ratio for AI-driven monitoring
• ICER (Incremental Cost-Effectiveness Ratio): $$\text{ICER} = \frac{C_a - C_h}{\text{QALY}_a - \text{QALY}_h}$$, where QALY$_a$ and QALY$_h$ represent quality-adjusted life years generated by AI-based and human-based interventions, respectively.

The model shows that AI can efficiently fill care gaps, particularly during lower-severity periods when human medical resources would be economically unjustifiable yet monitoring remains beneficial. AI-driven patient monitoring can significantly reduce per-patient costs, making preventive care economically viable for larger populations.

Agent PULSE: Experimental Validation of AI-Driven Healthcare

The authors present Agent PULSE, a voice-based AI system designed to conduct medical surveys and monitor patient conditions through natural conversation, as a real-world implementation of their economic theory. Agent PULSE targets the "blue zone" where human medical resources are economically unjustifiable yet monitoring remains beneficial. The system's architecture consists of a voice interface, an AI engine powered by IBM's watsonx platform, and a physician dashboard.

A pilot paper with 33 patients from Morehouse School of Medicine (MSM) with inflammatory bowel disease (IBD) revealed encouraging patient receptivity to AI-driven healthcare interactions. The paper found that 70% of patients expressed acceptance of the AI chatbot communication modality. Data completeness analysis revealed variations across question categories, with questions about daily activities and daily life impact achieving the highest completion rates (94.4%). The paper also identified workflow advantages for healthcare providers, including reduced administrative burden and standardized data collection.

Technical Challenges and Roadmap for Voice-Based AI Agents

The authors identify several technical challenges that must be addressed to achieve scalable, reliable, and effective voice-based AI health agents, including:

• Efficient Conversation Management: Improving memory management techniques to reduce AI response times and maintain natural flow in conversations.
• Infrastructure Optimization: Optimizing infrastructure across inbound and outbound call patterns to maximize resource utilization.
• Privacy, Security, and Compliance: Developing reference architectures and best practices for deploying voice-based AI health systems that meet regulatory requirements.
• Personalization and Adaptation: Adapting to individual patient communication styles, preferences, health literacy levels, and cultural contexts.
• Retrieval-Augmented Generation (RAG): Combining the generative capabilities of LLMs with structured retrieval of information from specialized domain-specific knowledge bases to deliver accurate, reliable, and evidence-based health guidance.

Conclusion: Implications for Healthcare Stakeholders

The paper concludes by emphasizing the transformative potential of voice-based AI agents in healthcare delivery. Key implications for various stakeholders include:

• Healthcare Executives and Administrators: Implementing voice-based AI can significantly extend care reach while reducing per-patient monitoring costs.
• Healthcare Professionals: AI voice systems can complement their expertise by automating routine monitoring tasks and freeing them to focus on complex cases.
• Patients: Voice-based AI health systems offer unprecedented convenience, privacy, and consistent interaction quality, removing traditional barriers to healthcare access.
• Technologists and Developers: Priority areas for innovation include optimizing session management, developing infrastructure that efficiently handles patient interactions, and creating personalization frameworks.
• Policymakers and Regulators: Ethical considerations like ensuring AI complements human connection, seamless escalation to human providers, and vigilant monitoring for algorithmic bias are critical.

The authors advocate for multidisciplinary collaboration among clinicians, technologists, policymakers, and patients to maximize voice-based AI's potential as an entry point for equitable, sustainable healthcare delivery.