Agent PULSE: AI Patient Support

• Agent PULSE is a voice-based, conversational AI system that bridges digital health gaps by supporting routine monitoring and patient engagement.
• It integrates advanced large language models with real-time voice interfaces and secure healthcare IT systems for efficient care delivery.
• Its scalable architecture automates symptom checking and continuous monitoring, reducing clinical workload and lowering costs.

Agent PULSE (Patient Understanding and Liaison Support Engine) is a voice-based and conversational AI agent designed to enhance patient monitoring, communication, and support in healthcare settings, with a particular focus on bridging access and resource gaps in digital health delivery. Developed through collaborations among IBM Research, Cleveland Clinic Foundation, and Morehouse School of Medicine, Agent PULSE is presented as both an economically efficient and clinically valuable technology for scalable, patient-centered engagement and routine healthcare monitoring (Wen et al., 22 Jul 2025). Its design synthesizes LLM-driven dialogue, real-time voice interfaces, and secure integration with healthcare IT systems, targeting underserved and general populations for preventive care, chronic disease management, and beyond.

1. System Architecture and Core Workflow

Agent PULSE is built as an LLM-powered conversational agent that operates via telephone or messaging systems, delivering natural, human-like interaction for medical surveys, symptom checking, and continuous patient monitoring (Wen et al., 22 Jul 2025). The architecture emphasizes:

• Voice First, Conversational Design: The conversational engine integrates speech-to-text (STT) and text-to-speech (TTS) modules, supporting both synchronous (real-time) and asynchronous (scheduled or on-demand) patient interactions. Advanced LLM models (e.g., GPT-3.5, GPT-4o) are employed for natural language comprehension and dialogue generation.
• Dialogue Management and Session Efficiency: Techniques such as Key-Value (KV) cache optimization and efficient session memory management are implemented to reduce computation, decreasing response latencies by a factor of 2–3 compared to standard approaches (Wen et al., 22 Jul 2025).
• Scalable Infrastructure: The system is designed to manage both inbound and outbound calls, dynamically allocating resources to accommodate spikes in usage while minimizing waiting times.
• Healthcare IT Integration: Interfaces are built for compatibility with Electronic Health Records (EHRs), supporting real-time data ingestion, secure storage, and interoperability.
• Privacy and Security Compliance: The system enforces data protection standards (HIPAA, GDPR) through end-to-end encryption, secure audit trails, and robust access controls.

The core workflow consists of initial patient engagement, ongoing symptom monitoring, risk assessment, and timely escalation of care when indicated. Agent PULSE supports both routine tasks (e.g., chronic condition check-ins) and more complex patient query resolution, acting as both a front-line monitor and a liaison to human providers when needed.

2. Patient Engagement, Adoption, and User Experience

Empirical studies and surveys indicate strong patient acceptance, with 70% of pilot participants expressing comfort with AI-driven monitoring and 37% preferring the AI agent over traditional human follow-up methods (Wen et al., 22 Jul 2025). Key determinants of engagement include:

• 24/7 Accessibility: Patients value round-the-clock availability, overcoming the time and logistical barriers of traditional care (Moradbakhti et al., 22 Jul 2025).
• Familiar Platform Integration: Deployments on widely used messaging services such as WhatsApp increase usability and engagement, especially among populations with variable digital literacy (Moradbakhti et al., 22 Jul 2025).
• Personalization and Tone: The system adapts dialogue to user preference, supporting both friendly, reassuring styles (nurse-like) and more direct professional styles, per patient choice or clinical context.
• Self-Management Support: For chronic disease contexts (e.g., asthma, IBD), PULSE delivers individualized education, self-assessment, and real-time monitoring, matching the needs of users less confident in self-management or perceiving higher disease severity (Moradbakhti et al., 22 Jul 2025).
• Efficient and Consistent Data Collection: Automated, conversational monitoring yields consistent, longitudinal data quality while reducing staff workload.

Obstacles such as privacy concerns and skepticism of AI capability are addressed through transparent security practices, patient education, and the option for seamless handoff to human care where necessary.

3. Economic Impact, Cost Analysis, and Healthcare Scalability

Agent PULSE explicitly addresses the economic inefficiencies of conventional health delivery through automation and scalability. The economic model formalizes cost efficiency via the ratio

$E = \frac{C_h - C_a}{C_h} \times 100\%$

where $C_h$ is the cost of traditional, human-provided care, and $C_a$ is the cost of the AI-driven intervention (Wen et al., 22 Jul 2025). For broader health economic evaluation, the Incremental Cost-Effectiveness Ratio (ICER) is used:

$ICER = \frac{C_a - C_h}{QALY_a - QALY_h}$

where $QALY$ refers to quality-adjusted life years for AI and human modalities respectively.

• Resource Allocation: By automating routine tasks unsuitable for regular human intervention (the “blue zone” in disease risk management), significant per-patient and aggregate savings are realized, particularly when economies of scale are exploited.
• Clinical Workforce Optimization: Freed clinical staff can focus on higher-acuity or specialized care, with AI handling baseline monitoring and patient education.

These analyses are supported by empirical pilot data demonstrating reduced staff workload and improved patient coverage, particularly for chronic disease follow-up.

4. Technical Challenges and Implementation Considerations

Practical deployment of Agent PULSE confronts several technical challenges:

• Latency and Real-Time Constraints: Achieving smooth, natural response times for voice and chat requires memory-efficient LLM operation, prompt engineering for rapid context switching, and infrastructure for dynamic load balancing (Wen et al., 22 Jul 2025).
• Healthcare Data Integration: Structural heterogeneity in EHR systems necessitates flexible data transformation and interoperability modules.
• Privacy and Security: Continuous, voice-based engagement requires robust end-to-end encryption, strict access controls, and comprehensive audit trailing to remain compliant with privacy regulations.
• Bias Mitigation and Algorithmic Fairness: To ensure equitable care delivery, policy frameworks and internal checks must be implemented to monitor disparities across user demographics.
• Scalability and Scheduling: The infrastructure must support fluctuations in demand, scaling resources during scheduled mass outreach (e.g., outbound monitoring calls) and unpredictable inbound surges.

These implementation strategies are critical for maintaining clinical safety, trust, and system resilience in real-world healthcare settings.

5. Policy, Ethics, and Regulatory Alignment

Deployment and scaling of Agent PULSE are governed by ethical and regulatory frameworks that guarantee patient autonomy and equitable access:

• Regulatory Compliance: Conformance with HIPAA, GDPR, and local health privacy standards is mandatory. Data encryption, minimal personally identifiable information (PII) collection, and user opt-in/out mechanisms are standard.
• Patient Autonomy and Safety: Clear explanations of the agent’s role, privacy protections, and escalation pathways to human care are necessary for ethical operation and user trust.
• Transparency and Accountability: The system maintains explicit documentation of AI decision-making and outputs, with pathways for auditing and clinician oversight.
• Bias Detection and Fairness: Regular algorithmic audits for disparate impact by demographic are required to prevent reinforcement of existing healthcare inequities.

Policy guidelines urge integration with trusted healthcare organizations and the communication of AI system limitations to patients.

6. Clinical Impact, Outcomes, and Future Directions

Agent PULSE represents a critical entry point for democratizing digital health access, notably for preventive care and the long-term monitoring of chronic conditions (Wen et al., 22 Jul 2025, Moradbakhti et al., 22 Jul 2025). Its deployment leads to:

• Improved Patient Engagement: Users report greater satisfaction, perceived support, and more frequent interaction compared to standard modalities, especially in medically underserved or digitally less literate populations.
• Operational Efficiency: Systematic automation supports patient panels at scales unfeasible for human staff, reduces care gaps, and enables timely escalation of detected risks.
• Cost Reduction: Quantified by cost-efficiency and cost-utility models, health systems benefit from substantial savings for low-acuity, high-volume monitoring tasks.
• Scalability and Equity: Voice- and chat-based delivery ensures inclusivity, with broad reach across socioeconomic and demographic groups.
• Continuous Feedback and Iterative Improvement: Built-in analytics and session tracking allow ongoing monitoring and adjustment of system performance, bias, and patient satisfaction.

Future directions include further integrating Agent PULSE with advanced clinical decision support systems, expanding to voice-activated severity detection (e.g., for asthma), adapting to additional chronic illnesses, and leveraging structured evaluation for regulatory validation (Wen et al., 22 Jul 2025, Moradbakhti et al., 22 Jul 2025).

7. Comparative Positioning and Recommendations

Agent PULSE shares core principles with other conversational health agents (e.g., PAL for palliative care, asthma-specific chatbots), but is distinctive in its:

• Commitment to economic scalability and cost modeling (Wen et al., 22 Jul 2025)
• Emphasis on voice-first accessibility and support for diverse populations
• Integration with clinical workflows for both patients and staff
• Implementation of technical, regulatory, and ethical best practices for digital health deployment

In summary, Agent PULSE operationalizes the goals of scalable, trustworthy, and inclusive digital health by combining advanced AI-driven dialogue with secure and efficient healthcare integration. The system demonstrates both clinical and operational effectiveness in bridging care gaps, reducing labor intensity, and supporting sustainable, equitable healthcare delivery in a variety of contexts (Wen et al., 22 Jul 2025, Moradbakhti et al., 22 Jul 2025).