AI-driven endpoint automation for patient monitoring: Transforming healthcare infrastructure

Koneru, Sri Harsha (2025) AI-driven endpoint automation for patient monitoring: Transforming healthcare infrastructure. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1291-1298. ISSN 2582-8266

This article explores an innovative enterprise architecture that leverages artificial intelligence and endpoint automation to revolutionize patient monitoring in modern healthcare environments. Contemporary healthcare facilities face significant challenges managing data from thousands of distributed endpoints, including medical IoT devices, wearables, clinical workstations, and environmental systems. The proposed three-tiered architecture addresses these challenges by integrating intelligent endpoint automation at the device level, HIPAA-compliant cloud integration for data aggregation and analysis, and human-AI collaborative interfaces for clinical decision support. This article distributes computational responsibilities across the technology stack, enabling edge-based preliminary analysis, cloud-powered population-level insights, and intelligent prioritization of alerts for clinicians. Implementation benefits extend to multiple stakeholders: healthcare professionals experience reduced administrative burden and enhanced decision support; patients receive earlier interventions, personalized care, and reduced complications; while healthcare organizations gain operational efficiencies, improved regulatory compliance, and positive return on investment. Technical implementation considerations include robust network infrastructure, comprehensive security frameworks, effective integration strategies, and thoughtful change management approaches. The architecture represents a balanced approach that strategically automates routine tasks while preserving the irreplaceable value of clinical judgment in healthcare delivery.