AI-Blockchain Integration for Remote Patient Monitoring and Alerts

Abstract

Remote Patient Monitoring (RPM) has matured from episodic teleconsultations to continuous, sensor-driven care supported by edge analytics and cloud services. Yet three friction points persist: (i) privacy and trust in data handling, (ii) interoperability across fragmented health information systems, and (iii) timely, auditable alerting that can be verified across organizations. This manuscript proposes a reference architecture that fuses Artificial Intelligence (AI) for streaming physiological inference with permissioned blockchain for tamper-evident logging, consent management, and cross-institutional data exchange. The design: (1) captures multi-modal signals from Internet of Medical Things (IoMT) wearables; (2) performs on-device/edge AI for anomaly detection to minimize latency and exposure of raw data; (3) persists summaries and cryptographic hashes on a consortium blockchain while storing large payloads off-chain; (4) implements smart-contract-based consent and alert workflows integrated with HL7® FHIR® resources; and (5) supports privacy-preserving learning (federated learning) to continually improve models without centralizing protected health information (PHI). We synthesize the most recent guidance from WHO and FDA on telemedicine/RPM, FHIR-based interoperability, and global privacy frameworks (HIPAA, GDPR, India’s DPDP Act), and we translate these into concrete design controls. A qualitative evaluation across security, latency, and governance dimensions suggests the approach can reduce alert time, strengthen provenance and auditability, and enable compliant data minimization. We conclude with an implementation roadmap, known risks (e.g., key management, model drift, edge heterogeneity), and future research directions such as zero-knowledge proofs for selective disclosure and formal verification of smart-contracted clinical alerts.