A Novel Modular Framework for Secure and Scalable Remote Health Monitoring: RHMS

Background: Remote health monitoring for time-critical conditions (e.g., acute stroke) demands rapid, reliable data delivery and immediate clinical interpretation. However, existing Remote Patient Monitoring (RPM) frameworks often exhibit fragmented designs, latency bottlenecks, and integration challenges when onboarding new sensors or clinical algorithms. Methods: To address these gaps, we introduce a unified Remote Health Monitoring System (RHMS) that combines MQTT-driven sensor transport, a pattern-oriented software architecture, and blockchain-based immutable audit logging. Results: In a TRL 3–4 technical feasibility evaluation using synthetic load and a 30 min smartwatch trace, RHMS achieved a median end-to-end latency of 480 ms (IQR 110 ms; P95 < 600 ms) under 500 concurrent 1 Hz streams and a peak throughput of 545 streams/s in controlled environments. The system emits algorithmic risk alerts from an integrated model; no adjudicated clinical diagnoses were performed. A modeled rollup-backed audit log estimates a per-record cost of $0.00016 (USD). Conclusion: RHMS demonstrates technical feasibility and interoperability that aligns with clinical recommendations. Clinical validation is out of scope for this study and will require prospective trials.