AI-Enabled Serverless Cloud and IoT Integration in Healthcare A Quantum Machine Learning Approach for Adaptive Business Rule Automation and Safety Optimization

Abstract

In the era of connected digital healthcare, the integration of the Internet of Things (IoT) and cloud‑native infrastructures offers compelling opportunities for intelligent decision support. This paper proposes a novel framework titled “AI‑Enabled Serverless Cloud and IoT Integration in Healthcare: A Quantum Machine Learning Approach for Adaptive Business Rule Automation”. In the proposed architecture, IoT‑enabled medical sensors continuously stream patient and environmental data into a serverless cloud pipeline, where preprocessing, feature extraction, and hybrid quantum‑classical inference models are deployed. Concurrently, an adaptive business‑rule automation layer dynamically manages decision logic—translating analytic outputs into actionable, auditable clinical or operational decisions in real time. The quantum machine learning component enables high‑dimensional, complex data analysis (e.g., simultaneous vital‑sign streams, wearable events, EHR triggers) with potential for improved pattern detection and predictive accuracy. The serverless cloud foundation provides scalable, event‑driven compute resource allocation and cost‑efficient deployment of IoT ingestion, inference, and rule execution. The adaptive business rules layer supports dynamic updating of decision logic in response to evolving protocols, analytics feedback and operational context. We present a simulation‑based evaluation of the framework, showing reductions in decision latency, improvements in decision support accuracy against a classical baseline, and enhanced agility of rule‑logic adaptation. We discuss the trade‑offs inherent in such a system—particularly around quantum hardware maturity, latency versus accuracy, data governance, and integration complexity. The findings suggest that this hybrid architecture offers a promising path toward next‑generation real‑time healthcare decision systems—but also highlight substantial practical challenges that must be addressed before broad clinical deployment.