AI-Enhanced DevOps Pipeline for Real-Time Patient Monitoring: Leveraging Databricks Data Intelligence and SAP-Integrated Cloud Workloads
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706009Keywords:
AI-enhanced DevOps, Databricks, SAP S/4HANA, real-time patient monitoring, cloud-native pipelines, healthcare informatics, risk-based automation, data intelligence, continuous deployment, complianceAbstract
This paper presents an AI-enhanced DevOps pipeline framework designed for real-time patient monitoring systems integrated with SAP workloads in cloud environments. Healthcare organisations are increasingly shifting toward continuous patient-data acquisition through wearable sensors, bedside telemetry and remote monitoring devices. Simultaneously, back-end enterprise resource planning (ERP) systems such as SAP S/4HANA handle critical hospital operations, financials and supply-chain management. Integrating these two domains requires secure, scalable and intelligent pipelines that support continuous deployment and monitoring while preserving clinical safety and compliance. The proposed model embeds artificial intelligence and data analytics, powered by Databricks Lakehouse architecture, into each stage of the DevOps pipeline to optimise build, test, deployment and monitoring activities. AI models dynamically assess risk, predict anomalies and prioritise testing for SAP-linked microservices and patient-data APIs. The Databricks platform enables the fusion of structured (ERP) and unstructured (patient telemetry) data to provide real-time observability, feedback and adaptive testing. The research introduces a case study of a multi-cloud hospital infrastructure that integrates patient telemetry with SAP-based logistics and billing workflows. The pipeline’s performance is evaluated in terms of deployment frequency, mean time to recovery (MTTR), incident prediction accuracy and regulatory compliance traceability. Results demonstrate substantial reductions in production failures and improved latency management. This paper contributes to the field by establishing a cloud-native, AI-driven DevOps architecture tailored to the dual demands of healthcare informatics and enterprise ERP integration.
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