International Journal of Science, Engineering and TechnologyAuthors: Raghav Senmor
Abstract: Real-time decision-making is critical for enhancing patient outcomes, reducing medical errors, and optimizing healthcare operations. The proliferation of IoT-enabled medical devices, including wearables and connected sensors, generates massive streams of heterogeneous patient data, presenting both opportunities and challenges for healthcare analytics. This paper explores the integration of SAP HANA with IoT analytics to enable real-time healthcare decision support. SAP HANA’s in-memory computing and high-speed data processing capabilities allow for rapid ingestion, preprocessing, and analysis of streaming IoT data. By combining predictive and prescriptive analytics, healthcare providers can identify early warning signals, detect anomalies, and deliver timely interventions. The paper discusses system architecture, integration workflows, performance evaluation metrics, and key challenges such as data privacy, interoperability, and scalability. Emerging trends, including edge computing, AI-driven predictive analytics, and cloud-based healthcare platforms, are also highlighted. The findings demonstrate that integrating SAP HANA with IoT analytics facilitates proactive, data-driven clinical decision-making, improves operational efficiency, and supports personalized patient care.
