Event-Driven Data Processing Systems Using Ai For Real-Time Healthcare Decision Support

Abstract

Modern healthcare delivery demands real-time clinical decision support capable of processing continuous streams of patient data from monitoring devices, electronic health records, and laboratory systems. Traditional batch-oriented data processing systems prove inadequate for time-sensitive clinical scenarios where delayed interventions significantly affect patient outcomes. This research presents the design, implementation, and evaluation of an event-driven data processing system leveraging artificial intelligence for real-time healthcare decision support. The proposed architecture employs event streaming technology to capture physiological data continuously, complex event processing to identify critical patterns, machine learning models for risk prediction, and automated alerting mechanisms for clinical intervention. Through deployment across three healthcare facilities—an intensive care unit, an emergency department, and a general medical ward—serving approximately 850 patients during an 8-month evaluation period, the system demonstrated substantial clinical benefits. Results show 89% accuracy in early deterioration detection, 43-minute average warning time before critical events, 67% reduction in response time to patient deterioration, 58% decrease in adverse events, and 84% clinician satisfaction with alert relevance. The research contributes both a comprehensive event-driven architecture framework for clinical decision support and empirical evidence demonstrating practical feasibility and patient safety improvements. Findings indicate that event-driven AI systems represent a significant advancement over traditional monitoring approaches, enabling proactive rather than reactive clinical care.