Critical Care Analytics: Insights from the Emergency Room
Rajendra M. Jotawar *
Department of MCA, Acharya Institute of Technology, Banglore-560107, India.
Prajwal C K
Department of MCA, Acharya Institute of Technology, Banglore-560107, India.
Rakshitha B
Department of MCA, Acharya Institute of Technology, Banglore-560107, India.
*Author to whom correspondence should be addressed.
Abstract
Emergency departments (EDs) face unpredictable patient surges, fragmented data systems, and operational inefficiencies that delay clinical decision-making and increase patient wait times, staff burnout, and risk of adverse outcomes. This chapter aims to introduce the Critical Care Data Analysis (CCDA) framework—an integrated machine-learning and time-series forecasting system with real-time system-level monitoring—to optimise emergency room (ER) operations. CCDA combines electronic health records, staffing schedules, and environmental data to predict patient volumes, forecast resource shortages, and detect staff burnout. Initial simulations demonstrated measurable benefits, including a 20 % reduction in average wait times, a 15 % improvement in staff allocation efficiency, and a 10 % decrease in equipment-shortage incidents. A real-time Power BI dashboard provides actionable alerts and performance diagnostics. This scalable, data-centric approach offers a robust decision-support tool for improving ER efficiency, resilience, and clinical outcomes.
Keywords: Critical care data analysis, emergency room, machine learning, time-series forecasting, electronic health records, Power BI dashboards