Real-Time Operational Dashboards for Executive Leadership to Drive Agile Decision-Making in Multisite Health Systems

Abstract

Real‐time operational dashboards have emerged as indispensable tools for executive leadership in multisite health systems seeking to align clinical quality, patient safety, and financial performance under rapidly changing conditions. This paper delineates a comprehensive framework for the design, implementation, and continuous enhancement of such dashboards, emphasizing sub‐second data refresh, high‐throughput ingestion of heterogeneous event streams, and actionable analytics for strategic decision support. Our architecture integrates event‐driven microservices, stream processing engines, and in‐memory columnar time‐series stores to synthesize electronic health record transactions, IoT sensor feeds, staffing rosters, and facility telemetry into unified key performance indicators. We develop rigorous mathematical models including continuous‐time state‐space formulations with adaptive Kalman filtering for demand forecasting, multivariate vector autoregressive processes for trend extraction, and mixed‐integer programming for stochastic resource allocation. We further introduce a parameterized rendering pipeline that supports dynamic drill‐down, anomaly detection, and scenario simulation within executive dashboards. Experimental validation on a three‐site hospital network demonstrates sustained ingestion above 120,000 events per second, end‐to‐end dashboard latency below 180 ms, forecast mean absolute percentage error under 4.5 percent, and optimal allocation solutions computed within operational time bounds. We conclude with a detailed discussion of security, governance, and scalability considerations, and propose an extensible reinforcement‐learning extension for closed‐loop capacity management. This work offers a repeatable methodology for health systems to leverage real‐time streaming data in support of evidence‐based executive actions.