Intelligent Data Engineering Pipelines Using AI for Smart Hospital Operations

Abstract

Hospitals generate large amounts of valuable data. Building intelligent data pipelines that support the entire hospital data ecosystem can greatly improve hospital operational performance. These pipelines connect data from various sources, such as electronic health records, imaging and laboratory results, scheduling systems, and sensor networks. Advanced analytics and AI models can provide new insights and forecasts about patient and resource flows. Based on these insights, hospitals can improve decision-making for real-time operations, resource planning, and external demand forecasting. Real-world experience shows that using a structured data pipeline approach is essential. Issues can arise with data quality, model accuracy, and operational bottlenecks. Nevertheless, the potential benefits are substantial: improved patient flow, increased resource utilization, enhanced service reliability, and reduced waiting times. Intelligent data pipelines can strengthen AI-powered hospital operations. Specifically, they support the three main operational workflows: real-time management of patient flow in the hospital, planning of internal resources (staffing and equipment), and resourcing of external demand (forecasting of patient arrivals). For each workflow, the required data, corresponding transformations, planned analysis, and expected performance metrics are described. Finally, practical deployment experience is synthesized to highlight key success factors and common pitfalls.

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