Intelligent Data Engineering Pipelines Using AI for Smart Hospital Operations

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Published: 2023-12-19
DOI: https://doi.org/10.15662/IJRPETM.2023.0606019
Keywords:
Intelligent Hospital Data Pipelines, AI-Driven Hospital Operations, Healthcare Data Integration Architectures, Real-Time Patient Flow Management, Hospital Resource Planning Analytics, External Demand Forecasting in Healthcare, Clinical and Operational Data Fusion, Advanced Healthcare Analytics, Predictive Modeling for Hospital Management, Data Quality and Model Governance in Healthcare, Operational Bottleneck Identification, Scalable Health Data Ecosystems, Healthcare Performance Optimization, Real-Time Decision Support Systems, AI-Enabled Healthcare Resource Utilization

Main Article Content

Nareddy Abhireddy
Independent Researcher, India

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.

Article Details

Issue

Vol. 6 No. 6 (2023): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Intelligent Data Engineering Pipelines Using AI for Smart Hospital Operations. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9755-9768. https://doi.org/10.15662/IJRPETM.2023.0606019

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