Vol. 3 No. 2 (2023): Journal of Machine Learning for Healthcare Decision Support
Articles

Real-Time AI-Powered Predictive Analytics for Emergency Room Management

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  • Dr. Marco Rossi
Dr. Marco Rossi
Professor of Information Engineering, University of Pisa, Italy
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Published 04-12-2023

Keywords

  • Predictive Analytics,
  • Emergency Room Management
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Marco Rossi, “Real-Time AI-Powered Predictive Analytics for Emergency Room Management”, Journal of Machine Learning for Healthcare Decision Support, vol. 3, no. 2, pp. 100–116, Dec. 2023, Accessed: Jan. 22, 2025. [Online]. Available: https://medlines.uk/index.php/JMLHDS/article/view/51

Abstract

Overcrowding is a global problem facing the healthcare industry, and nowhere is this felt more keenly than in emergency departments (EDs). Moreover, emergency room staff find themselves needing to respond to more complex acuity each year. Many healthcare facilities are moving to electronic records, which has helped collate vast quantities of clinical data from complex patients, bringing together information from different sources. This has given rise to the increased interest in using analytic technology to help drive improvements in healthcare facilities. Indeed, healthcare analytics are expected to reach $67.82 billion by 2025, as the desire to use such insights to drive performance is growing across the sector.

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