Vol. 3 No. 2 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

Real-Time AI-Powered Systems for Enhancing Hospital Infection Control: Utilizing Machine Learning to Monitor and Manage Infection Risks and Outbreaks

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  • Dr. Ingrid Gustavsson
Dr. Ingrid Gustavsson
Associate Professor of Human-Computer Interaction, University of Gothenburg, Sweden
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Published 13-12-2023

Keywords

  • Hospital Infection Control,
  • Infection Risks and Outbreaks
Creative Commons License

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

How to Cite

[1]
Dr. Ingrid Gustavsson, “Real-Time AI-Powered Systems for Enhancing Hospital Infection Control: Utilizing Machine Learning to Monitor and Manage Infection Risks and Outbreaks”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 2, pp. 87–100, Dec. 2023, Accessed: Nov. 23, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/52

Abstract

Hospitals are some of the main hotspots for disease-causing microorganisms and need to be able to quickly and effectively respond to managing infection risks and outbreaks. Technological interventions can often lead to radical and transformative changes in the functioning of social systems, and hospital surveillance and infection control is no exception. In particular, the application of machine learning has gradually begun to revolutionize the infection control domain. The objective of this survey text is to discuss how advanced artificial intelligence-powered solutions can be used to monitor, model, and manage hospital-associated infections. The easy and ready availability of healthcare data, combined with sensor-to-cloud communication and analytic services, rolls out a new paradigm for deploying such interventions in real life. A healthcare facility is one of the principal hotspots of contagious diseases. The rise in infection rates, despite investment in healthcare infrastructure around the world, is raising questions about the efficacy of the disease control measures put in place. Consequently, managing such risks has become an important component of healthcare provision services. The importance of timely surveillance data is especially critical in healthcare response, thereby making quicker the personalization of outbreak surgical decision-making. Emerging technologies involve appealing to infectious disease modeling techniques and predictive analytics. Despite the potential of these technologies, no systematic survey discussing the utilization of advanced algorithms exists to handle hospital systems and disease interaction. In this work, we bring forward the potential laid by these technologies and introduce the reader to the multitude of uses. Infections have posed a significant rise in the aging and vulnerable population, especially resulting from problems of antibiotic resistance. Public health strategies to reduce the risk of such infections are numerous and could be coordinated by public health authorities, local health jurisdictions, and other healthcare networks.

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