Automated Intelligent Mutation of COVID-19 Management: A Revolutionary Connect


Authors : Reshma Murali

Volume/Issue : Volume 9 - 2024, Issue 11 - November


Google Scholar : https://tinyurl.com/4y83z3ke

Scribd : https://tinyurl.com/3rdfe9e3

DOI : https://doi.org/10.5281/zenodo.14406652


Abstract : COVID-19 has significantly affected the healthcare management system and has posed healthcare workers with issues that need a response approach and accuracy. The objective of this research paper is to analyze how the use of Intelligent Automation Technologies, including Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) can be applied to enhance COVID-19 management. The objective is to achieve an integrated, self-optimizing system that augments data acquisition, analysis, and treatment with real-time process control. It will also include the recommended approaches to implementing technologies such as RPA to capture data from various sources for healthcare organizations. Other methodologies will also incorporate AI/ML for diagnosis, in which tools such as CT and X-ray are used, and the health system needs to recommend the proper care. The research indicates that automation reduces reliance on manual procedures, improves the rate of data processing and analysis, and sharpens diagnostic capabilities, thus leading to faster clinical decisions. This paper proves that such technologies can redefine approaches implemented to combat the pandemic and ensure that the healthcare system is sustainable and efficient. This integration is thought to be a significant improvement in the process of developing automated healthcare service systems and management intelligent systems.

References :

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  2. Ugajin, A. (2023). Automation in hospitals and health care. In Springer Handbook of Automation (pp. 1209-1233). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-030-96729-1_56
  3. Ni, Y., Lingren, T., Huth, H., Timmons, K., Melton, K., & Kirkendall, E. (2020). Integrating and evaluating the data quality and utility of smart pump information in detecting medication administration errors: evaluation study. JMIR Medical Informatics8(9), e19774. https://medinform.jmir.org/2020/9/e19774/
  4. Escobar, M., Jeanneret, G., Bravo-Sánchez, L., Castillo, A., Gómez, C., Valderrama, D., ... & Arbelaez, P. (2020). Smart pooling: AI-powered COVID-19 testing. medrxiv, 2020-07. https://www.medrxiv.org/content/10.1101/2020.07.13.20152983v2.full.pdf
  5. Kumaragurubaran, T., SR, V. R., & Vigneshwaran, R. (2024, March). Predictive Modelling of Critical Vital Signs in ICU Patients by Machine Learning: An Early Warning System for Improved Patient Outcomes. In 2024 3rd International Conference for Innovation in Technology (INOCON) (pp. 1-6). IEEE. https://ieeexplore.ieee.org/abstract/document/10512042

COVID-19 has significantly affected the healthcare management system and has posed healthcare workers with issues that need a response approach and accuracy. The objective of this research paper is to analyze how the use of Intelligent Automation Technologies, including Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) can be applied to enhance COVID-19 management. The objective is to achieve an integrated, self-optimizing system that augments data acquisition, analysis, and treatment with real-time process control. It will also include the recommended approaches to implementing technologies such as RPA to capture data from various sources for healthcare organizations. Other methodologies will also incorporate AI/ML for diagnosis, in which tools such as CT and X-ray are used, and the health system needs to recommend the proper care. The research indicates that automation reduces reliance on manual procedures, improves the rate of data processing and analysis, and sharpens diagnostic capabilities, thus leading to faster clinical decisions. This paper proves that such technologies can redefine approaches implemented to combat the pandemic and ensure that the healthcare system is sustainable and efficient. This integration is thought to be a significant improvement in the process of developing automated healthcare service systems and management intelligent systems.

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