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 :
- Moezzi, M., Shirbandi, K., Shahvandi, H. K., Arjmand, B., & Rahim, F. (2021). The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. Informatics in medicine unlocked, 24, 100591. https://www.sciencedirect.com/science/article/pii/S2352914821000812
- 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
- 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 Informatics, 8(9), e19774. https://medinform.jmir.org/2020/9/e19774/
- 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
- 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.