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Artificial Intelligence Model for Risk Management in Healthcare Institutions: Towards Sustainable Development

Author

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  • Abdelaziz Darwiesh

    (Department of Mathematics, Faculty of Science, Damietta University, New Damietta 34517, Egypt)

  • A. H. El-Baz

    (Department of Computer Science, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta 34517, Egypt)

  • Abedallah Zaid Abualkishik

    (Information Technology Management Department, College of Computer and Information Technology, American University in the Emirates, Dubai 00000, United Arab Emirates)

  • Mohamed Elhoseny

    (Information Systems Department, Faculty of Computing and Informatics, University of Sharjah, Sharjah 26666, United Arab Emirates)

Abstract

This paper proposes an artificial intelligence model to manage risks in healthcare institutions. This model uses a trendy data source, social media, and employs users’ interactions to identify and assess potential risks. It employs natural language processing techniques to analyze the tweets of users and produce vivid insights into the types of risk and their magnitude. In addition, some big data analysis techniques, such as classification, are utilized to reduce the dimensionality of the data and manage the data effectively. The produced insights will help healthcare managers to make the best decisions for their institutions and patients, which can lead to a more sustainable environment. In addition, we build a mathematical model for the proposed model, and some closed-form relations for risk analysis, identification and assessment are derived. Moreover, a case study on the CVS institute of healthcare in the USA, and our subsequent findings, indicate that a quartile of patients’ tweets refer to risks in CVS services, such as operational, financial and technological risks, and the magnitude of these risks vary between high risk (19%), medium risk (80.4%) and low risk (0.6%). Further, several performance measures and a complexity analysis are given to show the validity of the proposed model.

Suggested Citation

  • Abdelaziz Darwiesh & A. H. El-Baz & Abedallah Zaid Abualkishik & Mohamed Elhoseny, 2022. "Artificial Intelligence Model for Risk Management in Healthcare Institutions: Towards Sustainable Development," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:420-:d:1016186
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    References listed on IDEAS

    as
    1. Shin-Cheng Yeh & Ai-Wei Wu & Hui-Ching Yu & Homer C. Wu & Yi-Ping Kuo & Pei-Xuan Chen, 2021. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-34, August.
    2. Liverani, Marco & Waage, Jeff & Barnett, Tony & Pfeiffer, Dirk U. & Rushton, Jonathan & Rudge, James W. & Loevinsohn, Michael E. & Scoones, Ian & Smith, Richard D. & Cooper, Ben S. & White, Lisa J. & , 2013. "Understanding and managing zoonotic risk in the new livestock industries," LSE Research Online Documents on Economics 50665, London School of Economics and Political Science, LSE Library.
    3. Carlos Corvalan & Elena Villalobos Prats & Aderita Sena & Diarmid Campbell-Lendrum & Josh Karliner & Antonella Risso & Susan Wilburn & Scott Slotterback & Megha Rathi & Ruth Stringer & Peter Berry & S, 2020. "Towards Climate Resilient and Environmentally Sustainable Health Care Facilities," IJERPH, MDPI, vol. 17(23), pages 1-18, November.
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