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Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries

Author

Listed:
  • Sudhanshu Joshi

    (Operations and Supply Chain Management Research Lab, School of Management, Doon University, Kedarpur 248001, India
    Australian Artificial Intelligence Institute (AAII), Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia)

  • Manu Sharma

    (Department of Management Studies, Graphic Era Deemed to be University, Dehradun 248002, India
    Guildhall School of Business and Law, London Metropolitan University, London N7 8DB, UK)

  • Rashmi Prava Das

    (Bhubaneswar Engineering College, CV Raman Global University, Bhubaneswar 752054, India)

  • Joanna Rosak-Szyrocka

    (Department of Production Engineering and Safety, Faculty of Management, Częstochowa University of Technology, 42-200 Częstochowa, Poland)

  • Justyna Żywiołek

    (Department of Production Engineering and Safety, Faculty of Management, Częstochowa University of Technology, 42-200 Częstochowa, Poland)

  • Kamalakanta Muduli

    (Department of Mechanical Engineering, Papua New Guinea University of Technology, Lae 411, Papua New Guinea)

  • Mukesh Prasad

    (Australian Artificial Intelligence Institute (AAII), Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia)

Abstract

This study work is among the few attempts to understand the significance of AI and its implementation barriers in the healthcare systems in developing countries. Moreover, it examines the breadth of applications of AI in healthcare and medicine. AI is a promising solution for the healthcare industry, but due to a lack of research, the understanding and potential of this technology is unexplored. This study aims to determine the crucial AI implementation barriers in public healthcare from the viewpoint of the society, the economy, and the infrastructure. The study used MCDM techniques to structure the multiple-level analysis of the AI implementation. The research outcomes contribute to the understanding of the various implementation barriers and provide insights for the decision makers for their future actions. The results show that there are a few critical implementation barriers at the tactical, operational, and strategic levels. The findings contribute to the understanding of the various implementation issues related to the governance, scalability, and privacy of AI and provide insights for decision makers for their future actions. These AI implementation barriers are encountered due to the wider range of system-oriented, legal, technical, and operational implementations and the scale of the usage of AI for public healthcare.

Suggested Citation

  • Sudhanshu Joshi & Manu Sharma & Rashmi Prava Das & Joanna Rosak-Szyrocka & Justyna Żywiołek & Kamalakanta Muduli & Mukesh Prasad, 2022. "Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11698-:d:917986
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    References listed on IDEAS

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    Cited by:

    1. Sudhanshu Joshi & Manu Sharma, 2022. "Sustainable Performance through Digital Supply Chains in Industry 4.0 Era: Amidst the Pandemic Experience," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    2. Li, Chao & Zhang, Yuhan & Li, Xiang & Hao, Yanwei, 2024. "Artificial intelligence, household financial fragility and energy resources consumption: Impacts of digital disruption from a demand-based perspective," Resources Policy, Elsevier, vol. 88(C).
    3. Xinshang You & Shuo Zhao & Yanbo Yang & Dongli Zhang, 2022. "Influence of the Government Department on the Production Capacity Reserve of Emergency Enterprises Based on Multi-Scenario Evolutionary Game," Sustainability, MDPI, vol. 14(23), pages 1-35, November.
    4. Anand Singh Rajawat & S. B. Goyal & Pradeep Bedi & Tony Jan & Md Whaiduzzaman & Mukesh Prasad, 2023. "Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)," Future Internet, MDPI, vol. 15(8), pages 1-21, August.

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