IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i18p11698-d917986.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/18/11698/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/18/11698/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    2. Amrutanshu Panigrahi & Ajit Kumar Nayak & Rourab Paul, 2022. "HealthCare EHR: A Blockchain-Based Decentralized Application," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 15(3), pages 1-15, July.
    3. Kumar, Shashank & Raut, Rakesh D. & Queiroz, Maciel M. & Narkhede, Balkrishna E., 2021. "Mapping the barriers of AI implementations in the public distribution system: The Indian experience," Technology in Society, Elsevier, vol. 67(C).
    4. Walton, Nigel & Nayak, Bhabani Shankar, 2021. "Rethinking of Marxist perspectives on big data, artificial intelligence (AI) and capitalist economic development," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    5. Manu Sharma & Sunil Luthra & Sudhanshu Joshi & Himanshu Joshi, 2022. "Challenges to agile project management during COVID-19 pandemic: an emerging economy perspective," Operations Management Research, Springer, vol. 15(1), pages 461-474, June.
    6. Wang, Yichuan & Hajli, Nick, 2017. "Exploring the path to big data analytics success in healthcare," Journal of Business Research, Elsevier, vol. 70(C), pages 287-299.
    7. Jitendra Narayan Biswal & Kamalakanta Muduli & Suchismita Satapathy, 2017. "Critical analysis of drivers and barriers of sustainable supply chain management in Indian thermal sector," International Journal of Procurement Management, Inderscience Enterprises Ltd, vol. 10(4), pages 411-430.
    8. Manu Sharma & Sudhanshu Joshi, 2020. "Analytics in healthcare: a practical introduction," Asia Pacific Business Review, Taylor & Francis Journals, vol. 26(4), pages 520-522, August.
    9. Xing, Fei & Peng, Guochao & Zhang, Bingqian & Li, Shuyang & Liang, Xinting, 2021. "Socio-technical barriers affecting large-scale deployment of AI-enabled wearable medical devices among the ageing population in China," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adilson Carlos Yoshikuni & José Eduardo Ricciardi Favaretto & Alberto Luiz Albertin & Fernando de Souza Meirelles, 2022. "How can Strategy-as-Practice Enable Innovation under the Influence of Environmental Dynamism?," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 26(1), pages 200131-2001.
    2. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    3. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    4. Alaassar, Ahmad & Mention, Anne-Laure & Aas, Tor Helge, 2021. "Exploring a new incubation model for FinTechs: Regulatory sandboxes," Technovation, Elsevier, vol. 103(C).
    5. Cheng-Feng Cheng & Chien-Che Huang & Ming-Chang Lin & Ta-Cheng Chen, 2023. "Exploring Effectiveness of Relationship Marketing on Artificial Intelligence Adopting Intention," SAGE Open, , vol. 13(4), pages 21582440231, December.
    6. Fazal Ur Rehman & Basheer M. Al-Ghazali & Adel Ghaleb Haddad & Ehab Abdullatif Qahwash & M. Sadiq Sohail, 2023. "Exploring the Reverse Relationship between Circular Economy Innovation and Digital Sustainability—The Dual Mediation of Government Incentives," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    7. Ayesha Amjad & Piotr Kordel & Gabriela Fernandes, 2023. "A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence," Sustainability, MDPI, vol. 15(8), pages 1-24, April.
    8. Christopher Kurzhals & Lorenz Graf‐Vlachy & Andreas König, 2020. "Strategic leadership and technological innovation: A comprehensive review and research agenda," Corporate Governance: An International Review, Wiley Blackwell, vol. 28(6), pages 437-464, November.
    9. Gupta, Shivam & Modgil, Sachin & Choi, Tsan-Ming & Kumar, Ajay & Antony, Jiju, 2023. "Influences of artificial intelligence and blockchain technology on financial resilience of supply chains," International Journal of Production Economics, Elsevier, vol. 261(C).
    10. Youmi Suk & Kyung T. Han, 2024. "A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 151-172, April.
    11. Ksenia V. Ekimova, 2023. "Development of the potential of the digital economy of Russian regions through artificial intelligence humanisation," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    12. Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
    13. Zhang, Yaqiong & Wang, Shifu, 2023. "The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    14. Bingqian Zhang & Guochao Peng & Caihua Liu & Zuopeng Justin Zhang & Sajjad M. Jasimuddin, 2022. "Adaptation behaviour in using one-stop smart governance apps: an exploratory study between digital immigrants and digital natives," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1971-1991, December.
    15. Hani Bani-Salameh & Mona Al-Qawaqneh & Salah Taamneh, 2021. "Investigating the Adoption of Big Data Management in Healthcare in Jordan," Data, MDPI, vol. 6(2), pages 1-16, February.
    16. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    17. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
    18. Plantec, Quentin & Deval, Marie-Alix & Hooge, Sophie & Weil, Benoit, 2023. "Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry," Technovation, Elsevier, vol. 124(C).
    19. Asamoah, Daniel Adomako & Sharda, Ramesh, 2021. "What should I believe? Exploring information validity on social network platforms," Journal of Business Research, Elsevier, vol. 122(C), pages 567-581.
    20. Piotr Tomasz Makowski & Yuya Kajikawa, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Papers 2103.02395, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11698-:d:917986. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.