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

Sudhanshu Joshi, Manu Sharma, Rashmi Prava Das, Joanna Rosak-Szyrocka, Justyna Żywiołek, Kamalakanta Muduli () and Mukesh Prasad
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Sudhanshu Joshi: Operations and Supply Chain Management Research Lab, School of Management, Doon University, Kedarpur 248001, India
Manu Sharma: Department of Management Studies, Graphic Era Deemed to be University, Dehradun 248002, India
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

Sustainability, 2022, vol. 14, issue 18, 1-23

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.

Keywords: artificial intelligence; healthcare systems; developing countries (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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