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

Joshi, S; Sharma, M; Das, RP; Rosak-Szyrocka, J; Żywiołek, J; Muduli, K; Prasad, M

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

Joshi, S

Sharma, M Das, RP Rosak-Szyrocka, J Żywiołek, J Muduli, K Prasad, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sustainability (Switzerland), 2022, 14, (18)
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Joshi, S https://orcid.org/0000-0003-4748-5001
dc.contributor.author	Sharma, M
dc.contributor.author	Das, RP
dc.contributor.author	Rosak-Szyrocka, J
dc.contributor.author	Żywiołek, J
dc.contributor.author	Muduli, K
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2023-03-24T09:51:53Z
dc.date.available	2023-03-24T09:51:53Z
dc.date.issued	2022-09-01
dc.identifier.citation	Sustainability (Switzerland), 2022, 14, (18)
dc.identifier.issn	2071-1050
dc.identifier.issn	2071-1050
dc.identifier.uri	http://hdl.handle.net/10453/168359
dc.description.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.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Sustainability (Switzerland)
dc.relation.isbasedon	10.3390/su141811698
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	12 Built Environment and Design
dc.title	Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	12 Built Environment and Design
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-24T09:51:52Z
pubs.issue	18
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	18

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.

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http://hdl.handle.net/10453/168359