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

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.

What is the impact of healthcare innovation on measurable outcomes of healthcare organisation performance? A systematic review

“…New technologies accounted for only a quarter of innovations described, which is surprising given the evolution and availability of new technologies in healthcare. Further, policy statements and professional standards consistently emphasise the importance of technology use to improve patient outcomes,53 and recent research findings54 highlight the potential of artificial intelligence (AI) for transforming the healthcare system in terms of securing medical and clinical data, trusted collaboration and holistic quality management. Joshi et al 54 acknowledge a range of barriers that may impact the implementation of AI in healthcare including low awareness, low investment, and low commitment from top-level management.…”

“…Further, policy statements and professional standards consistently emphasise the importance of technology use to improve patient outcomes,53 and recent research findings54 highlight the potential of artificial intelligence (AI) for transforming the healthcare system in terms of securing medical and clinical data, trusted collaboration and holistic quality management. Joshi et al 54 acknowledge a range of barriers that may impact the implementation of AI in healthcare including low awareness, low investment, and low commitment from top-level management. Future research should focus on addressing such barriers, given the intricate links between AI development and innovation performance 55…”

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

“…[102]- [106], [109], [111]- [113], [116], [134]-[147] RQ3 [27], [35], [40], [52], [53], [63], [74], [77], [81], [82], [89], [98], [101], [105], [110], [148]- [159] RQ4 [96], [107], [138], [144], [160]- [184] statistical analysis, potential biases, and the overall relevance and contribution of the research to the field.…”

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“…However, the application of AI in the healthcare supply chain field still has some challenges and limitations. First of all, the application scope of artificial intelligence in the field of healthcare supply chain is relatively narrow ( 37 ). Compared with other fields (such as retail industry and banking industry), the healthcare supply chain is subject to more strict legal constraints, and its data security and privacy protection are more important, which limits the application of artificial intelligence in the medical field to a certain extent.…”