Implementing Artificial Intelligence in the United Arab Emirates Healthcare Sector

Shaikha FS Alhashmi; Said A. Salloum; Chaker Mhamdi

doi:10.21070/pels.v6i0.1984

(1) * Shaikha FS Alhashmi Faculty of Engineering & IT,The British University in Dubai, Dubai, UAE
India
(2) Said A. Salloum Faculty of Engineering & IT,The British University in Dubai, Dubai, UAE Research Institute of Sciences & Engineering, University of Sharjah, Sharjah, UAE
India
(3) Chaker Mhamdi University of Manouba, Tunisia Al Buraimi University College
Oman
(*) Corresponding Author

DOI: https://doi.org/10.21070/pels.v6i0.1984

Abstract

The United Arab Emirates (UAE) has recently focused on implementing Artificial Intelligence (AI) projects in the government healthcare sector to help manage chronic diseases and early detection. However, successful AI implementation depends on adoption and acceptance by decision-makers, physicians, nurses, and patients. This paper develops and tests a modified Technology Acceptance Model (TAM) to explore critical success factors (CSFs) for the adoption of AI in the healthcare sector. The most widely used CSF variables for TAM are Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitudes toward Use (ATU) and Behavioral Intention to Use (BIU). However, a review of 23 qualitative and quantitative studies of TAM literature from 2015 to 2018 suggested that five key external factors should be included in CSF studies using TAM. An extended model was developed (ETAM) and tested using a qualitative study comprising 53 employees working in the Dubai IT and healthcare sectors. The study showed that managerial, organizational, operational and IT infrastructure factors have a positive effect on PU and PEU and, hence, should be included as CSFs in determining the implementation of AI in the healthcare sector.

Downloads

Download data is not yet available.

References

Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for ELearning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. The impact of e-service quality
Al-dweeri, R. M., Obeidat, Z. M., Al-dwiry,M. A., Alshurideh, M. T., & Alhorani,
A. M. (2017). The impact of e-servicequality and e-loyalty on online shopping:moderating effect of e-satisfaction and e-trust. International Journal of Marketing Studies, 9(2), 92
Al-Emran, M. (2015b). Speeding Up the Learning in A Robot Simulator. International Journal of Computing and Network Technology, 3(3).
Al-Emran, M., Alkhoudary, Y. A., Mezhuyev, V., & Al-Emran, M. (2019). Students and Educators Attitudes towards the use of M-Learning: Gender and Smartphone ownership Differences. International Journal of Interactive Mobile Technologies (IJIM), 13(1), 127–135.
Al-Emran, M., Zaza, S., & Shaalan, K. (2015). Parsing modern standard Arabic using Treebank resources. In 2015 International Conference on Information and Communication Technology Research, ICTRC 2015. https://doi.org/10.1109/ ICTRC.2015.7156426 Al-Maroof, R. S.,Salloum, S.
Al Hamadand, A. Q. M., & Shaalan, K. (2019). A Unified Model for the Use and Acceptance of Stickers in Social Media Messaging. In International Conference on Advanced Intelligent Systems and Informatics (pp. 370–381). Springer Alhashmi, S. F. S., Salloum, S. A., & Abdallah,
S. (2019). Critical Success Factors for Implementing Artificial Intelligence (AI) Projects in Dubai Government United Arab Emirates (UAE) Health Sector: Applying the Extended Technology Acceptance Model (TAM). In International Conference on Advanced Intelligent Systems and Informatics (pp. 393–405). Springer.
Alkalha, Z., Al-Zu’bi, Z., Al-Dmour, H., Alshurideh, M., & Masa’deh, R. (2012). Investigating the effects of human resource policies on organizational performance: An empirical study on commercial banks operating in Jordan. European Journal of Economics, Finance and Administrative Sciences, 51(1), 44–64.
Alloghani, M., Hussain, A., Al-Jumeily, D., & Abuelma’atti, O. (2015). Technology Acceptance Model for the Use of M-Health Services among health related users in UAE. In 2015 International Conference on Developments of E-Systems Engineering (DeSE) (pp. 213–217). IEEE. Alomari, K.
M., AlHamad, A. Q., & Salloum, S. (n.d.). Prediction of the Digital Game Rating Systems based on the ESRB
Derbel, Emira. (2019b). Teaching Literature through Comics: An Innovative Pedagogical Tool. International Journal of Applied Linguistics and English Literature, 8(1), 54–61.
ELSamen, A., & Alshurideh, M. (2012). The impact of internal marketing on internal service quality: A case study in a Jordanian pharmaceutical company. International Journal of Business and Management, 7(19), 84. Emad, H.,
El-Bakry, H. M., & Asem, A. (2016). A Modified Technology Acceptance Model for Health Informatics