Determinants predicting the electronic medical record adoption in healthcare: A SEM-Artificial Neural Network approach

Almarzouqi, A, Aburayya, A and Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981 2022, 'Determinants predicting the electronic medical record adoption in healthcare: A SEM-Artificial Neural Network approach' , PLoS ONE, 17 (8) , e0272735.

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Abstract

An Electronic Medical Record (EMR) has the capability of promoting knowledge and awareness regarding healthcare in both healthcare providers and patients to enhance interconnectivity within various government bodies, and quality healthcare services. This study aims at investigating aspects that predict and explain an EMR system adoption in the healthcare system in the UAE through an integrated approach of the Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Model (TAM) using various external factors. The collection of data was through a cross-section design and survey questionnaires as the tool for data collection among 259 participants from 15 healthcare facilities in Dubai. The study further utilised the Artificial Neural Networks (ANN) algorithm and the Partial Least Squares Structural Equation Modeling (PLS-SEM) in the analysis of the data collected. The study's data proved that the intention of using an EMR system was the most influential and predictor of the actual use of the system. It was also found that TAM construct was directly influenced by anxiety, innovativeness, self-efficacy, and trust. The behavioural intention of an individual regarding EMR was also proved to positively influence the use of an EMR system. This study proves to be useful practically by providing healthcare decision-makers with a guide on factors to consider and what to avoid when implementing strategies and policies.

Item Type: Article
Schools: Schools > School of Environment and Life Sciences
Journal or Publication Title: PLoS ONE
Publisher: Public Library of Science
ISSN: 1932-6203
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 14 Sep 2022 11:15
Last Modified: 14 Sep 2022 11:30
URI: https://usir.salford.ac.uk/id/eprint/64683

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