Factors Influencing Medical Internet of Things Adoption by Riyadh Hospital Staff in Saudi Arabia: A Quasi-Experimental and Model Testing Study

Abstract

Medical IoT (mIoT) has tremendous capabilities in healthcare, ranging from monitoring patients’ basic health statistics remotely to more complex healthcare solutions, such as timely prediction of fatal life events and prevention of lethal communicable diseases. Saudi Arabia faces significant challenges in smoothly transitioning to mIoT. Previous experiences with eHealth solutions have encountered obstacles, indicating the potential hurdles ahead. For instance, the underutilisation of EHR functionalities has been reported across the board due to a lack of interest associated with low computer literacy. Other factors resistant to eHealth technology adoption are insufficient user knowledge, time constraints, and a lack of appreciation for the importance and functionality of technologies. To that end, this thesis aims to investigate the mIoT knowledge, perceptions and determinants that influence mIoT adoption, in conjunction with the role of evidence-based awareness videos and personal demographics in hospitals in Riyadh, Saudi Arabia. A study framework is proposed utilising UTAUT as the base model. The proposed framework incorporates these six study factors: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Computer and English language Self-Efficacy (CESE), Perceived Threat to Autonomy (PTA) and Confidentiality and Privacy Concerns (CPC). A quasi-experimental and model testing design is incorporated into our study. This study finds that the determinants (PE, EE, CESE, SI, PTA, CPC) proposed in the study framework hold significant predictive power in explaining the adoption behaviour of hospital care staff towards mIoT. Notably, the findings related to PTA yield valuable insights characterised by novelty, complexity, variability, and relevance in understanding mIoT adoption. The thesis concludes that the determinants should be incorporated during the initial stage of mIoT adoption in the healthcare sector. Future large-scale studies, including the involvement of sufficient numbers of doctors, are required to increase confidence and expand the relevance of the framework developed in this thesis.