A Framework to Enhance the Adoption of Artificial Intelligence (AI) Decision Making among Workers of the Healthcare Sector

Abstract

AI decision-making is transforming the healthcare industry by increasing efficiency and improving patient outcomes. Despite concerns about job displacement and data privacy, the use of AI is critical for improving healthcare systems and developing leadership in medical innovation. Thus, the goal of this study was to create a framework that identifies elements that improve AI decision-making and its impact on organisational performance, with organisational learning acting as a mediator, incorporating both direct and indirect relationships between these aspects. This paradigm analyses how AI adoption elements might improve decision-making processes and, as a result, boost overall performance, assisted by the organization’s constant learning and adaptability. To validate the framework, a questionnaire survey was administered to 1,033 employees at three Abu Dhabi hospitals which are the Tawam Hospital, Shaikh Khalifa Medical City (SKMC), and Ambulatory Healthcare Services (AHS). The validation was carried by utilising SmartPLS software. All five direct and indirect relationships were found to be statistically significant, demonstrating that organisational learning had partial mediation effects. This suggests that all of the identified characteristics have a major impact on AI decision-making tool adoption, with organisational learning serving as a key mediator. The framework highlights the relationships between the factors to boost AI adoption in the UAE healthcare business, solving concerns such as long wait times and administrative challenges through better technological integration.