Integrating Artificial Intelligence in Healthcare: A Step Towards Enhancing Service Quality

Abstract

This paper defined Artificial Intelligence (AI) as the future of healthcare since it increases diagnostic correctness, efficacy, and patient satisfaction. While ENM might be known internationally, the literature reviewing its application in the context of the Saudi Arabian healthcare and Vision 2030 remains limited. Therefore, this study seeks to fill the above gap by assessing the effect; of AI integration on diagnostic credibility, perceived patient satisfaction, organizational performance, financial conservatives and confidence in AI systems in ten healthcare organizations in Saudi Arabia. Both quantitative patient and operation data and qualitative patient feedback and staff perceptions were analyzed with a view to achieving balance. To investigate the relationship between diagnostic accuracy, satisfaction scores and efficiency, correlation and regression analyses were performed. The various hypotheses were analyzed in a bid to confirm the effects of AI on specific healthcare fundamental measures. The research findings showed increase in performance in various facets post implementation of the AI system. Diagnostic accuracy increased to an average of 96.6%, with satisfaction scores improving by 18.9% (mean: 8.8/10). The following operational efficiency measures also increased: patient waiting time: -21.9% Diagnostic turnaround time improved by 31.8% both p < 0.001. It was found that an average of $280,000 per institution could be saved and this carried with it a decreased percentage of diagnostic errors of 27%. The correlations of diagnostic accuracy with patient satisfaction were found to be positive and significant ( r = +0.87) between cost savings with error reduction ( r = +0.85 ). The study stresses that AI improves the healthcare industry performance and fully complies with Saudi Arabian Vision 2030. Nevertheless, questions arising from the implementation of the three pillars regarding preparedness of the workforce, adapting to cultural difference, and data governance are still pertinent. This research can serve as the groundwork for policy makers and managers of healthcare centers to use AI properly, and call for more studies concerning how the AI can be used to deal with chronic diseases and improve healthcare accessibility for employees.