Impact of an artificial intelligence-driven operational management system on operational efficiency in health care organization in Saudi Arabia
Introduction: In recent years, Artificial Intelligence (AI) is transforming healthcare systems globally and improved the operational efficiency in healthcare organizations. The authors examined how an artificial intelligence (AI)–driven operational management system (OMS) affected operational efficiency in health care units in the Kingdom of Saudi Arabia (KSA). They also investigated the mediating role of staff attitudes in the relationship between OMSs and operational efficiency. This research contributes to the field by applying the theory of planned behavior to examine health care professionals’ perceptions of AI-based OMSs and their impact on operational efficiency.
Methods: To achieve study objectives, a quantitative research design, with cross-sectional survey methodology, was used to gather data from 287 health care professionals across various hospitals in the KSA. The authors used a partial least squares structural equation modeling (PLS-SEM) approach to hypothesis testing.
Results: The findings indicated that an AI-based OMS significantly improved operational efficiency and positively affected staff attitudes. Furthermore, staff attitudes mediated the relationship between an AI-based OMS and operational efficiency.
Discussion: The study finding highlights the dual benefits of AI-based OMSs in enhancing both operational performance and employee satisfaction. The results suggest that health care organizations in the KSA should invest in AI technologies to optimize operational efficiency and improve staff attitudes. The findings also emphasize the need to address employee perceptions to fully capitalize on the benefits of AI implementations. They also introduce staff attitudes as a mediating factor, offering new insights into the interaction between technology and employee engagement.
1 Introduction
Artificial intelligence (AI) integrated with health care management has revolutionized operational processes worldwide. It presents an unsurpassed opportunity for efficiency and enhancement in the delivery of services (1). Operational efficiency is the ability of an organization for optimizing processes with the least cost, time, and errors while maintaining quality. The Kingdom of Saudi Arabia (KSA) health care sector is driven by and in line with the Vision 2030 initiative (2). It has embraced AI-based operational management systems (OMSs) to enhance resource utilization, streamline patient care, and lower operational costs (3, 4). AI-based OMS refers to utilizing machine learning and predictive analytics to enhance and automate organization operations. This technology promotes better decision-making by refining workflows and boosting efficiency through real-time data analysis while minimizing the need for manual input. The use of AI is expected to contribute to decision-making processes, scheduling optimization, and patient flow management by improving operational efficiencies in the health care sector. It has several benefits for every industry around the globe. For example, AI has the potential to handle enormous data volumes and to automate routine tasks (5). Furthermore, AI can suggest predictive insights and skills that are important in a highly demanding area such as health (6). Thus, AI is regarded as a significant step to modernize the health sector in the KSA. It is in line with global trends relating to the adoption of this digital transformation in health care settings.
Employee attitudes have been identified in the available literature as central to any successful technology implementation. Employee attitudes encompass the perceptions, willingness, and apprehensions that workers have regarding the adoption of AI-driven OMS. It includes perceived utility, user friendliness, and reliance upon AI, as well as concerns about job loss due to automation. These factors significantly influence the successful adoption of the AI system. For example, attitudes toward new technologies taken up by staff can profoundly affect their effectiveness in health care units (7). In addition, positive perceptions and adaptability influence the degree of staff engagement with the AI systems (8). Moreover, staff could be resistant or show reluctance to use AI-based tools, which would automatically lower the potential of AI (9). The KSA has culturally distinctive and structured health care environments (10). An understanding of AI and its influence on operational efficiency, taking into account staff attitudes, is key to the successful integration of digital transformation in the health care operations in the KSA.
The adoption of AI-based OMSs in health care represents a great opportunity to enhance the efficiency of the entire health care sector in the KSA. The health care sector is considered to be among those undergoing modernization at one of the fastest rates in the KSA. Despite the fact that all these new technologies promise the optimization of resource utilization, cost reduction, and better patient care, but their effective implementation is not guaranteed (11). Furthermore, there is a human factor in this case: the attitude of health care staff toward an AI system which significantly affect the success or failure of such initiatives (12).
The health care sector of the KSA has some distinct features that may create challenges for the implementation of AI. It contains a very rigidly organized and hierarchically set environment (13, 14). Therefore, a resistance to or negative perception on the part of health care staff toward AI may undo the expected benefits and result in inefficiencies rather than improvements. Understanding this dual influence of AI systems and staff attitudes is important in maximizing their operational potential. Therefore, in this study we addressed the pressing need to evaluate not only the direct impact that AI itself makes on operational efficiency but also how staff attitudes mediate this relationship—a subject of utmost importance for the effective introduction of AI into health care.
We investigated the impact of an AI-based OMS on operational efficiency in health units within the KSA. We also addressed the mediating role of staff attitudes in the relationship between the AI-based OMS and operational efficiency in health care units in the KSA. We sought to answer the following research questions:
• How do AI-based OMSs in health care units in the KSA affect their operational efficiency?
• Do AI-based OMSs also influence staff attitudes toward work in health care units in the KSA?
• How do staff attitudes affect operational efficiency in health care units in the KSA?
• Do staff attitudes mediate the influence of AI-based OMSs on operational efficiency in health care units in the KSA?
This research provides timely and critical insights into the KSA’s rapidly changing health care landscape, driven by the ambitious Vision 2030 initiative. In a world where AI-based OMSs have become indispensable tools to enhance efficiency, understanding their real-world impact becomes paramount. The real significance of this study lies in its exploration of one of the most crucial, yet often overlooked, factors, including staff attitudes. AI systems promise great improvements in resource optimization and operational efficiency. However, their actual success is inextricably linked to how health care professionals perceive and engage with these technologies. We investigated the mediating role of staff attitudes as a means to progress from a purely technological perspective toward a holistic human–technology interface view in health care. The results will add to the increasing debate on AI in health. They also will add to practical knowledge for decision-makers in government and health leadership to notice or ensure meaningful, sustainable changes in operational efficiency. This doubled focus points to a very relevant study, both for the local and global health care system.
The key contributions of this study are substantial and exist on many fronts. First, the study adds to the growing body of literature on AI in the health care sector of the KSA. It provides initial empirical evidence of how AI-based systems can help enhance operational management. Much of the literature has focused on Western health care systems; this study addresses a critical gap in the literature by investigating the use of AI within a culturally distinctive non-Western setting. This research provides fresh insights into the ongoing global debate on AI’s transformative potential in health care.
Second, this study provides new insight into the mediating effect of staff attitudes in the relationship between AI-based OMSs and operational efficiency. Whereas most of the literature focuses on the core technology itself, this study brings the critical factor of human intervention into the limelight. It looks at how staff perceptions and attitudes can influence the effectiveness of the deployment of AI. Therefore, it can delve more deeply and holistically into how technology uptake will either work or fail because of human engagement.
Finally, this study contributes practically by providing real means through an approach by which health care leaders and policymakers can act. It emphasizes the development of positive attitudes among staff and the implementation of AI systems. It also helps ensure that technological adoption brings efficiency improvements that are sustainable at both the local and global health care system levels.
