With the rapid evolution of data over the last few years, many new technologies have arisen with artificial intelligent (AI) technologies at the top. Artificial intelligence (AI), with its infinite power, holds the potential to transform patient healthcare. Given the gaps revealed by the 2020 COVID-19 pandemic in healthcare systems, this research investigates the effects of using an artificial intelligence-driven public healthcare framework to enhance the decision-making process using an extended model of Shaft and Vessey (2006) cognitive fit model in healthcare organizations in Saudi Arabia. The model was validated based on empirical data collected using an online questionnaire distributed to healthcare organizations in Saudi Arabia. The main sample participants were healthcare CEOs, senior managers/managers, doctors, nurses, and other relevant healthcare practitioners under the MoH involved in the decision-making process relating to COVID-19. The measurement model was validated using SEM analyses. Empirical results largely supported the conceptual model proposed as all research hypotheses are significantly approved. This study makes several theoretical contributions. For example, it expands the theoretical horizon of Shaft and Vessey's (2006) CFT by considering new mechanisms, such as the inclusion of G2G Knowledge-based Exchange in addition to the moderation effect of Experience-based decision-making (EDBM) for enhancing the decision-making process related to the COVID-19 pandemic. More discussion regarding research limitations and future research directions are provided as well at the end of this study.

随着过去几年数据的快速发展，许多新技术已经出现，其中人工智能 (AI) 技术处于领先地位。人工智能 (AI) 以其无限的力量，具有改变患者医疗保健的潜力。鉴于 2020 年 COVID-19 大流行在医疗保健系统中所揭示的差距，本研究使用 Shaft 和 Vessey (2006) 认知拟合的扩展模型调查了使用人工智能驱动的公共医疗保健框架来增强决策过程的效果沙特阿拉伯医疗机构的典范。该模型根据使用分发给沙特阿拉伯医疗保健组织的在线问卷收集的经验数据进行了验证。主要样本参与者是医疗保健首席执行官、高级经理/经理、医生、护士、卫生部下的其他相关医疗保健从业人员参与了与 COVID-19 相关的决策过程。使用 SEM 分析验证了测量模型。实证结果在很大程度上支持了所提出的概念模型，因为所有研究假设都得到了显着认可。本研究做出了若干理论贡献。例如，它通过考虑新机制扩展了 Shaft 和 Vessey (2006) CFT 的理论视野，例如除了基于经验的决策 (EDBM) 的调节作用之外，还包含 G2G 基于知识的交换以增强与 COVID-19 大流行相关的决策过程。本研究结束时还提供了有关研究局限性和未来研究方向的更多讨论。