Effect of AI Application on Decision-Making Efficiency in Public Hospitals of Hawassa City, Ethiopia

The investigation analyzes the effects of AI applications on decision-making efficiency in public hospitals in Hawassa City, Ethiopia. Employing an explanatory design and quantitative analysis, the research collected data from 316 workers who were working in various departments of public hospitals through self-administered questionnaires. The data analyzed using multiple linear regression techniques to explore the relationship between AI applications and decision-making efficiency. The findings reveal that AI applications facilitate the dissemination of medical knowledge, clinical guidelines, and best practices among medical professionals, helping them enhance decision-making with greater accuracy, insight, and speed in treating patients. Additionally, the study shows that AI aids in automating and optimizing healthcare workflows, which reduces operational inefficiencies, minimizes human errors, and improves overall productivity. AI’s ability to monitor and manage healthcare operations in real-time allows for proactive interventions, anticipating potential issues before they escalate, and facilitating optimized resource allocation. The result also showed that the use of AI technologies in decision-making processes can improve healthcare service delivery, reduce wait times, enhance patient satisfaction, and support evidence-based practices. In conclusion, public hospitals may improve operational effectiveness and offer society better care by utilizing AI. In addition, incorporating AI into healthcare decision-making processes has demonstrated significant improvements in key performance metrics related to service delivery. These advancements include shorter patient wait times, enhanced efficiency in diagnostic and treatment procedures, higher levels of patient satisfaction, and a broader implementation of evidence-based medical practices within clinical environments. By leveraging AI, hospitals cultivate a data-centric culture in which decisions are grounded in analytical evidence rather than relying solely on intuition or prior experience.