The Implementation of Artificial Intelligence (AI) in Hospital Management and Its Impact on Operational Efficiency: A Systematic Review
Abstract
Digital transformation has encouraged hospitals to adopt intelligent technologies to improve operational efficiency and service quality. Artificial Intelligence (AI) represents a strategic innovation with the potential to support data-driven managerial decision-making, particularly in patient administrative flow and operational resource allocation. This study aims to analyze the application of AI in hospital management, evaluate its impact on operational efficiency, and identify key factors influencing successful implementation, especially within the Indonesian hospital context. This study employs a systematic literature review method guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Relevant studies were retrieved from reputable scientific databases using predefined inclusion and exclusion criteria. A total of 30 selected articles were analyzed using a thematic synthesis approach to identify patterns of AI implementation and its operational impacts in hospital settings. The findings indicate that AI applications in hospital management are predominantly focused on patient flow management, bed management, service scheduling, and human resource management. The implementation of AI has been shown to improve operational efficiency by reducing patient waiting times, optimizing resource utilization, enhancing healthcare workforce productivity, and controlling operational costs. However, the success of AI adoption is strongly influenced by technological infrastructure readiness, human resource capabilities, organizational support, and ethical as well as data security considerations. Based on these findings, this study proposes a conceptual model for AI-based operational efficiency in hospitals that is relevant to the Indonesian context by integrating the TechnologyOrganizationEnvironment (TOE) framework and the Resource-Based View (RBV) perspective.
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Copyright (c) 2026 Gian Paul Samuel Sumayku, Bernard Tirtomoeljono Widjaja, Eka Desy Purnama
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