Artificial Intelligence and Management Control in Hospitals: A Critical Review and Conceptual Integration

This article provides a critical review of the literature on the integration of artificial intelligence (AI) into hospital management control systems (MCS). The rapid development of AI in healthcare has created significant opportunities for enhancing forecasting, optimizing resource allocation, and improving costing accuracy. Despite these advances, the adoption of AI within financial and strategic control frameworks remains limited. The review synthesizes empirical findings and conceptual contributions published between 2020 and 2025. It examines applications such as predictive analytics for patient flows, digital twin simulations for resource optimization, and Time-Driven Activity-Based Costing (TDABC) for more precise financial monitoring. These findings are analyzed in relation to established management control frameworks, including the Balanced Scorecard (BSC) and Performance Management Systems (PMS). The literature reveals a strong convergence on AI’s technical potential, particularly in improving predictive accuracy and operational efficiency. However, divergences remain regarding the extent to which these tools are integrated into governance and financial control systems. Methodological limitations, including reliance on single-site studies, absence of causal designs, and fragmented approaches to prediction and costing, restrict the strength of current evidence. This article proposes a conceptual model that links AI capabilities with MCS and highlights governance as a key moderator of outcomes. It emphasizes that AI does not replace management control but augments it, moving from retrospective reporting toward proactive and simulation-based governance. By identifying theoretical gaps and outlining a future research agenda, the study contributes to a better understanding of how AI can support financial sustainability and strategic decision-making in hospitals.