The Role of Artificial Intelligence in Alleviating Hospital Queuing Problems and Its Specific Manifestations
Keywords:
Artificial intelligence, Hospital queuing, Patient flow management, Service operations, Capacity schedulingAbstract
With the continuous growth in healthcare service demand, hospital queuing problems have become increasingly prominent and have emerged as a critical factor affecting both the operational efficiency of healthcare systems and patients’ care experiences. Traditional approaches that primarily rely on increasing human resources often fail to fundamentally alleviate queuing phenomena in complex healthcare systems. From the perspectives of service operations and patient flow management, this study explores the role of artificial intelligence (AI) in mitigating hospital queuing problems and examines its specific manifestations. Using a scenario analysis approach, this study takes the outpatient process of a general hospital as a representative research context and analyzes AI interventions from three dimensions: bottleneck identification, decision front-loading, and dynamic capacity scheduling. The findings indicate that by integrating multi-source healthcare data and conducting predictive analytics, AI can systematically alleviate queuing problems and reduce patient waiting times without relying solely on workforce expansion. The results provide managerial insights into the application of AI in hospital queuing management.
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