Solving patient scheduling problems in hospitals using a dedicated GenAI system

ObjectivesPatient scheduling is a vital yet complex task that strongly influences patient satisfaction and optimizes healthcare efficiency. Recent studies have emphasized the importance of devising innovative approaches and developing new scheduling frameworks. Therefore, this study establishes a dedicated generative artificial intelligence (GenAI) system for patient scheduling.MethodsThe proposed system first imports scheduling data to formulate the default patient scheduling problem. Subsequently, users enter their scheduling requirements using natural language via the system interface, which are parsed using a deep neural network to establish the corresponding extended three-field notations. A customized genetic algorithm is automatically generated to solve the customized patient scheduling problem.ResultsThe dedicated GenAI system was applied to a real-world case obtained from the literature, involving 12 anesthesiologists, surgeons, and anesthesia resuscitation doctors; 12 operating rooms; and 15 patients undergoing three types of surgeries, each consisting of three operations. The experimental results reveal that the difference in the optimal fitness achieved using this system and branch-and-bound was less than 1% on average, demonstrating that the proposed methodology is effective. In addition, the most complex customized patient scheduling problem could be automatically modeled and solved in 20 s. Furthermore, the scheduling performance achieved using this system was significantly higher (α = 0.05) than those achieved using two current practices. Moreover, customized patient scheduling problems are often substantially more complex than problems addressed using traditional methods reported in previous studies.ConclusionsApplying this dedicated GenAI system improved the effectiveness of patient scheduling. This is expected to considerably enhance patient satisfaction and overall healthcare efficiency.