Modularized Artificial Intelligence Services for Multiple Patients’ Medical Resource Sharing and Scheduling

This research addresses the growing challenges of patient scheduling with limited medical resources, such as consultation and examination services. Based on the concept of a pipeline, multiple intelligent heuristic algorithms are considered to optimize outpatient scheduling while reducing total time spent, including in consultation, the laboratory, or examination and idle time caused by scheduling conflicts. Multiple cross-department medical services with various patient conditions are considered for scheduling. To handle the complexity of limited examination resources, intelligent heuristic algorithms, like the genetic algorithm, simulated annealing, and ant colony optimization, are deployed. The modularized artificial intelligence service prioritizes minimizing not only patients’ average waiting time but also the total process time for all patients completing the medical service requests, while ensuring effective allocation of shared medical resources. According to the verification results, the genetic algorithm can adapt quickly to diverse hospital or patient requirements. Both average waiting time and total process time can be reduced and saved.