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In the service industry, scheduling medical procedures causes difficulties for both patients and management. Factors such as fluctuations in customer demand and service time affect the appointment scheduling systems' performance in terms of, for example, patients' waiting time, idle time of resources, and total cost/profits. This research implements four appointment scheduling policies, i.e., constant arrival, mixed patient arrival, three-section pattern arrival, and irregular arrival, in an ultrasound department of a hospital in Taiwan. By simulating the four implemented policies' optimization procedures, optimal or near-optimal solutions can be obtained for patients per arrival, patients' inter-arrival time, and the number of the time slots for arrived patients. Furthermore, three objective functions are tested, and the results are discussed. The managerial implications and discussions are summarized to demonstrate how outcomes can be useful for hospital managers seeking to allocate their healthcare service capacities.

Scheduling approaches for conventional surgery operating rooms in a hospital treat surgeons as bottleneck resources directly, but do not deal with stochastic medical resources, leading to an uneven human resource distribution in optimizing medical resource scheduling. Thus, this research focuses on the dynamic configuration scheduling problem for stochastic medical resources. In this paper, the surgical operating room is limited, and the arriving calls (i.e., number of patients) are dynamic. When a patient arrives, the nurse anesthetist and anesthesiologist are limited, but the medical service duration per patient is random. We introduce the drum-buffer-rope (DBR) scheduling approach to analyze which types of medical resources become bottleneck resources for optimizing operating room scheduling. After verifying the effectiveness of the DBR method in uncertain situations, the Monte Carlo simulation is demonstrated.

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With the growth in the number of elderly and people with chronic diseases, the number of hospital services will need to increase in the near future. With myriad of information technologies utilized daily and crucial information-sharing tasks performed at hospitals, understanding the relationship between task performance and information system has become a critical topic. This research explored the resource pooling of hospital management and considered a computed tomography (CT) patient-referral mechanism between two hospitals using the information system theory framework of Task-Technology Fit (TTF) model. The TTF model could be used to assess the 'match' between the task and technology characteristics. The patient-referral process involved an integrated information framework consisting of a hospital information system (HIS), radiology information system (RIS), and picture archiving and communication system (PACS). A formal interview was conducted with the director of the case image center on the applicable characteristics of TTF model. Next, the Icam DEFinition (IDEF0) method was utilized to depict the As-Is and To-Be models for CT patient-referral medical operational processes. Further, the study used the 'leagility' concept to remove non-value-added activities and increase the agility of hospitals. The results indicated that hospital information systems could support the CT patient-referral mechanism, increase hospital performance, reduce patient wait time, and enhance the quality of care for patients.