The provision of efficient healthcare services is essential, driven by the increasing demand for healthcare resources and the need to optimize hospital operations. In this context, the motivation to innovate and improve services while addressing urgent concerns is critical. Hospitals face challenges in managing internal dispatch services efficiently. Outsourcing such services can alleviate the burden on hospital staff, reduce costs, and introduce professional expertise. However, the pressing motivation lies in enhancing service quality, minimizing costs, and exploring innovative approaches. With the rising demand for healthcare services, there is an immediate need to streamline hospital operations. Delays in internal transportation services can have far-reaching implications for patient care, necessitating a prompt and effective solution. Drawing upon dispatch data from a healthcare center in Taiwan, this study constructed a decision-making model to optimize the allocation of hospital service resources. Employing simulation techniques, we closely examine how hospital services are currently organized and how they work. In our research, we utilized dispatch data gathered from a healthcare center in Taichung, Taiwan, spanning from January 2020 to December 2020. Our findings underscore the potential of an intelligent dispatch strategy combined with deployment restricted to the nearest available workers. Our study demonstrates that for cases requiring urgent attention, delay rates that previously ranged from 5% to 34% can be notably reduced to a much-improved 3% to 18%. However, it is important to recognize that the realm of worker dispatch remains subject to a multifaceted array of influencing factors. It becomes evident that a comprehensive dispatching mechanism must be established as part of a broader drive to enhance the efficiency of hospital service operations.

Study Objective: Overcrowding in emergency departments (ED) is an increasingly common problem in Taiwanese hospitals, and strategies to improve efficiency are in demand. We propose a bed resource allocation strategy to overcome the overcrowding problem. Method: We investigated ED occupancy using discrete-event simulation and evaluated the effects of suppressing day-to-day variations in ED occupancy by adjusting the number of empty beds per day. Administrative data recorded at the ED of Taichung Veterans General Hospital (TCVGH) in Taiwan with 1500 beds and an annual ED volume of 66,000 visits were analyzed. Key indices of ED quality in the analysis were the length of stay and the time in waiting for outward transfers to in-patient beds. The model is able to analyze and compare several scenarios for finding a feasible allocation strategy. Results: We compared several scenarios, and the results showed that by reducing the allocated beds for the ED by 20% on weekdays, the variance of daily ED occupancy was reduced by 36.25% (i.e., the percentage of reduction in standard deviation). Conclusions: This new allocation strategy was able to both reduce the average ED occupancy and maintain the ED quality indices.

Emergency department crowding has been one of the main issues in the health system in Taiwan. Previous studies have usually targeted the process improvement of patient treatment flow due to the difficulty of collecting Emergency Department (ED) staff data. In this study, we have proposed a hybrid model with Discrete Event Simulation, radio frequency identification applications, and activity-relationship diagrams to simulate the nurse movement flows and identify the relationship between different treatment sections. We used the results to formulate four facility layouts. Through comparing four scenarios, the simulation results indicated that 2.2 km of traveling distance or 140 min of traveling time reduction per nurse could be achieved from the best scenario.

In the post-pandemic era, emergency department congestion has become a global issue. When congestion occurs in the emergency department, it directly impacts mortality and morbidity rates among emergency patients. Therefore, the timely provision of critical resources by healthcare institutions has become a primary goal. Faced with the increasing number of emergency patients each year, easing the growing frequency of emergency department congestion is a challenge. One prevalent solution involves admitting emergency patients to inpatient care, especially as hospitalization is a common strategy. In Taiwan's medical centers, there are dedicated units managing inpatient beds. Despite variations in the importance hospitals place on inpatient bed management, it remains a significant revenue source. Therefore, effective inpatient bed management can serve as an avenue for implementing strategies to improve emergency department congestion. Single emergency department indicators are insufficient to meet the diverse management policies and complex requirements of different hospitals. Analyzing emergency department congestion requires identifying appropriate key performance indicators crucial for evaluating a hospital's emergency performance. This study collects data from Taiwan's medical centers and utilizes surveys to assess the situation of emergency department congestion in Taiwan. Additionally, it develops a simulation for optimizing the allocation of inpatient bed resources for emergency patients. The optimization considers different hospitals' inpatient department resource allocation speeds, selecting the optimal bed numbers for specific time periods. This approach provides hospital bed managers with the means to optimize bed planning based on the hospital's needs, thus contributing to the improvement of emergency department congestion.