School of Mechanical Engineering, Yonsei University, Seoul 03722, Korea.
College of Nursing and Mo-Im Kim Nursing Research Institute, Yonsei University, Seoul 03722, Korea.
Int J Environ Res Public Health. 2022 Feb 12;19(4):2073. doi: 10.3390/ijerph19042073.
The time a patient spends waiting to be seen by a healthcare professional is an important determinant of patient satisfaction in outpatient care. Hence, it is crucial to identify parameters that affect the waiting time and optimize it accordingly. First, statistical analysis was used to validate the effective parameters. However, no parameters were found to have significant effects with respect to the entire outpatient department or to each department. Therefore, we studied the improvement of patient waiting times by analyzing and optimizing effective parameters for each physician. Queueing theory was used to calculate the probability that patients would wait for more than 30 min for a consultation session. Using this result, we built metamodels for each physician, formulated an effective method to optimize the problem, and found a solution to minimize waiting time using a non-dominated sorting genetic algorithm (NSGA-II). On average, we obtained a 30% decrease in the probability that patients would wait for a long period. This study shows the importance of customized improvement strategies for each physician.
患者等待医疗专业人员就诊的时间是门诊护理患者满意度的一个重要决定因素。因此，确定影响等待时间的参数并进行相应优化至关重要。首先，使用统计分析来验证有效参数。然而，没有发现任何参数对整个门诊部或每个部门的等待时间有显著影响。因此，我们通过分析每位医生的有效参数来研究患者等待时间的改善。排队论用于计算患者在咨询会话中等待超过 30 分钟的概率。使用此结果，我们为每位医生建立了元模型，制定了优化问题的有效方法，并使用非支配排序遗传算法 (NSGA-II) 找到了最小化等待时间的解决方案。平均而言，我们将患者长时间等待的概率降低了 30%。本研究表明，为每位医生制定定制改进策略的重要性。
