How to allocate the existing medical resources reasonably, alleviate hospital congestion and improve the patient experience are problems faced by all hospitals. At present, the combination of artificial intelligence and the medical field is mainly in the field of disease diagnosis, but lacks successful application in medical management. We distinguish each area of the emergency department by the division of medical links. In the spatial dimension, in this study, the waitlist number in real-time is got by processing videos using image recognition via a convolutional neural network. The congestion rate based on psychology and architecture is defined for measuring crowdedness. In the time dimension, diagnosis time and time-consuming after diagnosis are calculated from visit records. Factors related to congestion are analyzed. A total of 4717 visit records from the emergency department and 1130 videos from five areas are collected in the study. Of these, the waiting list of the pediatric waiting area is the largest, including 10,436 (person-time) people, and its average congestion rate is 2.75, which is the highest in all areas. The utilization rate of pharmacy is low, with an average of only 3.8 people using it at the one time. Its average congestion rate is only 0.16, and there is obvious space waste. It has been found that the length of diagnosis time and the length of time after diagnosis are related to age, the number of diagnoses and disease type. The most common disease type comes from respiratory problems, accounting for 54.3%. This emergency department has congestion and waste of medical resources. People can use artificial intelligence to investigate the congestion in hospitals effectively. Using artificial intelligence methods and traditional statistics methods can lead to better research on healthcare resource allocation issues in hospitals.

如何合理配置现有医疗资源，缓解医院拥堵，改善患者就医体验是所有医院面临的问题。目前，人工智能与医疗领域的结合主要集中在疾病诊断领域，但在医疗管理方面缺乏成功的应用。我们通过医疗环节的划分来区分急诊科的各个区域。在空间维度上，本研究通过卷积神经网络使用图像识别处理视频来实时获取候补名单数量。基于心理学和建筑学的拥堵率被定义用于测量拥挤度。在时间维度上，根据就诊记录计算诊断时间和诊断后耗时。分析了与拥塞相关的因素。研究共收集了急诊科的4717条就诊记录和来自5个地区的1130段视频。其中，儿科候诊区的候诊人数最多，达10436人次，平均拥堵率为2.75，为各区域中最高。药房利用率低，平均一次只有3.8人使用。其平均拥堵率仅为0.16，存在明显的空间浪费。研究发现，诊断时间的长短和诊断后的时间长短与年龄、诊断次数和疾病类型有关。最常见的疾病类型来自呼吸道疾病，占54.3%。这个急诊科存在拥挤和医疗资源浪费的情况。人们可以利用人工智能有效地调查医院的拥堵情况。 使用人工智能方法和传统统计方法可以更好地研究医院的医疗资源分配问题。