In this study, we verified that getting a laboratory test or an imaging examination prior to seeing a doctor could significantly reduce patients’ waiting time. We also found that accepting the tests or examinations recommended by the AI-assisted system did not result in higher costs; on the contrary, the cost was lower than that of ordinary patients. This research suggests a way to enhance the outpatient procedure to a certain extent by reducing the links in the whole process. The number of outpatient services in public tertiary general hospitals has increased dramatically. Long waiting times can lead to patients with potentially urgent problems not receiving timely treatment [21]. They may also lead to canceled or no-show appointments [21, 22]. In other studies, the average waiting time at Chinese general tertiary general hospitals was 23 minutes [9]. The waiting time for outpatient service in pediatric hospitals was found to be generally longer at 42 minutes [23]. In our study, as the waiting time was defined as the time from registration to preparation for the examination or test, it was longer than that reported in other studies. With AI, the waiting time was reduced to 0.38 hours (i.e., < 5 minutes) from about two hours before.
The waiting time of outpatients has always been a matter of great concern in China and other developing countries. Substantial research has shown that evaluating and redesigning outpatient systems in the healthcare process would successfully reduce waiting times and improve satisfaction. Studies at tertiary general hospitals in China have reported similar findings. For example, Wang et al. reported that staff carried out a quality circle-themed activity, which reduced the time for patients to see a doctor [24]. Chen et al. suggested that waiting time could be substantially reduced through the introduction of an appointment system and flexible, demand-oriented doctor scheduling according to the number of patients waiting at different times of the workday [25]. However, for pediatric hospitals with a limited number of doctors, it would undoubtedly increase the daily work burden of doctors. In addition, pediatric hospitals and general hospitals are different in many ways. The immune functions of children are still developing, and a variety of diseases caused by climate factors has a significant impact on the number of pediatric visits. Therefore, it is questionable whether the advantages of redesigned outpatient systems are applicable to a large children’s hospital. Accordingly, we believe that an AI-based system would simplify the pediatric outpatient process and reduce the waiting time of patients without increasing (or even reducing) doctors’ workload in a children’s hospital. In the emergency department and the radiology department, there is a precedent for using AI to reduce outpatient time. Curtis [13] investigated the applicability of machine learning models to predict waiting times at a walk-in radiology facility (for radiography) and delay times for scheduled radiology services (CT, MRI, and ultrasound). Accurately predicting waiting times and delays in scheduled appointments may enable staff members to more accurately respond to patient flow. In Lin’s study [26], supervised machine learning models provided an accurate patient wait time prediction and were able to identify the factors with the largest contribution to patient wait times. It is important to note that patient satisfaction increases when patients are told about their expected wait time. Similar results have been reported in other studies [10, 27−31].
To our knowledge, ours is the first study to use AI for assisting in the outpatient process by predicting whether a lab test or an imaging examination is recommended prior to seeing a doctor. The innovation of our study lies in the embedding of the combination of AI-assisted diagnosis and prescription into the outpatient procedure. By extending this system, it is conceivable that the parents of the children could complete a series of steps, such as registration, pre-consultation, and prescription at home or on the way, to the hospital with the help of Xiao Yi. After registration, patients could immediately undergo the required examination or tests, which considerably improves the efficiency of medical care. Since the implementation of the Xiao Yi system in 2018, it has assisted in more than 270,000 visits, in total, and more than 60,000 children have experienced the new outpatient procedure. All of the datasets we used for training and validation were from patients with real medical experience, and they were more reflective of the real world than recruiting volunteers to participate in the experiment. As in the real world, a patient’s medical process is often subject to change. In addition, a patient’s waiting time is affected by a number of factors, and the most obvious one is the time of registration. Seasons, holidays, and periods of time may affect the flow of patients. Another advantage of this study is that PSM was used to pair the data of the control group and the case group in order to eliminate the influence of different registration times.
This study has the following limitations. First, the system was designed for the target patients, that is, the patients who needed an imaging examination or a lab test. The patients who did not undergo an imaging examination or a lab test were excluded. Second, the AI system and the hospital information system need to be connected by the unique outpatient number to make the data exchange. If the doctor forgets to enter the patient’s outpatient number during diagnosis, there would be no way to connect this part of the data. This resulted in missing data and the appearance of illogical values. With debugging and other interventions, this issue can be resolved.