Harada Yukinori, Shimizu Taro
Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Japan.
Department of General Internal Medicine, Nagano Chuo Hospital, Nagano, Japan.
JMIR Med Inform. 2020 Aug 31;8(8):e21056. doi: 10.2196/21056.
Patient waiting time at outpatient departments is directly related to patient satisfaction and quality of care, particularly in patients visiting the general internal medicine outpatient departments for the first time. Moreover, reducing wait time from arrival in the clinic to the initiation of an examination is key to reducing patients' anxiety. The use of automated medical history-taking systems in general internal medicine outpatient departments is a promising strategy to reduce waiting times. Recently, Ubie Inc in Japan developed AI Monshin, an artificial intelligence-based, automated medical history-taking system for general internal medicine outpatient departments.
We hypothesized that replacing the use of handwritten self-administered questionnaires with the use of AI Monshin would reduce waiting times in general internal medicine outpatient departments. Therefore, we conducted this study to examine whether the use of AI Monshin reduced patient waiting times.
We retrospectively analyzed the waiting times of patients visiting the general internal medicine outpatient department at a Japanese community hospital without an appointment from April 2017 to April 2020. AI Monshin was implemented in April 2019. We compared the median waiting time before and after implementation by conducting an interrupted time-series analysis of the median waiting time per month. We also conducted supplementary analyses to explain the main results.
We analyzed 21,615 visits. The median waiting time after AI Monshin implementation (74.4 minutes, IQR 57.1) was not significantly different from that before AI Monshin implementation (74.3 minutes, IQR 63.7) (P=.12). In the interrupted time-series analysis, the underlying linear time trend (-0.4 minutes per month; P=.06; 95% CI -0.9 to 0.02), level change (40.6 minutes; P=.09; 95% CI -5.8 to 87.0), and slope change (-1.1 minutes per month; P=.16; 95% CI -2.7 to 0.4) were not statistically significant. In a supplemental analysis of data from 9054 of 21,615 visits (41.9%), the median examination time after AI Monshin implementation (6.0 minutes, IQR 5.2) was slightly but significantly longer than that before AI Monshin implementation (5.7 minutes, IQR 5.0) (P=.003).
The implementation of an artificial intelligence-based, automated medical history-taking system did not reduce waiting time for patients visiting the general internal medicine outpatient department without an appointment, and there was a slight increase in the examination time after implementation; however, the system may have enhanced the quality of care by supporting the optimization of staff assignments.
门诊患者的等待时间与患者满意度和医疗质量直接相关，尤其是对于首次前往普通内科门诊就诊的患者。此外，缩短从到达诊所到开始检查的等待时间是减轻患者焦虑的关键。在普通内科门诊使用自动病史采集系统是减少等待时间的一种有前景的策略。最近，日本的Ubie公司开发了AI Monshin，这是一种基于人工智能的普通内科门诊自动病史采集系统。
我们假设用AI Monshin取代手写的自我管理问卷的使用将减少普通内科门诊的等待时间。因此，我们进行了这项研究，以检验使用AI Monshin是否能减少患者的等待时间。
我们回顾性分析了2017年4月至2020年4月期间在日本一家社区医院未预约就诊的普通内科门诊患者的等待时间。AI Monshin于2019年4月实施。我们通过对每月的中位等待时间进行间断时间序列分析，比较了实施前后的中位等待时间。我们还进行了补充分析以解释主要结果。
我们分析了21615次就诊。实施AI Monshin后的中位等待时间（74.4分钟，四分位间距57.1）与实施AI Monshin前的中位等待时间（74.3分钟，四分位间距63.7）无显著差异（P = 0.12）。在间断时间序列分析中，潜在的线性时间趋势（每月-0.4分钟；P = 0.06；95%可信区间-0.9至0.02）、水平变化（40.6分钟；P = 0.09；95%可信区间-5.8至87.0）和斜率变化（每月-1.1分钟；P = 0.16；95%可信区间-2.7至0.4）均无统计学意义。在对21615次就诊中的9054次（41.9%）数据的补充分析中，实施AI Monshin后的中位检查时间（6.0分钟，四分位间距5.2）略长于实施AI Monshin前（5.7分钟，四分位间距5.0），但差异有统计学意义（P = 0.003）。
实施基于人工智能的自动病史采集系统并未减少未预约就诊的普通内科门诊患者的等待时间，且实施后检查时间略有增加；然而，该系统可能通过支持优化人员分配提高了医疗质量。
