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Impact of a Commercial Artificial Intelligence–Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study

机译:商业人工智能驱动患者自我评估解决方案对一般内科门诊部门等候时间的影响:回顾性研究

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Background 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. Objective 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. Methods 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. 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). Conclusions 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.
机译:背景技术在门诊部门的患者等待时间与患者满意度和护理质量直接相关,特别是在第一次访问通用内科门诊部门的患者中。此外,从诊所到达诊所的等待时间转到检查的启动是减少患者焦虑的关键。在通用内部医学门诊部门的使用自动化医学历史系统是一个有希望的减少等待时间的策略。近日,日本的Ubie Inc开发了AI Monshin,是一种基于人工智能的自动化历史,用于一般内科门诊部门。目标我们假设用AI Monshin的使用取代手写自我管理问卷的使用将减少一般内科门诊部的等待时间。因此,我们进行了这项研究,以检查AI Monshin的使用是否减少了患者等候时间。方法回顾性分析了在2017年4月至4月20日期间的日本社区医院访问一般内科门诊部门的患者的等待时间。艾·蒙太宾于2019年4月实施。我们比较了在实施前后的中位等待时间进行每月等候时间的中间序列分析。我们还进行了补充分析来解释主要结果。结果我们分析了21,615次访问。 AI Monshin实施后的中位数等待时间(74.4分钟,IQR 57.1)与AI Monshin实施之前没有显着差异(74.3分钟,IQR 63.7)(P = .12)。在中断的时间序列分析中,下面的线性时间趋势(每月 - 0.4分钟; P = .06; 95%CI -0.9至0.02),水平变化(40.6分钟; P = .09; 95%CI -5.8到87.0)和坡度变化(每月-1.1分钟; P = .16; 95%CI -2.7至0.4)没有统计学意义。在评估分析中,从9054次访问(41.9%),AI Monshin实施后的中位考试时间(6.0分钟,IQR 5.2)略微但明显长于AI Monshin实施前的时间(5.7分钟,IQR 5.0)( p = .003)。结论实施人工智能的自动化历史制度的实施并没有减少患者访问一般内科门诊部门的等待时间,但实施后的考试时间略有增加;然而,系统可能通过支持员工分配的优化来提高护理质量。

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