Impact of a Commercial Artificial Intelligence-Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study
Impact of a Commercial Artificial Intelligence-Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study
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DOI：
10.2196/21056
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发表时间：
2020-08-01
影响因子：
3.2
通讯作者：
Shimizu, Taro
中科院分区：
文献类型：
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作者：
Harada, Yukinori;Shimizu, Taro
背景：患者在门诊部的等待时间直接关系到患者的满意度和护理质量，尤其是首次到普通内科门诊部就诊的患者。此外，减少从到达诊所到开始检查的等待时间是减少患者焦虑的关键。在普通内科门诊部使用自动病历记录系统是减少等待时间的一个有前途的策略。最近，日本Ubie公司开发了基于人工智能的普通内科门诊自动病历记录系统AI Monshin。目的：我们假设用AI Monshin取代手写自填式问卷的使用将减少普通内科门诊的等待时间。方法：回顾分析2017年4月至2020年4月日本某社区医院普通内科门诊部就诊患者无预约就诊的等待时间。AI Monshin于2019年4月实施。我们通过对每月中位数等待时间进行中断时间序列分析，比较了实施前后的中位数等待时间。并对主要结果进行补充分析。结果：共分析21615人次。AI Monshin实施后的中位等待时间(74.4分钟，IQR 57.1)与AI Monshin实施前(74.3分钟，IQR 63.7)相比没有显著差异(P=.12)。在中断的时间序列分析中，基本的线性时间趋势(-0.4分钟/月；P=0.06；95%CI-0.9至0.02)、水平变化(40.6分钟；P=0.09；95%CI-5.8至87.0)和斜率变化(-1.1分钟/月；P=0.16；95%CI-2.7至0.4)没有统计学意义。在对21,615人次中9054人次(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，IQR 5.2)略长于实施AI Monshin前(5.7分钟，IQR 5.0)(P=.003)。结论：基于人工智能的自动化病历记录系统的实施并没有减少普通内科门诊患者无预约就诊的等待时间，实施后检查时间略有增加；然而，该系统可能通过支持优化工作人员分配而提高了护理质量。
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.