Shi Jinming, Gao Jinghong, Zhai Yunkai, Ye Ming, Lu Yaoen, He Xianying, Cui Fangfang, Ma Qianqian, Zhao Jie
The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
National Engineering Laboratory for Internet Medical Systems and Applications, Zhengzhou, China.
Front Med (Lausanne). 2021 Nov 23;8:781781. doi: 10.3389/fmed.2021.781781. eCollection 2021.
The outbreak of novel coronavirus disease 2019 (COVID-19) has led to tremendous individuals visit medical institutions for healthcare services. Public gatherings and close contact in clinics and emergency departments may increase the exposure and cross-infection of COVID-19. The purpose of this study was to develop and deploy an intelligent response system for COVID-19 voice consultation, to provide suggestions of response measures based on actual information of users, and screen COVID-19 suspected cases. Based on the requirements analysis of business, user, and function, the physical architecture, system architecture, and core algorithms are designed and implemented. The system operation process is designed according to guidance documents of the National Health Commission and the actual experience of prevention, diagnosis and treatment of COVID-19. Both qualitative (system construction) and quantitative (system application) data from the real-world healthcare service of the system were retrospectively collected and analyzed. The system realizes the functions, such as remote deployment and operations, fast operation procedure adjustment, and multi-dimensional statistical report capability. The performance of the machine-learning model used to develop the system is better than others, with the lowest Character Error Rate (CER) 8.13%. As of September 24, 2020, the system has received 12,264 times incoming calls and provided a total of 11,788 COVID-19-related consultation services for the public. Approximately 85.2% of the users are from Henan Province and followed by Beijing (2.5%). Of all the incoming calls, China Mobile contributes the largest proportion (66%), while China Unicom and China Telecom are accounted for 23% and 11%. For the time that users access the system, there is a peak period in the morning (08:00-10:00) and afternoon (14:00-16:00), respectively. The intelligent response system has achieved appreciable practical implementation effects. Our findings reveal that the provision of inquiry services through an intelligent voice consultation system may play a role in optimizing the allocation of healthcare resources, improving the efficiency of medical services, saving medical expenses, and protecting vulnerable groups.
2019年新型冠状病毒病（COVID-19）疫情导致大量人员前往医疗机构寻求医疗服务。诊所和急诊科的公众聚集及密切接触可能会增加COVID-19的暴露和交叉感染风险。本研究旨在开发并部署一个用于COVID-19语音咨询的智能响应系统，根据用户实际信息提供应对措施建议，并筛查COVID-19疑似病例。基于业务、用户和功能的需求分析，设计并实现了物理架构、系统架构及核心算法。系统运行流程依据国家卫生健康委员会的指导文件及COVID-19的实际防治经验进行设计。回顾性收集并分析了该系统实际医疗服务中的定性（系统建设）和定量（系统应用）数据。该系统实现了远程部署与运行、快速操作流程调整及多维度统计报告等功能。用于开发该系统的机器学习模型性能优于其他模型，字符错误率（CER）最低为8.13%。截至2020年9月24日，该系统共接到来电12264次，为公众提供了共计11788次与COVID-19相关的咨询服务。约85.2%的用户来自河南省，其次是北京（2.5%）。在所有来电中，中国移动所占比例最大（66%），中国联通和中国电信分别占23%和11%。用户接入系统的时间在上午（08:00 - 10:00）和下午（14:00 - 16:00）分别出现一个高峰期。该智能响应系统取得了显著的实际应用效果。我们的研究结果表明，通过智能语音咨询系统提供查询服务可能在优化医疗资源配置、提高医疗服务效率、节省医疗费用以及保护弱势群体方面发挥作用。
