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中国实体医院运营的互联网医院在线咨询服务现状与挑战:多中心数据的大规模汇总分析

The status and challenges of online consultation service in internet hospitals operated by physical hospitals in China: a large-scale pooled analysis of multicenter data.

作者信息

Yang Ming, Yan Yiwei, Xu Zhong, Liu Hongli, Ran Jing, Zheng Yingbin, Cai Zhefeng, Liu Zhengwei, Gong Kai

机构信息

The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, 10 Shanggu Road, Siming District, Xiamen City, 361003, China.

College of Public Health, Zhengzhou University, Zhengzhou, 450001, China.

出版信息

BMC Health Serv Res. 2025 Apr 28;25(1):611. doi: 10.1186/s12913-025-12787-6.


DOI:10.1186/s12913-025-12787-6
PMID:40289105
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12036205/
Abstract

BACKGROUND: While Internet hospitals have rapidly developed as China's dominant digital healthcare model, critical evidence gaps persist regarding operational status and challenges of their core online consultation services. This study aimed to evaluate the current status and challenges of online consultation in Internet hospitals services through large-scale multi-center business data analysis. METHODS: Retrospective analysis of 594,695 online consultations (2020-2021) from 30 Internet hospitals across 11 Chinese provinces. Descriptive analyses were conducted on counselee demographics, consultant qualifications, and order informations. A novel five-category consultation classification was applied. Multivariate logistic regression identified the inflencing factors for order, while locally weighted regression (LOESS) modeled workload-response relationships. RESULTS: There were 244,678 counselees (median age 29) and 5,781 providers (91.89% doctors) involved. Service are concentrated in pediatrics, obstetrics and gynecology (48.25%). Senior-title providers handled 43.79% consultations but showed reduced completion probability (OR = 0.77). The main types of consultations were re-visit (44.89%) and first visit (34.57%). Temporal patterns revealed peak consultation hours at 10:00 (8.11%) and 16:00 (7.29%), with provider response peaks at 12:00 (5.38%), 16:00 (6.61%), and 21:00 (6.63%), averaging 3.64-hour response delays. Provided medical history (OR = 2.13) could independently increase the response probability, whereas senior title (OR = 0.77) could reduce such probability. Workload-response efficiency transitioned from positive (< 78 orders) to negative correlation (> 1,700 orders), with 27.69% uncompleted orders attributed to consultant factors (75.87%). CONCLUSIONS: Even with the increased momentum, the online consultation service still faces many challenges mainly including the relative absence of elderly patients with chronic diseases, personnel qualification issues, the imbalance of service supply and demand, the unfitness of order contents with official regulations, and the insufficient quality control of response rate and timeliness. Comprehensive measures should be carried out to promote the effectiveness of online consultation for better disease prevention and control.

摘要

背景:互联网医院作为中国主要的数字医疗模式迅速发展,但其核心在线咨询服务的运营状况和挑战仍存在关键证据空白。本研究旨在通过大规模多中心业务数据分析评估互联网医院服务中在线咨询的现状和挑战。 方法:对来自中国11个省份30家互联网医院的594,695次在线咨询(2020 - 2021年)进行回顾性分析。对咨询对象人口统计学、咨询师资质和订单信息进行描述性分析。应用一种新的五类咨询分类法。多变量逻辑回归确定订单的影响因素,而局部加权回归(LOESS)模拟工作量 - 响应关系。 结果:涉及244,678名咨询对象(中位年龄29岁)和5,781名服务提供者(91.89%为医生)。服务集中在儿科、妇产科(48.25%)。高级职称提供者处理了43.79%的咨询,但完成概率降低(OR = 0.77)。咨询的主要类型是复诊(44.89%)和初诊(34.57%)。时间模式显示咨询高峰时间为10:00(8.11%)和16:00(7.29%),提供者响应高峰为12:00(5.38%)、16:00(6.61%)和21:00(6.63%),平均响应延迟3.64小时。提供病史(OR = 2.13)可独立提高响应概率,而高级职称(OR = 0.77)则会降低这种概率。工作量 - 响应效率从正相关(<78个订单)转变为负相关(>1,700个订单),27.69%的未完成订单归因于咨询师因素(75.87%)。 结论:即使发展势头增强,在线咨询服务仍面临诸多挑战,主要包括慢性病老年患者相对缺乏、人员资质问题、服务供需不平衡、订单内容不符合官方规定以及响应率和及时性的质量控制不足。应采取综合措施提高在线咨询的有效性,以更好地防控疾病。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc53/12036205/3a3e22d103c2/12913_2025_12787_Fig10_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc53/12036205/39e2344a95b9/12913_2025_12787_Fig7_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc53/12036205/3a3e22d103c2/12913_2025_12787_Fig10_HTML.jpg

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[2]
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[3]
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A review of multi-factor authentication in the Internet of Healthcare Things.

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[5]
Using Telemedicine during the COVID-19 Pandemic: How Service Quality Affects Patients' Consultation.

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Doctors' Preferences in the Selection of Patients in Online Medical Consultations: An Empirical Study with Doctor-Patient Consultation Data.

Healthcare (Basel). 2022-7-30

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Knowledge Graph and Deep Learning-based Text-to-GQL Model for Intelligent Medical Consultation Chatbot.

Inf Syst Front. 2022-7-6

[8]
Improving patient self-description in Chinese online consultation using contextual prompts.

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Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province.

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[10]
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