在互联网时代，人们越来越习惯于在公共网络平台上收集必要的信息并表达自己的意见。医疗保健行业也不例外，这些言论在一定程度上影响着人们的医疗决策。在COVID-19大流行期间，中国患者的医疗体验和对医院的评价发生了怎样的变化还有待研究。因此，我们计划从互联网上收集患者就诊数据，以反映特定情况下医疗关系的现状。本研究旨在探讨新冠肺炎疫情不同阶段（期间、之前、之后）患者评价的差异。 19大流行，以及不同类型医院之间（儿童医院、妇产医院、肿瘤医院）。此外，通过利用 ChatGPT (OpenAI)，该研究对医院负面评价的要素进行了分类。对获得的数据进行分析，并提出可以提高患者满意度的潜在解决方案。本研究旨在协助医院管理者为突发公共卫生危机中就诊的患者提供更好的体验。选取全国排名前50的综合性医院和排名前列的专科医院（儿童医院、肿瘤医院、妇产医院），我们在大众点评网站上收集了这些医院的患者评价。使用 ChatGPT，我们对负面评论的内容进行了分类。此外，我们还利用SPSS（IBM公司）进行了统计分析，考察负面评价的评分和构成。2018年1月1日至2023年8月15日，共收集有效评论信息30317条，其中负面评价7696条。评论信息。人工检测结果显示，ChatGPT 的准确率为 92.05%。 F1 分数为 0.914。对这些数据的分析显示，大流行期间医院收到的评论和评级之间存在显着相关性。总体而言，疫情期间平均评论得分显着上升（P<.001）。此外，不同类型医院的负面评价构成也存在显着差异（P<.001）。儿童医院收到了有关等待时间和治疗效果的敏感反馈，而妇产医院的患者则更加关注医疗保健提供者的态度。肿瘤医院的患者表达了及时检查和治疗的愿望，尤其是在大流行期间。COVID-19大流行与患者评价分数存在一定关联。不同类型专科医院的评分和评价内容存在差异。使用 ChatGPT 分析患者评论内容代表了一种统计评估导致患者不满意的因素的创新方法。本研究的结果可以为医院管理人员在突发公共卫生事件期间建立更和谐的医患关系并提高医院绩效提供宝贵的见解。©霍伟学、何孟伟、曾兆祥、包贤浩、吕野、田文、冯家轩，锐锋。最初发表于《医学互联网研究杂志》(https://www.jmir.org)，2024 年 7 月 2 日。

In the era of the internet, individuals have increasingly accustomed themselves to gathering necessary information and expressing their opinions on public web-based platforms. The health care sector is no exception, as these comments, to a certain extent, influence people's health care decisions. During the onset of the COVID-19 pandemic, how the medical experience of Chinese patients and their evaluations of hospitals have changed remains to be studied. Therefore, we plan to collect patient medical visit data from the internet to reflect the current status of medical relationships under specific circumstances.This study aims to explore the differences in patient comments across various stages (during, before, and after) of the COVID-19 pandemic, as well as among different types of hospitals (children's hospitals, maternity hospitals, and tumor hospitals). Additionally, by leveraging ChatGPT (OpenAI), the study categorizes the elements of negative hospital evaluations. An analysis is conducted on the acquired data, and potential solutions that could improve patient satisfaction are proposed. This study is intended to assist hospital managers in providing a better experience for patients who are seeking care amid an emergent public health crisis.Selecting the top 50 comprehensive hospitals nationwide and the top specialized hospitals (children's hospitals, tumor hospitals, and maternity hospitals), we collected patient reviews from these hospitals on the Dianping website. Using ChatGPT, we classified the content of negative reviews. Additionally, we conducted statistical analysis using SPSS (IBM Corp) to examine the scoring and composition of negative evaluations.A total of 30,317 pieces of effective comment information were collected from January 1, 2018, to August 15, 2023, including 7696 pieces of negative comment information. Manual inspection results indicated that ChatGPT had an accuracy rate of 92.05%. The F1-score was 0.914. The analysis of this data revealed a significant correlation between the comments and ratings received by hospitals during the pandemic. Overall, there was a significant increase in average comment scores during the outbreak (P<.001). Furthermore, there were notable differences in the composition of negative comments among different types of hospitals (P<.001). Children's hospitals received sensitive feedback regarding waiting times and treatment effectiveness, while patients at maternity hospitals showed a greater concern for the attitude of health care providers. Patients at tumor hospitals expressed a desire for timely examinations and treatments, especially during the pandemic period.The COVID-19 pandemic had some association with patient comment scores. There were variations in the scores and content of comments among different types of specialized hospitals. Using ChatGPT to analyze patient comment content represents an innovative approach for statistically assessing factors contributing to patient dissatisfaction. The findings of this study could provide valuable insights for hospital administrators to foster more harmonious physician-patient relationships and enhance hospital performance during public health emergencies.©Weixue Huo, Mengwei He, Zhaoxiang Zeng, Xianhao Bao, Ye Lu, Wen Tian, Jiaxuan Feng, Rui Feng. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.07.2024.