Abstract：
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：在互联网时代，个人越来越习惯于在公共网络平台上收集必要的信息和表达意见。医疗保健部门也不例外,作为这些评论，在某种程度上,影响人们的医疗保健决策。在COVID-19大流行爆发期间，中国患者的医疗经验和他们对医院的评价如何变化还有待研究。因此,我们计划从互联网上收集患者就诊数据，以反映特定情况下的医疗关系现状。
目的：本研究旨在探讨不同阶段患者评论的差异（在，之前，以及在COVID-19大流行之后），以及不同类型的医院(儿童医院，妇产医院,和肿瘤医院)。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。对采集的数据进行分析，并提出了可以提高患者满意度的潜在解决方案。这项研究旨在帮助医院管理者为在突发公共卫生危机中寻求护理的患者提供更好的体验。
方法：选择全国排名前50位的综合性医院和排名前50位的专科医院（儿童医院，肿瘤医院,和妇产医院)，我们在大众点评网站上收集了这些医院的患者评论。使用ChatGPT，我们对负面评论的内容进行了分类。此外,我们使用SPSS(IBM公司)进行了统计分析,以检查负面评价的评分和构成.
结果：从2018年1月1日至2023年8月15日，共收集有效评论信息30317条，其中负面评论信息7696条。手工检查结果表明，ChatGPT的准确率为92.05％。F1评分为0.914。对这些数据的分析表明，大流行期间医院收到的评论和评级之间存在显着相关性。总的来说,在爆发期间,平均评论评分显著增加(P<.001).此外，不同类型医院的负面评价构成差异有统计学意义(P<.001)。儿童医院收到了关于等待时间和治疗效果的敏感反馈，而妇产医院的患者对医疗保健提供者的态度表现出更大的关注。肿瘤医院的患者表示希望及时检查和治疗，特别是在大流行期间。
结论：COVID-19大流行与患者评论评分有一定关联。不同类型的专科医院之间的评分和评论内容存在差异。使用ChatGPT分析患者评论内容代表了一种用于统计评估导致患者不满的因素的创新方法。这项研究的结果可以为医院管理者提供有价值的见解，以促进更和谐的医患关系并在突发公共卫生事件中提高医院绩效。
目的：本研究旨在探讨不同阶段患者评论的差异（在，之前，以及在COVID-19大流行之后），以及不同类型的医院(儿童医院，妇产医院,和肿瘤医院)。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。对采集的数据进行分析，并提出了可以提高患者满意度的潜在解决方案。这项研究旨在帮助医院管理者为在突发公共卫生危机中寻求护理的患者提供更好的体验。
方法：选择全国排名前50位的综合性医院和排名前50位的专科医院（儿童医院，肿瘤医院,和妇产医院)，我们在大众点评网站上收集了这些医院的患者评论。使用ChatGPT，我们对负面评论的内容进行了分类。此外,我们使用SPSS(IBM公司)进行了统计分析,以检查负面评价的评分和构成.
结果：从2018年1月1日至2023年8月15日，共收集有效评论信息30317条，其中负面评论信息7696条。手工检查结果表明，ChatGPT的准确率为92.05％。F1评分为0.914。对这些数据的分析表明，大流行期间医院收到的评论和评级之间存在显着相关性。总的来说,在爆发期间,平均评论评分显著增加(P<.001).此外，不同类型医院的负面评价构成差异有统计学意义(P<.001)。儿童医院收到了关于等待时间和治疗效果的敏感反馈，而妇产医院的患者对医疗保健提供者的态度表现出更大的关注。肿瘤医院的患者表示希望及时检查和治疗，特别是在大流行期间。
结论：COVID-19大流行与患者评论评分有一定关联。不同类型的专科医院之间的评分和评论内容存在差异。使用ChatGPT分析患者评论内容代表了一种用于统计评估导致患者不满的因素的创新方法。这项研究的结果可以为医院管理者提供有价值的见解，以促进更和谐的医患关系并在突发公共卫生事件中提高医院绩效。
