Impact Analysis of COVID-19 Pandemic on Hospital Reviews on Dianping Website in Shanghai, China: Empirical Study.

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.

Minimally Invasive Endovascular Repair for Nondissected Ascending Aortic Disease: A Systematic Review.

Objective: The aim of this study is to evaluate the efficacy of endovascular treatment for nondissected diseases of the ascending aorta. Data Sources. PubMed, Embase, and SciELO. Review Methods. In this study, we conducted a search on the PubMed, Embase, and SciELO databases for all cases of ascending aortic endovascular repair included in the literature published between January 2007 and July 2023, excluding type A aortic dissection. We reviewed 56 case reports and 7 observational studies included in this study, assessing the techniques, equipment, procedural steps, and results. We summarized the age, complications, follow-up time, and access route. Results: This study includes 63 articles reporting 105 patients (mean age: 64.96 ± 17.08 years) who received endovascular repair for nondissected ascending aortic disease. The types of disease include aneurysm (N = 16), pseudoaneurysm (N = 71), penetrating aortic ulcer (N = 10), intramural hematoma (N = 2), thrombosis (N = 2), iatrogenic coarctation (N = 1), and rupture of the aorta (N = 3). The success rate of surgery is 99.05% (104/105). Complications include endoleak (10.48%, 11/105), stroke (5.71%, 6/105), postoperative infection (1.91%, 2/105), acute renal failure (0.95%, 1/105), aortic rupture (0.95%, 1/105), thrombosis (0.95%, 1/105), and splenic infarction (0.95%, 1/105). Five patients required conversion to open surgery, two patients underwent endovascular reintervention, and four of these five patients underwent surgery due to endoleak. Early mortality was 2.86% (3/105). Conclusion: While the viability and results of endovascular repair for the treatment of ascending aortic disease are acknowledged in some circumstances, further research is needed to determine the safety and effectiveness of endovascular treatment for ascending aortic disease.