Health care service is a high-credence service and patients may face difficulties ascertaining service quality in order to make choices about their available treatment options. Online health communities (OHCs) provide a convenient channel for patients to search for physicians’ information, such as Word-of-Mouth (WOM), particularly on physicians’ service quality evaluated by other patients. Existing studies from other service domains have proved that WOM impacts consumer choice. However, how patients make a choice based on physicians’ WOM has not been studied, particularly with reference to different patient characteristics and by using real data. One thousand eight hundred fifty three physicians’ real data were collected from a Chinese online health community. The data were analyzed using ordinary least squares (OLS) method. The study found that functional quality negatively moderated the relationship between technical quality and patient choice, and disease risk moderated the relationship between physicians’ service quality and patient choice. Our study recommends that hospital managers need to consider the roles of both technical quality and functional quality seriously. Physicians should improve their medical skills and bedside manners based on the severity and type of disease to provide better service.

Background: There is growing evidence that the internet has beneficial effects on the mental health of middle-aged and older people (≥45 years), but the evidence is inconclusive, and the underlying mechanisms are less known. Objective: This study aims to explore the relationship between multidimensional (devices, frequency, and purpose) internet use and depression in middle-aged and elderly Chinese, as well as the mediating effect of social participation. Moreover, this study will explore the moderating effect of the regional informatization development level (RIDL) on the relationships between individual internet use, social participation, and depression. Methods: Data on 17,676 participants aged 45 years or older were obtained from the China Health and Retirement Longitudinal Study (CHARLS) 2018 data set. The 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10) was used to identify the presence of depression. Logistic regression was used to explore the relationship between each dimension of internet use and depression. Multiple linear regression was used to explore the mediating effect of social participation and the moderating effect of the RIDL. Results: The results showed that 28.33% (5008/17,676) of the total population had depression. In terms of regional subgroups, respondents living in the western region exhibited the highest proportion of depression (2041/5884, 34.69%). Internet use was negatively associated with depression (odds ratio 0.613, 95% CI 0.542-0.692; P<.001). Various dimensions of internet use positively contributed to individual social participation and reduced individual depression (devices: β=–.170, 95% CI –0.209 to –0.127; frequency: β=–.065, 95% CI –0.081 to –0.047; and purpose: β=–.043, 95% CI –0.053 to –0.031). In addition, the RIDL weakened the relationship between individual-level internet use and social participation (internet use: F74.12,9.82=7.55, P<.001; devices: F51.65/9.88=5.23, P=.005; frequency: F66.74/10.08=6.62, P=.001; and purpose: F66.52/9.78=6.80, P=.001), and negatively moderated the relationship between the frequency of internet use and depression (frequency: F662.67/188.79=3.51, P=.03). Conclusions: This study found that different dimensions of internet use are associated with lower levels of depression. Social participation partially mediates the association between multidimensional internet use and depression in the eastern, central, and western regions, respectively. Additionally, the RIDL helps individuals further their internet use and social participation, reducing the impact of depression. However, this effect weakens sequentially from the western region to the central region and then to the eastern region.

Background: The coronavirus disease (COVID-19) epidemic poses an enormous challenge to the global health system, and governments have taken active preventive and control measures. The health informatics community in China has actively taken action to leverage health information technologies for epidemic monitoring, detection, early warning, prevention and control, and other tasks. Objective: The aim of this study was to develop a technical framework to respond to the COVID-19 epidemic from a health informatics perspective. Methods: In this study, we collected health information technology–related information to understand the actions taken by the health informatics community in China during the COVID-19 outbreak and developed a health information technology framework for epidemic response based on health information technology–related measures and methods. Results: Based on the framework, we review specific health information technology practices for managing the outbreak in China, describe the highlights of their application in detail, and discuss critical issues to consider when using health information technology. Technologies employed include mobile and web-based services such as Internet hospitals and Wechat, big data analyses (including digital contact tracing through QR codes or epidemic prediction), cloud computing, Internet of things, Artificial Intelligence (including the use of drones, robots, and intelligent diagnoses), 5G telemedicine, and clinical information systems to facilitate clinical management for COVID-19. Conclusions: Practical experience in China shows that health information technologies play a pivotal role in responding to the COVID-19 epidemic.

Septic shock is one of the most lethal conditions in ICU, and early risk prediction may help reduce mortality. We developed a TOPSIS-based Classification Fusion (TCF) model to predict mortality risk in septic shock patients using data from 4872 ICU patients from February 2003 to November 2023 across three hospitals. The model integrates seven machine learning models via the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), achieving AUCs of 0.733 in internal validation, 0.808 in the pediatric ICU, 0.662 in the respiratory ICU, with external validation AUCs of 0.784 and 0.786, respectively. It demonstrated high stability and accuracy in cross-specialty and multi-center validation. This interpretable model provides clinicians with a reliable early-warning tool for septic shock mortality risk, facilitating early intervention to reduce mortality.

Purpose: The purpose of this research is to investigate how postpartum mothers conduct self-disclosure on social media may obtain social support and therefore improve their depressive mood.Design/methodology/approach: The authors extract variables of self-disclosure by manual coding postpartum mothers' 835 posts from a parenting social media in China. The ordinary least squares model and the binary logistic regression model are used to test the proposed hypotheses.Findings: The study suggests that both mothers' superficial level disclosure and personal level disclosure positively affect online social support received, and the effect of personal level disclosure on social support is much greater than that of superficial level disclosure. Online social support received is related to the content of the post and reduces mothers' depressive mood. The authors further find that the association between personal level disclosure and depressive mood is fully mediated by social support.Research limitations/implications: The data are collected from a parenting social network. Although it is the major parenting social media with the most users in China, the generalizability of this model and the findings to other social media need additional research.Practical implications: This study offers implications for researchers and practitioners with regard to social media uses and impacts, which also has important implications for policy and interventions for the mental health of mothers.Originality/value: This paper makes theoretical contributions to the literature of social penetration theory and social support by (1) dividing self-disclosure into superficial level disclosure and personal level disclosure according to the intimacy of self-disclosure; (2) empirically investigating the direct effect of online self-disclosure on social support and the mediating effect of social support between online self-disclosure and mothers' depressive mood.