Jing Liu

Background: In China, medical savings accounts (MSAs) were initially limited in covering long‐term outpatient costs for chronic diseases. To enhance financial protection, the government introduced the family mutual aid system (FMAS). This study explores the associations of FMAS with healthcare utilization and expenses, and examines its heterogeneous effects across different characteristic groups. Methods: A mixed‐methods study was conducted in Hangzhou. The quantitative phase relied on a cross‐sectional survey of 1001 hypertensive or diabetic patients to assess outpatient and inpatient utilization and expenses. Causal forest models were applied to explore overall and subgroup‐level heterogeneous associations related to FMAS. The qualitative phase included semistructured interviews with 4 medical insurance managers, 10 outpatient physicians, and 10 patients, analyzed using the Smith Policy Implementation Process Model to identify factors driving heterogeneity. Results: Compared with non‐FMAS users, those who used FMAS exhibited a higher likelihood of outpatient service use, accompanied by significantly greater outpatient visits (ATE = 0.821, p < 0.001), total payments (ATE = 0.271, p < 0.001), and out‐of‐pocket (OOP) payments (ATE = 0.547, p < 0.001), as well as a higher probability of seeking care at high‐tier hospitals (ATE = 0.196, p < 0.001). By contrast, they showed a lower probability of hospitalization (ATE = −0.163, p = 0.026) and lower total inpatient payments (ATE = −0.557, p < 0.001). These association‐based group differences varied across subgroups defined by age, health status, income, and education. Qualitative findings further revealed key contributors to the observed heterogeneity, including policy design and procedural convenience, promotional incentives, individual healthcare needs, socioeconomic status, insurance status, and contextual environmental factors. Conclusions: FMAS use correlates with higher outpatient utilization and expenses, alongside reduced hospitalization rates, but may shift demand toward higher tier hospitals. Despite its financial‐protection objective, policy‐associated outcomes are complex and socially heterogeneous. Higher status groups may gain comparatively more, while vulnerable populations face potential access barriers. Targeted adjustments are needed to strengthen the scheme’s effectiveness and equity.

BACKGROUND Internet hospitals have emerged as a innovative telemedicine model in China, providing online services like consultations, prescription renewals, and report interpretations. These platforms generate a vast amount of authentic, patient-written text describing symptoms and needs. While Natural Language Processing (NLP) and Large Language Models (LLMs) offer powerful tools for text analysis, a significant gap exists in applying these techniques to systematically decode and categorize the specific service intents within real-world clinical texts from internet hospitals. Most existing research relies on data from social media or forums, not from actual clinical practice. OBJECTIVE This study aimed to leverage AI to analyze patient descriptions from an internet hospital platform with three primary objectives: 1.To develop and optimize a specialized large language model for understanding unstructured clinical narratives. 2.To analyze and categorize the specific service intents expressed in patients' medical descriptions. 3.To examine the distribution of these patient intents across different clinical departments to reveal demand patterns for strategic planning. METHODS The study retrospectively collected 16,457 de-identified patient consultation records from a tertiary hospital's internet hospital in China. A hybrid methodology was employed: First, an unsupervised learning approach was used to discover intent categories. Patient texts were converted into embeddings using the chinese-roberta-wwm-ext-large model, reduced in dimensionality with UMAP, and clustered using K-Means, which revealed eight distinct intent categories. Subsequently, a random sample of 5,000 records was manually annotated by three clinical experts based on these categories (achieving high inter-rater agreement, Fleiss' Kappa = 0.82). This labeled dataset was then used to fine-tune the pre-trained model, and its performance was compared against traditional machine learning models (e.g., SVM with TF-IDF features). RESULTS The analysis successfully identified eight coherent patient intent categories. The most prevalent needs were "Online Interpretation of Existing Medical Reports" (25.5%) and "Initial Consultation" (24.4%). The fine-tuned chinese-roberta-wwm-ext-large model achieved the best performance with a weighted F1-score of 0.7030, outperforming traditional models. A highly significant association was found between clinical departments and the types of patient intents (χ² = 8874.241, p < .01). Based on standardized residual analysis, four distinct operational models for internet hospitals were synthesized: Centralized Efficiency Departments (e.g., Dentistry, Dermatology): Strongly associated with "Initial Consultation" and "Online Test Booking." Long-Term Management Departments (e.g., Hematology, Gastroenterology): Primarily linked to "Prescription Renewal for Chronic Diseases." Balanced Departments (e.g., Nephrology, ENT): Managed a diverse mix of intents like "Report Interpretation" and "Online Follow-up." Low-Engagement Departments (e.g., Neurosurgery, Oncology): Primarily used for "Report Interpretation" and "Remote Guidance," serving an auxiliary role. CONCLUSIONS This study demonstrates that a hybrid AI methodology effectively identifies and categorizes patient service intents within internet hospitals. The findings reveal a clear, efficiency-driven allocation of patient demand, with core services being report interpretation and initial consultations, and significant variation across departments. This provides a critical evidence base for hospitals to optimize resource allocation, design specialized operational models for different departments, and strategically develop their telemedicine services. Future work should focus on refining categories, integrating multimodal data (e.g., images), and modeling temporal consultation sequences for a more comprehensive understanding.

Background: To improve accessibility and financial support for outpatient services, China introduced a scheme to decrease cost‐sharing for outpatient care under the Urban Employee Basic Medical Insurance. This study evaluates the health impacts of this policy and examines its heterogeneous effects.Methods: Utilising data from the 2018 China Health and Retirement Longitudinal Study, we analysed 2896 individual‐level observations across 105 prefectures. Propensity score matching and a causal forest model were applied to evaluate the effects on chronic disease status, body pain, self‐rated health, and hospitalisation, while accounting for various demographic, socioeconomic, residential, health‐related behaviours, and prefecture‐specific factors.Results: The reduction in cost‐sharing was significantly linked to decreased probabilities of chronic disease (Average Treatment Effect (ATE) = −0.0619, p < 0.01), body pain (ATE = −0.0715, p < 0.05), and hospitalisation (ATE = −0.0592, p < 0.001), as well as improved self‐rated health (ATE = 0.1557, p < 0.001). These benefits may be attributed to reduced out‐of‐pocket payments for outpatient care (ATE = −287.6112, p < 0.01) and increased outpatient visits (ATE = 0.0414 visits, p < 0.05). Causal forest analyses revealed that older individuals, those with higher educational attainment, higher household income, urban residents, and those engaging in healthier behaviours exhibited larger treatment effects.Conclusions: Decreasing outpatient cost‐sharing in China has beneficial health outcomes, with variations in its impact based on socio‐economic status and health behaviours. It is advisable to further increase reimbursement rates and broaden benefit packages for outpatient care, while addressing the unequal distribution of benefits.

Background: The urban-rural disparities in health outcomes in China are remarkable. The internet has shown the potential to reduce the likelihood of contracting a disease by increasing disease knowledge. However, little is known about the effects of internet use in alleviating health inequities between urban and rural areas. Objective: This study aimed to examine the mediation and moderation of health disparities between urban and rural older adults through internet use. Methods: A total of 8223 respondents were selected from the China Health and Retirement Longitudinal Study 2018 data set. Basic activities of daily living, a brief Community Screening Instrument for Dementia, and the Centre for Epidemiologic Studies Depression Scale were used to measure functional disability, cognitive function, and depressive symptoms, respectively. Logistic regressions testing “internet use×urban-rural status” interactions for moderation and Karlson-Holm-Breen decomposition for mediation were performed. Results: Internet use moderated the urban-rural disparities in cognitive function (odds ratio 7.327, 95% CI 3.011-17.832) and depressive symptoms (odds ratio 1.070, 95% CI 1.037-1.787), but the moderating effects were significant only for those using the internet daily. Karlson-Holm-Breen results showed the suppression effects of using the internet daily (β=.012, 95% CI .002-.021) on the association between urban-rural status and cognitive function. The urban-rural inequality in depressive symptoms was partially attributed to the disparity in internet use (β=−.027, 95% CI −.043 to −.009). Conclusions: The urban-rural inequalities in mental health are partially attributable to disparities in the prevalence of internet use between the 2 groups. However, using the internet is more beneficial for the psychological health of rural users, thereby alleviating the urban-rural disparities in health. Providing convenient channels for rural older adults to use the internet, improving the ability of rural users to effectively use the internet, and promoting internet popularity in rural areas are effective approaches to reducing urban-rural health inequalities.

Background: Although patient accessible electronic health records (PAEHRs) offer great potential in enhancing the provision of patient-centered care and improving satisfaction, the adoption rate is still low. Currently, few studies are there for researchers and health organization leaders to understand patients’ thoughts and related factors of PAEHRs adoption in developing countries. China adopted more limited practices of PAEHRs, among which we selected Yuebei People's Hospital as an example. Objective: The study aimed to research patient attitudes toward PAEHRs use and the associated factors of patients’ adoption of PAEHRs in China, which is achieved by both qualitative and quantitative studies. Methods: This study employed sequential mixed-methods. The DeLone & McLean information systems (D&M IS) success model, Unified Theory of Acceptance and Use of Technology (UTAUT) and task-technology fit (TTF) model were used to guide the research. Finally, we collected 28 valid in-depth interview responses, 51 valid semi-structured interview responses and 235 valid questionnaire responses. The research model was tested and validated using data collected. Results: The findings of the qualitative study reveal that patients’ rate perceived task productivity and customer satisfaction as benefits, and poor-quality information as flaws. Results of the quantitative study show that the drivers of behavioral intention are performance expectance, effort expectancy and social influence; the predictors of use behavior are TTF and behavioral intention. Conclusion: It is necessary to consider PAEHRs’ task-tool role in patients’ adoption behavior. Hospitalized patients value PAEHRs’ practical attributes and attach much importance to the information content and application design.

BACKGROUND The urban-rural disparities in health outcomes in China are remarkable. The internet has shown the potential to reduce the likelihood of contracting a disease by increasing disease knowledge. However, little is known about the effects of internet use in alleviating health inequities between urban and rural areas. OBJECTIVE This study aimed to examine the mediation and moderation of health disparities between urban and rural older adults through internet use. METHODS A total of 8223 respondents were selected from the China Health and Retirement Longitudinal Study 2018 data set. Basic activities of daily living, a brief Community Screening Instrument for Dementia, and the Centre for Epidemiologic Studies Depression Scale were used to measure functional disability, cognitive function, and depressive symptoms, respectively. Logistic regressions testing “internet use×urban-rural status” interactions for moderation and Karlson-Holm-Breen decomposition for mediation were performed. RESULTS Internet use moderated the urban-rural disparities in cognitive function (odds ratio 7.327, 95% CI 3.011-17.832) and depressive symptoms (odds ratio 1.070, 95% CI 1.037-1.787), but the moderating effects were significant only for those using the internet daily. Karlson-Holm-Breen results showed the suppression effects of using the internet daily (β=.012, 95% CI .002-.021) on the association between urban-rural status and cognitive function. The urban-rural inequality in depressive symptoms was partially attributed to the disparity in internet use (β=−.027, 95% CI −.043 to −.009). CONCLUSIONS The urban-rural inequalities in mental health are partially attributable to disparities in the prevalence of internet use between the 2 groups. However, using the internet is more beneficial for the psychological health of rural users, thereby alleviating the urban-rural disparities in health. Providing convenient channels for rural older adults to use the internet, improving the ability of rural users to effectively use the internet, and promoting internet popularity in rural areas are effective approaches to reducing urban-rural health inequalities.

BACKGROUND The application of gamification and serious games in health attracts much scholarly attention in the last decade. However, we still lack a comprehensive understanding of contributions of gamification and serious games to the growth of scientific research in the health domain. OBJECTIVE The purpose of this study is to obtain insights into the current state and future directions of gamification and serious games in health by analyzing the related academic literature. METHODS This study collected papers related to gamification in health published in the last 10 years from Web of Science Core Collection database. Analyzing tools of CiteSpace and VOSviewer were used to investigate the literature characteristics, including publications growth, related journal and disciplines, author collaboration, as well as impact articles and research trends. RESULTS A total of 1611 bibliographic records on gamification in health were included. Publications increased considerably with 45.9% at an annual compound average growth rate. Studies of gamification in health were conducted with multiple disciplines of knowledge from various journals. Some stable authors' collaboration is formed up. Five impactful papers are selected to reflect the research evolution. The research trends of the area are gamification in rehabilitation, medical education, mobile health, physical activity and mental health. CONCLUSIONS Research of gamification in health is promising and drives towards meaningful healthcare practice and medical education. This study might provide potential implications for future research of gamification in health.

Background: Since the early 1970s, health care provision has experienced rapid growth in the investment and adoption of health information technologies (HITs). However, the development and deployment of HITs has often been conducted in silos, at different organizational levels, within different regions, and in various health care settings; this has resulted in their infrastructures often being difficult to manage or integrate. Health information standards (ie, the set norms and requirements that underpin the deployment of HITs in health care settings) are expected to address these issues, yet their adoption remains to be frustratingly low among health care information technology vendors. Objective: This study aimed to synthesize a comprehensive framework of factors that affect the adoption and deployment of health information standards by health care organizations. Methods: First, electronic databases, including Web of Science, Scopus, and PubMed, were searched for relevant articles, with the results being exported to the EndNote reference management software. Second, study selection was conducted according to pre-established inclusion and exclusion criteria. Finally, a synthesized best fit framework was created, which integrated a thematic analysis of the included articles. Results: In total, 35 records were incorporated into the synthesized framework, with 4 dimensions being identified: technology, organization, environment, and interorganizational relationships. The technology dimension included relative advantage, complexity, compatibility, trialability, observability, switching cost, standards uncertainty, and shared business process attributes. The organization dimension included organizational scale, organizational culture, staff resistance to change, staff training, top management support, and organizational readiness. The environment dimension included external pressure, external support, network externality, installed base, and information communication. Finally, the interorganizational relationships dimension included partner trust, partner dependence, relationship commitment, and partner power. Conclusions: The synthesized framework presented in this paper extends the current understanding of the factors that influence the adoption of health information standards in health care organizations. It provides policy and decision makers with a greater awareness of factors that hinder or facilitate their adoption, enabling better judgement and development of adoption intervention strategies. Furthermore, suggestions for future research are provided.

BACKGROUND Since the early 1970s, health care provision has experienced rapid growth in the investment and adoption of health information technologies (HITs). However, the development and deployment of HITs has often been conducted in silos, at different organizational levels, within different regions, and in various health care settings; this has resulted in their infrastructures often being difficult to manage or integrate. Health information standards (ie, the set norms and requirements that underpin the deployment of HITs in health care settings) are expected to address these issues, yet their adoption remains to be frustratingly low among health care information technology vendors. OBJECTIVE This study aimed to synthesize a comprehensive framework of factors that affect the adoption and deployment of health information standards by health care organizations. METHODS First, electronic databases, including Web of Science, Scopus, and PubMed, were searched for relevant articles, with the results being exported to the EndNote reference management software. Second, study selection was conducted according to pre-established inclusion and exclusion criteria. Finally, a synthesized best fit framework was created, which integrated a thematic analysis of the included articles. RESULTS In total, 35 records were incorporated into the synthesized framework, with 4 dimensions being identified: technology, organization, environment, and interorganizational relationships. The technology dimension included relative advantage, complexity, compatibility, trialability, observability, switching cost, standards uncertainty, and shared business process attributes. The organization dimension included organizational scale, organizational culture, staff resistance to change, staff training, top management support, and organizational readiness. The environment dimension included external pressure, external support, network externality, installed base, and information communication. Finally, the interorganizational relationships dimension included partner trust, partner dependence, relationship commitment, and partner power. CONCLUSIONS The synthesized framework presented in this paper extends the current understanding of the factors that influence the adoption of health information standards in health care organizations. It provides policy and decision makers with a greater awareness of factors that hinder or facilitate their adoption, enabling better judgement and development of adoption intervention strategies. Furthermore, suggestions for future research are provided.

Background: Complaints made online by patients about their health care experiences are becoming prevalent because of widespread worldwide internet connectivity. An a priori framework, based on patient centeredness, may be useful in identifying the types of issues patients complain about online across multiple settings. It may also assist in examining whether the determinants of patient-centered care (PCC) mirror the determinants of patient experiences. Objective: The objective of our study was to develop a taxonomy framework for patient complaints online based on patient centeredness and to examine whether the determinants of PCC mirror the determinants of patient experiences. Methods: First, the best fit framework synthesis technique was applied to develop the proposed a priori framework. Second, electronic databases, including Web of Science, Scopus, and PubMed, were searched for articles published between 2000 and June 2018. Studies were only included if they collected primary quantitative data on patients’ online complaints. Third, a deductive and inductive thematic analysis approach was adopted to code the themes of recognized complaints into the framework. Results: In total, 17 studies from 5 countries were included in this study. Patient complaint online taxonomies and theme terms varied. According to our framework, patients expressed most dissatisfaction with patient-centered processes (101,586/204,363, 49.71%), followed by prerequisites (appropriate skills and knowledge of physicians; 50,563, 24.74%) and the care environment (48,563/204,363, 23.76%). The least dissatisfied theme was expected outcomes (3651/204,363, 1.79%). People expressed little dissatisfaction with expanded PCC dimensions, such as involvement of family and friends (591/204,363, 0.29%). Variation in the concerns across different countries’ patients were also observed. Conclusions: Online complaints made by patients are of major value to health care providers, regulatory bodies, and patients themselves. Our PCC framework can be applied to analyze them under a wide range of conditions, treatments, and countries. This review has shown significant heterogeneity of patients’ online complaints across different countries.