Shengchao Hou

Background: Effective postdischarge management is essential for maintaining disease control and improving long-term outcomes in rheumatoid arthritis (RA). Digital health technologies, particularly intelligent management platforms, provide new opportunities for continuous care and self-management in real-world settings. Objective: This study aimed to develop a nurse-led artificial intelligence (AI)–assisted interactive management platform for postdischarge RA care and examine its association with patient outcomes in a real-world clinical setting. Methods: A single-center, multicampus, real-world observational study with a quasi-experimental framework was conducted at Tongji Hospital between November 2024 and October 2025. Participants were allocated to either a platform-based management group (campus 1; n=184) or a comparison group receiving routine postdischarge care (campus 2; n=157). The platform enabled remote monitoring through a smartphone app, which collected information on joint symptoms, fatigue, medication adherence, laboratory results, and emotional status, with data uploaded to a secure cloud server. The AI system analyzed data in real time and alerted health care providers to abnormalities. Nurses and health coaches delivered personalized education and interventions. Primary outcomes were changes in Disease Activity Score in 28 Joints and Health Assessment Questionnaire–II scores at discharge and the 6-month follow-up. Secondary outcomes included medication adherence measured as the proportion of prescribed doses taken and patient satisfaction assessed via a 5-point Likert-scale questionnaire at 6 months. Results: A total of 341 patients with RA completed the 6-month follow-up. Disease Activity Score in 28 Joints values decreased significantly in the platform-based management group at 6 months (mean 5.04, SD 1.38 at discharge to mean 4.27, SD 1.20 at 6 months; P <.001), showing a greater reduction than in the comparison group (mean 4.86, SD 1.19 at discharge to mean 4.59, SD 1.36 at 6 months; within-group P =.03; between-group P =.02). Health Assessment Questionnaire–II scores improved significantly in the platform-based management group (mean 2.45, SD 0.75 at discharge to mean 1.67, SD 0.56 at 6 months; P <.001), while the comparison group showed no significant change ( P =.12; between-group P <.001). Medication adherence was higher in the platform-based group (166/184, 90.2% vs 125/157, 79.6%; χ ₁ ² =7.6; P =.006). Overall health care satisfaction was also higher in the platform-based group (176/184, 95.7% vs 120/157, 76.4%; χ ₁ ² =27.8; P <.001), with a greater proportion reporting to be “very satisfied” in this group (150/184, 81.5% vs 59/157, 37.6%). Conclusions: The intelligent interactive management platform markedly enhanced disease control, functional status, medication adherence, and satisfaction among patients with RA after discharge. This nurse-led, AI-assisted system represents a promising model for improving outpatient RA management.

Purpose: Advancements in electronic health (eHealth) technology have profoundly impacted patient engagement. This study aimed to develop and validate the Electronic Patient Engagement Behavior (EPEB) scale to measure the conceptual and underlying framework of patient engagement behaviors in an eHealth context. Patients and Methods: Initial measurement items were generated based on a literature review and qualitative research. Two rounds of surveys, a pilot survey and validation survey, were conducted to evaluate the psychometric properties of the scale. Results: The EPEB scale consists of 15 items in four dimensions: disease information search, physician-patient interaction, social interaction between patients, and disease self-monitoring. In the pilot survey, the exploratory factor analysis revealed a four-factor model, explaining 69.411% of variance. In the validation survey, the Cronbach’s α coefficient of each sub-scale was 0.865, 0.904, 0.904, and 0.900 respectively. The Spearman-Brown split coefficient of the scale was 0.963. The results of the cross-sex measurement equivalence test indicate that all fit indices met the measurement criteria. The confirmatory factor analysis indicated second-order 4-factor model fit the data well. The EPEB has a good reliability and validity. Conclusion: The EPEB scale provides a reliable tool for measuring patient engagement behaviors in the eHealth context. The utilization of this scale may yield valuable insights into strategies for enhancing patient engagement and optimizing health outcomes.

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: 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.

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.