Introduction This formative study aimed to develop and evaluate supporter-focused text messages intended to enhance helpful support behaviors and reduce counterproductive support behaviors in the context of smoking cessation. Methods We developed 65 messages informed by responsiveness theory and established research on cessation. One hundred and ninety six adults (eligibility: ≥18 years, knowing someone who smokes) rated the messages on three dimensions of helpfulness (i.e., clarity, usefulness, relevance). Using mixed-effects regression models, we examined whether helpfulness ratings differed by sociodemographic factors, relationship factors, and tobacco use perceptions. Results Mean ratings ranged from 4.04 to 4.31/5. Compared with participants who reported knowing a spouse/partner who smoked, those who knew a friend (β̂= 0.34, SE = 0.16, 95% CI: 0.04 - 0.64), family member (β̂= 0.38, SE = 0.15, 95% CI: 0.07 - 0.67), or coworker/roommate (β̂= 0.56, SE = 0.22, 95% CI:0.14 - 0.98) provided relatively higher ratings. Greater perceived relationship closeness to the individual who smokes was positively associated with higher ratings (β̂= 0.08, SE = 0.03, 95% CI:0.03- 0.14). Conclusion The high ratings suggest that potential support providers found the messages helpful and tailoring them to relationship characteristics might provide a small additional benefit.
Objectives This study aimed to evaluate order management (OM) efficiency and fulfilment performance within ERP-supported healthcare supply chains during the COVID-19 pandemic in the United Arab Emirates (UAE). The study was motivated by the increasing need for resilient digital supply chain systems capable of supporting uninterrupted access to critical medical supplies during public health emergencies. Methods A quantitative retrospective longitudinal design was employed using secondary ERP data extracted from 14 Emirates Health Services (EHS) healthcare facilities between 2020 and 2022. A total of 87 essential medical and pharmaceutical items across four categories, including COVID-19 medications, supportive medications, personal protective equipment (PPE)/disposables, and vaccines, were analyzed. Order fulfilment rate was used as the primary performance indicator and compared against the healthcare industry benchmark of 95%. Wilcoxon Signed-Rank and Friedman tests were conducted to assess differences in fulfilment performance across study years and healthcare facility types. Results ERP-supported healthcare supply chains demonstrated standardized order processing and operational coordination during the early stages of the pandemic; however, observed performance patterns were likely influenced by additional factors including supply disruptions, supplier constraints, and changing demand conditions. Hospitals and primary healthcare centres demonstrated relatively high and stable fulfilment performance in 2020 and 2021. However, statistically significant variability and declines in fulfilment rates were observed in 2022 across several supply categories, particularly within public health centres. Overall, fulfilment rates remained below the 95% benchmark throughout the study period. Conclusion The findings indicate that healthcare supply chains operating within ERP-supported environments maintained operational continuity and supply chain coordination during the COVID-19 pandemic. However, the observed performance patterns suggest that ERP systems alone may not be sufficient to ensure optimal healthcare supply chain resilience during large-scale disruptions. Integrating ERP systems with advanced analytics, supplier diversification strategies, and real-time decision-support tools may further strengthen healthcare supply chain performance and emergency preparedness.
Objective To develop and preliminarily assess the usability of a visual usability checklist designed for rural users to evaluate the mHealth interfaces and provide practical feedback to designers. Methods A two-phase research design was adopted: the first phase involved developing checklist items through a literature review, followed by a pilot application with 30 rural users from various regions of China and the Spring Rain Doctor App to assess the checklist’s effectiveness. The results The item checklist identified 6 dimensions and 40 items, with a focus on rural Chinese users. In the pilot application, older users took a median of 13 minutes (IQR: 9.5–16), whereas younger users finished in 3 minutes (IQR: 2–5). The results revealed that older rural users are less familiar with mHealth apps than younger users are. The evaluation results, together with the Mann–Whitney U test results, indicate that the checklist has initial potential for assessing the usability and experiences of Chinese rural mHealth users. Conclusion This study developed a domain-specific checklist for assessing the visual usability of mHealth applications for rural users, providing product designers with a structured visual evaluation tool to better understand the needs of rural users and guide optimization of the mHealth application’s UI.
Promoting mental well-being among young people is a pressing need for Sweden to mitigate declines in health among the next generation. To achieve this, various programs have been implemented at both the school and municipal levels. Different municipalities implement programs, monitor, and collect data on youth mental well-being within their areas. Analysis of these data is challenging because data silos exist across institutions. System-level assessment has been difficult in both the reasoning and decision-making phases, and in attributing the impact of these strategies to the specific actions taken by municipalities. In this work, we propose using a data fusion approach to create a common dataset for studying youth mental well-being by combining data from various departments across Swedish municipalities. We identify the required datasets, along with their schemas, metadata, and definitions, and develop a graph database design that defines standard terms and related concepts. We deploy this min two Stockholm municipalities to demonstrate the approach’s ability to query across different institutional datasets through the common graph schema. Finally, we outline the use of a graph database in a participatory setting to better understand programs that promote youth mental well-being across departments in both municipalities.
Background Short-video platforms increasingly influence patients’ access to glioma treatment information; however, the quality and reliability of such content remain unclear. Methods This cross-sectional study analyzed 200 high-visibility Chinese-language videos on TikTok and Bilibili (TikTok, n = 103; Bilibili, n = 97). Videos were evaluated for engagement metrics, uploader characteristics, and content coverage. Information quality and reliability were assessed using the Global Quality Score, modified DISCERN, Journal of the American Medical Association benchmark criteria, and the Patient Education Materials Assessment Tool. Results TikTok videos were shorter and received greater user engagement than Bilibili videos, while also achieving higher Global Quality Score and modified DISCERN scores. Professional uploaders provided more reliable and evidence-based content, whereas non-professional videos were longer and attracted significantly more comments (p < 0.001); differences in likes, collections, and shares were not statistically significant. Only 30.0% of videos addressed grade- or molecular-specific treatment pathways, and prognostic factors were discussed in only 15.5% of videos; recurrence management and long-term care were seldom covered. Engagement metrics were weakly correlated with quality scores, indicating a mismatch between popularity and information quality. Conclusions Chinese-language short videos on glioma treatment provide accessible but uneven patient education. Verified medical authorship, guideline-based content labeling, and quality-weighted recommendation strategies may improve the visibility of trustworthy information.
Objective This study aimed to evaluate the quality and reliability of dengue fever-related videos on social media platforms. Methods A cross-sectional study was conducted on January 10, 2026. Top 120 videos from each platform (YouTube and Douyin) were screened. Video characteristics were extracted. Quality and reliability were assessed using the Global Quality Scale (GQS), modified DISCERN, JAMA Benchmark criteria, and the Content Completeness Score (CCS). Results 170 videos (70 from YouTube, 100 from Douyin) were included. YouTube videos were longer (118.50 vs. 78.50, Z = -3.79, p < 0.001), had higher CCS (8.00 vs. 6.00, Z = -3.50, p < 0.001) and mDISCERN scores (3.00 vs. 2.50, Z = -3.74, p < 0.001). YouTube videos were dominated by institutions (91.43%), followed by 8.57% healthcare professionals (HCPs), whereas nearly one third of contributors on Douyin were HCPs (31.00%). Associations between engagement and quality indicators were generally weak and varied across metrics, while engagement indicators on Douyin were strongly intercorrelated. Conclusions YouTube provided more comprehensive and reliable information, whereas Douyin content was shorter and less comprehensive. Both platforms showed inconsistent alignment between popularity and informational quality, with this disconnect appearing more pronounced on Douyin.
Objective Digital health literacy (DHL), as part of the social determinants of health, affects health outcomes. To address the problem of poor DHL in under-served populations, we designed a study to provide coaching for participants with poor DHL and hypertension to observe it’s impact on digital health literacy including self-efficacy. Methods The participants were recruited from the emergency department. We performed a non-randomized experimental cohort study in which coaches provided monthly sessions to participants on digital literacy and health management topics that could help to track their progress and change their behavior. Multiple instruments were administered longitudinally, including measures of self-efficacy in digital literacy, loneliness, nutrition, activity and blood pressure tracking. Results Participants demonstrated trends toward increased self-efficacy, digital communication, activity, and reduced loneliness. The qualitative results from the intervention combine to provide insights regarding the most important factors for successful DHL interventions. Conclusions This study demonstrates that a structured coaching intervention can enhance a patient’s self-confidence in managing their health overall, their chronic condition, and their use of digital tools.
This review systematically analyzed the existing evidence regarding the integration of telemedicine into nursing roles and practices for managing patients with systemic lupus erythematosus (SLE). The conduct and reporting of this scoping review followed the PRISMA-ScR checklist. A total of six studies were identified through searches conducted across five major databases, including PubMed and Web of Science, and were subsequently included in the analysis. The findings indicate that telemedicine significantly enhances access to care, reduces the incidence of missed appointments, and improves patient satisfaction and adherence to treatment protocols. Additionally, digital tools, such as mobile applications and remote coaching, have been shown to effectively improve patients’ quality of life and self-management capabilities. Within telemedicine frameworks, nurses undertake diverse roles, including those of educators, coordinators, and supporters. Nevertheless, some studies have reported increased hospitalization rates among patients in the telemedicine group, highlighting potential limitations in detecting subtle clinical changes. Overall, telemedicine represents a viable adjunct to traditional SLE care models, particularly for patients with limited resources or mobility constraints. However, the standardized implementation and long-term efficacy of telemedicine require further investigation through high-quality research.
Background Early detection of infectious disease outbreaks is essential for timely public health response. Artificial intelligence (AI) combined with digital traces from social media and search engines may strengthen epidemic early warning systems (EWS). Objective To map how AI-based EWS use social media and search engine signals for outbreak detection/forecasting, and to characterize whether studies integrate clinical and climate/environmental data. Methods Following PRISMA-ScR, we reviewed 38 studies (past five years) across six databases. We extracted study characteristics, data sources, target diseases, prediction tasks, validation practices, and performance metrics. For cross-tabulated evidence maps, each study was coded once using its primary data-source category and primary modeling approach to avoid double-counting. Results Deep learning and ensemble approaches were the most common methods. COVID-19 and influenza-like illness dominated the literature. Few studies combined social media and search engine signals. Clinical and climate/environmental data were rarely integrated. No study reported prospective, real-time evaluation. Geographic coverage was skewed toward high-income settings. Conclusion AI-based epidemic early warning systems using digital signals show promise for outbreak detection and forecasting. However, the evidence remains largely retrospective, fragmented, and geographically uneven. Future research should prioritize multi-stream data integration, equity-aware design, and prospective validation to support real-world global applicability.
Background At our hospital’s university, the data integration center (DIC) has the primary task of importing data from various systems, including unstructured medical reports and structured laboratory data into the openEHR data repository. As we participate in the German Medical Informatics Initiative (MII), we have an agreement to share common consent data for research purposes. The requirement for this is that the data must be available in the FHIR format. Objectives As a result, we have to transform our openEHR into FHIR data. Both formats are different, and simply programming a module to transform each openEHR template into a FHIR resource can be time-consuming and neither supportable nor updatable. Methods We have designed a method and developed an open-source software artifact that transforms an openEHR composition into a single FHIR resource or a bundle of resources, through the dynamic declaration of openEHR fields to API methods. The mappings can be updated at runtime, while the software artifact allows developers and end users to test and validate the mappings. Results We currently use our software, which is able to do robust transformations and has, until now, successfully stored and processed over 30 million FHIR bundles.
Background Chronic Obstructive Pulmonary Disease (COPD) is a globally burdensome chronic disease characterized by persistent respiratory symptoms and progressive airflow limitation, which severely impairs patients’ quality of life and prognosis. Effective self-management is crucial for COPD management, as targeted self-management interventions have been shown to improve disease outcomes. Digital health technologies (DHTs) offer an innovative approach to address the challenges of traditional self-management and provide sustainable support for patients’ long-term disease management. However, existing digital interventions for COPD commonly face several major challenges, including low user engagement, poor long-term adherence, and high participant withdrawal rates, highlighting an urgent need for optimized digital solutions. Objective This study aimed to develop a user-centered mobile application tailored to meet self-management needs of patients with COPD under the guidance of the Centre for eHealth and Wellbeing Research (CeHRes) Roadmap and persuasive system design (PSD) principles, as well as to evaluate its usability performance. Methods The development of the COPD Digital Health Self-Management System (COPD DHSS) was guided by the CeHRes Roadmap.The process commenced with a comprehensive literature review, semi-structured interviews involving fifteen COPD patients and five healthcare providers, and expert panel discussions (n=7) to identify challenges in COPD self-management and define the key values for the DHTs into self-management protocols. Based on these key values, the research team identified PSD features. These PSD features were integrated with COPD self-management evidence synthesis and were utilized to inform the design of the COPD DHSS prototype. A mixed-methods evaluation was employed: Sixteen COPD patients were recruited to test the COPD DHSS. After one month of system deployment, the System Usability Scale (SUS) was administered to evaluate usability metrics. Participants subsequently engaged in focus group discussions to provide feedback on user experience. Results A User-Centered Mobile Application for COPD was successfully developed through a structured process. The system integrated patient self-reported data with evidence-based best practices for COPD self-management, generating personalized recommendations to enhance patients’ self-management capabilities and improve disease prognosis and quality of life. Usability testing was conducted with 16 COPD patients, whose average age was 71.5 (±6.9) years. Of these participants, 14 (87.5%) maintained twice-weekly app usage throughout the evaluation period. The median SUS score was 76.25(66.88, 83.13),indicating an acceptable level of usability. User feedback consistently indicated that the COPD DHSS demonstrated favorable usability characteristics. Conclusions Integrating the CeHRes Roadmap with PSD principles, this study successfully developed a usable, user-centered mobile application tailored to the self-management needs of older patients with COPD. It also demonstrates how a structured, theory-driven approach can translate patient and stakeholder insights into a practical digital tool that is both acceptable and usable for this target population. It can serve as a paradigm for applying mobile health technology in chronic disease self-management.
Purpose To assess the accuracy, comprehensiveness, and reliability of Large Language Model chatbots in answering frequently asked questions about refractive errors. Methods Forty-four questions about refractive errors were posed to four chatbots, including Copilot, Perplexity, Gemini, and ChatGPT. Responses to each question were independently evaluated by three experts using a three-point accuracy scale. The readability of the chatbots’ responses was evaluated using several indices. Similarity was assessed using Sentence-Bidirectional Encoder Representations from Transformers (SBERT). Inter-rater agreement among the graders was evaluated using the Gwet Agreement Coefficient 1 (AC1) statistic. Results The overall agreement among the graders for all chatbot responses was almost perfect (Gwet AC1: 0.87). All chatbots received scores above 85% in every category, and there was no significant difference in chatbot accuracy scores ( P= 0.168 ). The highest mean comprehensiveness score was observed for Perplexity (8.13 ± 0.95, P = 0.028). The similarity scores of the chatbots were very close to each other. All readability scores showed significant differences ( P <0.001) between the chatbots. Conclusion All four chatbots showed comparable accuracy in answering questions about refractive errors. Readability levels of all chatbots exceeded public health recommended thresholds; however, ChatGPT produced relatively more accessible output.
Background Inflammatory Bowel Disease (IBD) is a chronic condition with increasing global prevalence. Short-video platforms like TikTok and Bilibili are popular health information sources, but their IBD-related content quality remains uncertain. Objective To assess the quality and reliability of IBD-related videos on TikTok and Bilibili. Methods We analyzed the top 100 IBD-related videos from each platform (200 total). Quality was evaluated using Global Quality Scale (GQS), modified DISCERN (mDISCERN), and JAMA benchmarks. Engagement metrics and quality scores were correlated. Results TikTok videos had higher engagement (P < .001) and better median scores (GQS: 3, mDISCERN: 3, JAMA: 3) than Bilibili (GQS: 3, mDISCERN: 3, JAMA: 3; P < .05). Gastroenterologist-created videos scored highest. GQS correlated positively with engagement (r = 0.16–0.26, P < .05), while mDISCERN correlated only with likes, shares, and saves (r = 0.16–0.18, P < .05). JAMA scores negatively correlated with duration (r = −0.24, P < .001). Conclusions IBD-related short videos showed moderate quality, with TikTok outperforming Bilibili. Gastroenterologist-produced content was most reliable. Viewers should critically evaluate such health information to avoid misinformation.
Objective This study aimed to identify characteristics of adverse events following immunization (AEFI) reporting systems and vaccine adverse events reporting systems (VAERS) including technical platforms, user groups, data elements, functional and non-functional requirements. Methods In this scoping review, various databases were searched from 1 st January 2015 to 31 st December 2024, and all types of studies that explained AEFI reporting system/VAERS characteristics were considered. The findings were reported descriptively. Results Different technical platforms including web-based, mobile-based, or hybrid platforms were used by multiple user groups. Data elements included personal, clinical, vaccination, and adverse events data. The functional requirements included recording vaccination and adverse events data as well as generating reports. Non-functional requirements were related to system security, data privacy, etc. Conclusion This review presented a set of characteristics that has been considered for different AEFI reporting systems and VAERS. The results can be used for designing more comprehensive AEFI reporting systems in different countries. These features along with new digital technologies and analytical tools including artificial intelligence offer more potential to enhance efficiency and effectiveness. Future research should focus on AI-driven methodologies, including natural language processing, machine learning techniques, and predictive analytics, while addressing ethical, regulatory, and practical challenges.
Objectives Patient scheduling is a vital yet complex task that strongly influences patient satisfaction and optimizes healthcare efficiency. Recent studies have emphasized the importance of devising innovative approaches and developing new scheduling frameworks. Therefore, this study establishes a dedicated generative artificial intelligence (GenAI) system for patient scheduling. Methods The proposed system first imports scheduling data to formulate the default patient scheduling problem. Subsequently, users enter their scheduling requirements using natural language via the system interface, which are parsed using a deep neural network to establish the corresponding extended three-field notations. A customized genetic algorithm is automatically generated to solve the customized patient scheduling problem. Results The dedicated GenAI system was applied to a real-world case obtained from the literature, involving 12 anesthesiologists, surgeons, and anesthesia resuscitation doctors; 12 operating rooms; and 15 patients undergoing three types of surgeries, each consisting of three operations. The experimental results reveal that the difference in the optimal fitness achieved using this system and branch-and-bound was less than 1% on average, demonstrating that the proposed methodology is effective. In addition, the most complex customized patient scheduling problem could be automatically modeled and solved in 20 s. Furthermore, the scheduling performance achieved using this system was significantly higher ( α = 0.05) than those achieved using two current practices. Moreover, customized patient scheduling problems are often substantially more complex than problems addressed using traditional methods reported in previous studies. Conclusions Applying this dedicated GenAI system improved the effectiveness of patient scheduling. This is expected to considerably enhance patient satisfaction and overall healthcare efficiency.
Diabetic Retinopathy (DR) is a medical condition in which high blood sugar levels damage the retina’s blood vessels. Existing Solutions for multi-class DR identification are computationally intensive and also suffer from low accuracy. There is an immense need for an automated, computationally efficient approach for monitoring DR progression in diabetic patients. The study proposed Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and Gaussian Naive Bayes (GNB) models for the classification of retinal images into five DR classes (No, Mild, Moderate, Proliferate, and Severe) using spatial features extracted through a Convolutional Neural Network (CNN), textural features extracted through a Grey Level Co-occurrence Matrix (GLCM), and hybrid features by combining these features. The CNN, EfficientNet, PyramidCNN, and Pyramid Vision Transformer (PVT) were also evaluated for the classification of DR stages. The results revealed that the RF model with hybrid features outperformed, with an accuracy of 98.00% and high performance across all evaluation metrics, with a 1.00% increase over existing approaches. The EfficientNet model also performs competitively with 97.00% accuracy. The ML models also emerged as computationally efficient in terms of training and inference time for deployment in low-resource clinical environments for automated monitoring of DR progression in diabetic patients.
Objective Preterm birth (PTB), defined as delivery before 37 weeks of gestation, is the leading cause of perinatal morbidity and mortality. Timely prediction of PTB is crucial for physicians to take preventive action. In this work, we propose an AI-based clinical decision support system to address this challenge. Methods We use an existing dataset containing demographic and clinical variables from 973 pregnant women and begin by examining it for potential data-level bias and representativeness issues. Upon identifying an outcome prevalence imbalance in one of the variables, we revised the dataset through a preprocessing adjustment. We then applied several Machine Learning (ML) algorithms to predict PTB as a binary outcome. Results During internal model evaluation we found that an optimized ensemble (voting) of Logistic Regression and XGBoost performed best, achieving an accuracy of 96% and a recall of 98%. We also compared the predictive performance using the original (biased) and revised (de-biased) datasets, observing statistically significant improvements in the revised dataset as confirmed by the Wilcoxon signed-rank test, with p-values less than 0.05 for Accuracy, Recall, and F1-score. Conclusion The results highlight the importance of data quality and dataset representativeness in developing reliable and trustworthy AI-based applications for PTB prediction.
BackgroundWeChat has become a central platform for health information seeking in China, particularly among young adults. Understanding the mechanisms underlying this behavior is crucial for improving digital health literacy and designing effective interventions.ObjectiveThis study applies the Comprehensive Model of Information Seeking (CMIS) to examine how WeChat use shapes users' perceptions of health information characteristics and utility, influencing health information-seeking behaviors among Chinese young adults.MethodsData were obtained from a cross-sectional online survey of 890 WeChat users aged 18-35 conducted in May 2024. Structural equation modeling (SEM) and the PROCESS macro were applied to test the hypothesized associations and mediating mechanisms involving perceived characteristics and utility.ResultsWeChat use, salience, and beliefs predicted perceived utility. Perceived characteristics and utility were positively associated with health information-seeking behaviors and mediated the relationship with WeChat use both in parallel and sequentially.ConclusionIntegrating platform-specific use into CMIS can promote health information seeking and inform digital health communication.
Purpose Patient-centered communication (PCC) is essential for quality healthcare. This study examined how PCC was affected by the shift to telehealth and COVID-19 safety measures. Methods An online survey was conducted (Sept–Oct 2020) among U.S. adults who had both in-person and telehealth visits after March 2020. PCC was assessed using the Communication Assessment Tool and analyzed with repeated measures ANOVA and multiple regression. Results Among 522 respondents (58.2% female; mean age 46.1), PCC scores were higher for in-person visits than telehealth (45% vs. 42%, P = .003). Lower PCC during telehealth was associated with younger age and caregiver presence. For in-person visits, mask use was linked to lower PCC among older adults. More frequent visits (≥3) correlated with higher PCC in both settings. Conclusions PCC was generally stronger during in-person visits. Pandemic-related changes may have negatively impacted communication, particularly among certain groups, highlighting areas for future improvement.
Objectives Short video platforms have become the main channel for the public to obtain information about cancer. In recent years, TikTok has gradually become an important source of health information for Chinese patients. This study aims to assess the content, quality and reliability of videos related to tongue cancer on the Tiktok platform. Methods From November 11 to 12, 2025, we searched for “tongue cancer” on TikTok platform. Based on the filtering criteria, collect information for the videos that meet the requirements. The global quality score (GQS) and the modified DISCERN (mDISCERN) were used to evaluate the quality and reliability of the videos. Finally, a spearman correlation analysis was conducted on all the indicators of the videos. Results This analysis included 79 videos. The duration of the videos is relatively short, with a median of 85.00 (IQR: 45.00, 127.50), but the interaction indicators are relatively high. The symptoms and treatment were mentioned more frequently, while the topics of epidemiology, etiology, diagnosis and prevention were mentioned less. The GQS score of the videos uploaded by the doctors was significantly higher, with a median of 4.00 (IQR: 3.00, 4.00), compared to videos uploaded by individual users 1.50 (IQR: 1.00, 2.00). The trend of the mDISCERN score was similar. The GQS and mDISCERN scores of videos uploaded by doctors are higher than those of individual uploaders. Conclusion TikTok videos on tongue cancer are short but highly engaging. Videos uploaded by doctors have higher quality and reliability than those by individual users. Symptoms and treatment are covered more than epidemiology, etiology, diagnosis, and prevention. Therefore, increasing the number of videos uploaded by doctors is crucial for improving the quality and reliability of videos on the TikTok platform.
