BACKGROUND:The effective translation of scientific insights into public health action remains a challenge, despite ongoing efforts to address obesity. OBJECTIVE:To apply an infodemiological framework and examine trends in public and scientific interest from 2004 to 2023 in obesity and two behavioral and sociocultural proxy topics, the Mediterranean diet (MedDiet) and body positivity. METHODS:Data sources included Google Trends, Wikipedia page views, and SCOPUS publications. Pearson's correlation assessed linear associations. Univariate and multivariate forecasting models projected future trends. RESULTS:Scientific interest in obesity moved in parallel with MedDiet (r = 0.96) and body positivity (r = 0.89), whereas public interest measured via Google Trends were inversely associated with these topics (r = -0.62; r = -0.61). While public interest in MedDiet and body positivity aligned with scientific interest, interest in obesity per se declined over time (r = -0.89). Univariate forecasting suggests public interest in obesity will remain low through 2030, while exploratory multivariate analysis indicates a possible rebound, coinciding with a projected decline in body positivity interest. CONCLUSION:These findings reveal a divergence between scientific attention and public engagement patterns with obesity over time. While declining searches may partly reflect evolving terminology, digital trend analysis offers valuable tools for identifying gaps between research focus and public attention to support responsive, evidence-informed public health communication.
BACKGROUND:Healthcare users can be assisted by a medicine or drug recommendation system (MRS) by understanding their needs and supporting informed decisions based on complex knowledge. The biggest challenge is analyzing users' sentiments, as human language characterizes user-generated data in many complex ways. METHODS:This paper proposes a sentiment analysis (SA)-based drug recommender system using an optimized deep learning (DL) model with an enhanced transformer-based feature representation mechanism. The proposed system comprises four stages: preprocessing, feature extraction, classification, and medicine recommendation. Firstly, drug reviews collected from public repositories are pre-processed to improve data quality. In the feature extraction stage, the pre-processed data are fed into the Multi-Head Attention Bidirectional Encoder Representations from Transformers (MHABERT) model to capture deep semantic and contextual features. The user sentiments are then classified using Golden Jackal Optimized Bidirectional Long Short-Term Memory (GOBLSTM). Finally, medicine recommendations are generated by comparing the similarities of drug reviews. RESULTS:The Kaggle medicine recommendation and Yelp health datasets are used to evaluate the proposed method. Experimental results demonstrate maximum accuracies of 98.43% and 98.30% on the Kaggle and Yelp datasets, respectively, outperforming existing approaches. CONCLUSION:The combination of SA and drug recommendation enhances patient outcomes, optimizes medicine selection, and reduces adverse reactions.
Continued use of Electronic Health Records (EHRs) remains a significant challenge despite widespread digitalization. Existing models often overlook key psychological and informational factors essential for continued engagement. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating digital health literacy, trust, and privacy concerns to better explain continued EHR use. A cross-sectional survey across 21 Turkish cities yielded 480 valid responses from users of the national EHR system (E-nabız). Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to identify usage intention predictors, and Necessary Condition Analysis (NCA) to determine must-have factors for continued use. UTAUT constructs were confirmed as relevant predictors of continued EHR use. Effort expectancy, facilitating conditions, and trust emerged as necessary conditions. Trust also reduced the impact of privacy concerns, which had only a weak negative effect. Social influence affected intentions but was not a necessary condition. Digital health literacy significantly predicted performance expectancy, effort expectancy, and facilitating conditions and proves critical to ongoing EHR usage. Educational interventions aimed at enhancing digital health literacy can significantly increase EHR continued use through multiple pathways.
OBJECTIVES:This study conducted an informatics system evaluation of two LLMs (GPT-4o and DeepSeek-V3) for patient education, combining clinician-rated quality with patient-perceived usability across thematically stratified queries. MATERIALS AND METHODS:In a blinded, within-subject design, 16 frequently asked questions about biologic therapies were categorized into three domains: treatment/drug selection, safety/adverse effects, and special conditions/daily life. Responses were standardized, generated without external retrieval, anonymized as A/B pairs. Thirty physicians assessed clinical appropriateness, scientific accuracy, comprehensiveness, while 60 patients rated readability, understandability, actionability, perceived adequacy, decision support, and trust on 5-point Likert scales. Analyses included paired t-tests, Holm/FDR corrections and two one-sided tests (TOST) to distinguish statistical non-difference from practical equivalence. RESULTS:Physicians rated GPT higher across all domains (p < .002), with largest gaps in safety/side effects and treatment/drug selection. Patients favored GPT for understandability, actionability, and decision support (p < .001), while readability, adequacy, trust, and reading time were statistically and clinically equivalent. CONCLUSION:Findings highlight the need for topic-aware governance: guideline-dense queries suited to retrieval-augmented generation and checklist compliance, and context-sensitive queries requiring uncertainty signaling and human oversight. This layered approach advances health informatics by defining where LLMs may substitute versus where they require verification, supporting safe and auditable integration into patient education.
To identify research topics in medical chatbots and analyze their temporal trends, geographic distributions, and journal preferences. Latent Dirichlet Allocation (LDA) topic modeling was applied to 9,650 publications (1986-2024), extracting eight core topics integrated with time-series analysis, geographic statistics, and journal associations. Eight topics were identified. Temporal trends revealed three phases: technology incubation (2000-2015), rapid breakthrough (2015-2020), and application consolidation (2020-2024). Geographically, the United States, China, and the United Kingdom dominated research output (46.0%). Journal analysis highlighted Journal of Medical Internet Research (JMIR) (7.1%), Journal of the American Medical Informatics Association (JAMIA) (5.4%), and IEEE Journal of Biomedical and Health Informatics (IEEE JBHI) (3.2%) as top contributors, with JMIR and JAMIA reinforcing clinical informatics and digital therapeutics. Research on medical chatbots needs to balance technical feasibility and clinical value. Future research should focus on three directions: developing validation frameworks for leveraging large language models in clinical applications (LLMs), establishing transnational data-sharing infrastructure, and creating ethical governance mechanisms that ensure responsible innovation while maintaining health equity.
PURPOSE:The U.S. Hospital Price Transparency mandate requires public disclosure of machine-readable files (MRFs), yet profound data heterogeneity hinders their utility for research and consumer use. This study evaluates a novel, multi-stage computational pipeline to systematically process diverse MRFs and enable robust price analysis. METHODS:The pipeline integrates a configurable parsing engine with an NLP module using Sentence-BERT embeddings and K-Means clustering for semantic standardization of procedure descriptions and CPT code alignment. It was applied to MRFs from five U.S. hospitals (Mayo Clinic, Johns Hopkins, Stanford, Jackson Memorial, and Mass General) for five elective procedures. RESULTS:The pipeline successfully processed 7,449 records, revealing substantial price variation across hospitals for semantically equivalent services. Predictive modeling using Lasso regression yielded an R^2 of 0.70 (RMSE = $808; MAE = $533). CONCLUSIONS:This work advances scalable, semi-automated MRF research methodology, transforming opaque pricing data into an analytically tractable form with implications for healthcare policy and consumer tools.
In today's world, where technology and digitalization impact every aspect of life, organizations prioritize adapting to these changes. This study aims to explore how digitalization influences organizational behaviors. The fact that individuals interact with technology constantly in their professional lives raises important questions in management science, offering valuable insights for both organizations and researchers. This research aims to determine the level of openness to change, technostress, and employee performance of healthcare employees and to investigate the moderator role of demographic variables. Data was collected with the participation of 362 healthcare employees from a university hospital. As a result of the study, the moderating role of age, marital status, and professional experience between openness to change and technostress; the moderating role of employee type between technostress and employee performance; the moderating role of age, education level, and professional experience between openness to change and employee performance were determined. Suggestions to researchers and hospital managers regarding the results obtained were expressed.
This cross-sectional study (November - December 2024) examined whether health literacy predicts adults' attitudes toward substance use in Kahramanmaraş, Türkiye. Data were collected from 400 participants (≥18 years) using convenience sampling. Participants completed a Personal Information Form, the Health Literacy Scale, and the Substance Abuse Attitude Scale, which assesses positive/negative attitudes (not substance use behavior or addiction severity). Analyses included descriptive statistics, t-tests/ANOVA, Pearson correlation, and linear regression (IBM SPSS 26). Health literacy was negatively associated with substance use attitudes (r = -0.304, p < .001), indicating that higher levels of health literacy relate to more negative attitudes toward substance use. Regression results confirmed health literacy as a significant predictor (β = -0.304, p < .001). Among health literacy dimensions, information evaluation showed the strongest negative association with substance use attitudes, whereas information comprehension was not significant. These findings suggest that competencies related to accessing, appraising, and applying health information may shape attitudes toward substance use and may be leveraged in educational and community interventions to strengthen risk perception and promote more cautious, informed attitudes.
The standardization of Electronic Health Records (EHRs) is a nationally significant priority in India, given the scale and diversity of its healthcare system. To promote uniform clinical documentation, the Ministry of Health and Family Welfare (MoHFW), Government of India, introduced the Electronic Health Record Minimum Data Set (EHRMDS). However, the structural alignment of existing Open-Source Electronic Health Record Systems (OS-EHRS) with this mandated dataset remains insufficiently examined. This study presents a field-level, metadata-based quantitative assessment of alignment between EHRMDS and OS-EHRS, focusing on metadata presence rather than semantic, workflow, or technical interoperability. A structured crosswalk methodology combining syntactic similarity and ontology-supported semantic evaluation was applied to compare EHRMDS elements with those exposed by selected OS-EHRS data models. A closeness percentage metric is introduced as a heuristic benchmarking measure of relative metadata alignment. Results show that OpenEMR has the highest alignment (73.81%), while OpenClinic has the lowest (33.33%). The study identifies 47 clinically relevant metadata elements present across OS-EHRS but absent from EHRMDS, consolidated into an extended schema (EHRMDS-ext.) proposed as an enhancement rather than a replacement. The primary novelty of this work lies in introducing a quantitative benchmarking framework for evaluating EHR metadata alignment in Indian context, moving beyond prior descriptive or qualitative analyses. The findings are intended to support standard development and future interoperability research.
Stomach abnormalities pose significant health concerns, ranging from minor digestive issues to severe conditions. The emergence of deep learning methods offers a promising solution to this problem. However, due to the risk of non-optimal hyperparameters affects its performance. To address this concern, this research proposes a Healthcare Internet of Things (IoT)-Based Stomach Abnormality Detection through Iris Image (HIoT-SADII). The SADI process begins with data collection through an IoT architecture. The data are preprocessed using a Gaussian filter. A Modified Mean with Niblacks' Threshold-based Deep Joint Segmentation (MMNT-DJS) is suggested for segmentation that separates the region of interest from background. Subsequently, features such shape features, statistical features, and Modified Local Gabor Transitional Pattern (MLGTP) are extracted from segmented images. Lastly, a hybrid deep learning approach that incorporates Bi-Directional Long Short-Term Memory (Bi-LSTM) and Block-Wise Modified Dropout in Convolutional Neural Network (BMDCNN) is proposed for detection. The proposed model is trained with extracted features to determine final outcome as normal or abnormal based on averaging both models' outcomes. Experimental findings demonstrate that the suggested model achieves a detection accuracy and F-measure of 0.950 and 0.927, respectively.
Voriconazole (VCZ) is a first-line antifungal agent for treating invasive fungal infections. However, due to its narrow therapeutic index and high pharmacokinetic variability, standard dosing can be subtherapeutic or toxic. Therapeutic Drug Monitoring (TDM) is recommended to optimize VCZ therapy. To enhance TDM based dosing, we implemented a Clinical Decision Support System (CDSS) integrated into our electronic health record (EHR) that generates alerts at VCZ prescribing and dispensing points. These alerts display the most recent TDM result and interpretation and include an option to request a new TDM order. The study comprised three phases: pre-implementation, transitional, and post-implementation. We reviewed VCZ orders extracted from the EHR, analyzing a total of 2199 orders with recent TDM results. Physician compliance with TDM-guided dosing notably increased from 52.2% in the pre-implementation period to 94.7% post-implementation (p < .05). Pharmacist compliance also rose from 90.5% to 95.6% during the same timeframe (p < .05). The CDSS also supported more patients reaching therapeutic levels and reduced prescriptions for extreme doses. Integrating TDM recommendations into a CDSS significantly improved adherence by physicians and pharmacists to TDM-guided VCZ dosing. This approach highlights the value of health information systems in optimizing personalized antifungal therapy and stewardship.
Sedentary nature of office work contributes to a range of physical health issues, including obesity, which can result from prolonged inactivity, and cardiovascular diseases, linked to heightened risk factors associated with a lack of movement. Furthermore, extended periods of sitting can lead to musculoskeletal disorders, causing discomfort and injuries related to poor posture and ergonomics. Collectively, these factors underscore the profound negative impact of sedentary behavior in the workplace on overall well-being. To address these issues, this study proposes a multi-layered digital twin (DT) system for remote healthcare monitoring in a smart office setting. The suggested approach thoroughly investigates various office-related actions in a DT environment, rating their criticality to estimate potential health consequences. By mining temporal instances of these events, a Physiological Risk Index (PRI) is derived, supporting a predictive healthcare framework capable of generating automated alerts during health emergencies. Furthermore, the time-based data module is designed to assist healthcare practitioners in making better decisions by providing precise information about significant occurrences. The system's usability and efficacy are demonstrated by testing it against two challenging datasets obtained from internet repositories. The findings indicate that the proposed approach is both efficient and effective in creating a comprehensive medical system.
Local hyperthermia is a noninvasive treatment that applies controlled heating to damage tumor cells, limit proliferation, and enhance therapy. This study models temperature distribution in tumor-bearing tissue to quantify heat-induced damage and examines its effects on tumor metabolism and glucose diffusion. Using Pennes' Bioheat equation, temperature variations in finite tissue are computed and integrated into a modified tumor growth model that includes thermal damage. The bioheat equation is solved with the finite difference method, while tumor progression under temperatures from 39°C to 43°C is simulated using Euler's method. Numerical results are validated against existing tumor and glucose concentration models. The findings show that higher hyperthermia temperatures increase thermal damage, reduce glucose availability, and significantly inhibit tumor growth, leading to shrinkage over time. The study highlights the importance of incorporating thermal effects into tumor-growth models and provides a computational framework for improving hyperthermia-based cancer therapy.
Parkinson's disease (PD) is a degenerative neurological condition defined by a wide range of motor and non-motor symptoms that can affect function to varying degrees. One of the hallmarks of PD is postural instability (PI), along with rest tremor, stiffness, and bradykinesia. PI is a primary cause of disability and decreased quality of life in people with PD. This paper presents a review of the available literature to understand PD, its pathophysiology, diagnosis, various risk factors and its pharmacological and non-pharmacological management. This review offers an overview of physiotherapeutic methods and recent advances in PD care, highlighting the growing role of digital health technologies, artificial intelligence (AI), and innovative rehabilitation tools in improving diagnosis, treatment, and patient outcomes. Diagnosis of PD relies on clinical signs observed during history-taking and physical examination, along with motor fluctuations and response to dopamine therapies over time. Identifying risk factors specific to PI helps in diagnosis and prevention by differentiating between modifiable and non-modifiable causes. The management of PD includes both pharmacological and non-pharmacological approaches. Physiotherapy is a common therapeutic approach for managing symptoms in patients with PD. Management of PD has seen various improvements with the emerging technologies, with ongoing studies driving further advancements.
Rhabdomyolysis is a severe condition with high morbidity and mortality, driven by complications like acute kidney injury. Early risk stratification remains challenging as traditional scores fail to capture complex data patterns. This study lays the foundation for an explainable AI (XAI)-based clinical decision support system (CDSS) by developing a machine learning model to predict the composite outcome of renal replacement therapy or 90-day mortality. Using routinely available admission data from 1031 adults, we applied multivariate imputation, Boruta feature selection, and ADASYN for class imbalance. The CatBoost model achieved the highest discrimination (AUC = 0.942, 95% CI: 0.904-0.980), accuracy (0.913), and maintained good calibration (Brier score = 0.080). Shapley additive explanations (SHAP) identified creatinine, troponin T, and albumin as key predictors, validating clinical plausibility and enabling instance-level explanations for CDSS deployment. Decision-curve analysis confirmed superior net benefit against treat-all or treat-none strategies across clinically relevant thresholds. We propose a framework for integrating this interpretable model into electronic health records to provide real-time risk scores at the point of care. Calibration drift in older adults highlights the need for age-specific refinement, underscoring the value of transparent, evaluable AI in clinical informatics.
INTRODUCTION:The increasing advancement in Information and Communication Technology (ICT) has led to the adoption of Health Information Systems (HIS) in healthcare settings to enhance service delivery. This study evaluated the effect of HIS implementation on the timeliness of medical claims submission within the National Catholic Health Service (NCHS) in Ghana. METHODS:Using longitudinal data from 2010 to 2019, the study compared monthly claims submission times across facilities using both paper-based and electronic HIS. The number of days taken to submit claims each month was analyzed using a segmented Interrupted Time-Series approach, employing the Prais-Winsten method. This allowed comparison of claim submission times before and after HIS adoption. A meta-analysis was conducted to determine the overall impact across facilities. RESULTS:Facilities using HIS submitted claims significantly faster than those using paper-based systems, with average submission times of 35.11 days versus 56.51 days, respectively (p < 0.0001). HIS implementation led to an immediate 5.84-day reduction in average submission time. Findings showed that over 75% of the facilities achieved timely submission of claims. CONCLUSION:In conclusion, HIS significantly improves the timeliness of medical claims submission. Scaling up HIS use for claims processing can enhance efficiency and promote prompt reimbursements from health insurers.
OBJECTIVE:Investigating whether there is a difference in health literacy and healthy lifestyle behaviors between individuals who have and have not had infection after the COVID-19 outbreak, and also to examine the relationship between health literacy and healthy lifestyle behaviors. METHODS:Participants were assessed with the European Health Literacy Survey (HLS-EU-Q) and the Healthy Lifestyle Profile-II (HPLP-II) scales online via Google Forms using various social media platforms. Also, the individuals' habits of obtaining health-related knowledge and general health conditions, health-related recourse sources, and the frequency of exercise were also recorded and compared. RESULTS:Three hundred and sixty-seven individuals (64.4% female) participated in the study and analysis of the HLS-EU-Q and HPLP-II Scale results of all participants revealed a positive correlation between two scales (p = .009, r = 0.137). However, no significant difference was found between participants with (Group I, n = 183) and without (Group II, n = 184) COVID-19 infection in terms of HLS-EU-Q and HPLP-II scales (p > .05). CONCLUSION:It was concluded that the health literacy levels and healthy lifestyle behaviors of individuals who had and did not have COVID-19 infection were similar. However, there was a weak but significant correlation between the level of health literacy and the adoption of healthy lifestyle behaviors.
Telemedicine holds significant potential to transform healthcare delivery in rural Nigerian communities; however, its adoption remains constrained by various barriers. This study explores patient-level barriers, including technological access, health literacy, and cultural attitudes, as well as provider-level challenges such as training, infrastructure, and organizational support. Additionally, it investigates the role of socio-economic factors and community dynamics in shaping perceptions of telemedicine. Using qualitative interviews with 39 participants (24 patients and 15 providers), the study identifies key obstacles, including the high cost of devices and internet data, unreliable connectivity, low health literacy, and cultural stigma surrounding remote healthcare consultations. The findings align with Rogers' Diffusion of Innovation theory, revealing that barriers related to relative advantage, complexity, and compatibility significantly affect the adoption process. Strategies to mitigate these barriers include subsidizing technology costs, implementing digital literacy programs, providing culturally tailored telemedicine models, and ensuring better infrastructure and technical support for healthcare providers. Recommendations also emphasize the importance of community-centered engagement to enhance acceptance and usage. This study highlights the need for systemic interventions and participatory approaches to design context-specific solutions that address both patient and provider challenges. Future research should focus on participatory action research to develop and implement effective interventions for telemedicine adoption in rural areas.
Lung nodules (LNs) detection using computer tomography (CT) images is essential to reduce the mortality of lung cancer (LC). The complex three-dimensional structure of lung CT data and the variation in the forms and appearances of LNs make the accurate identification of pulmonary nodules still extremely challenging. Although deep learning (DL) methods outperform handcrafted approaches, several challenges remain to be solved. Detecting malignant regions alone is insufficient for clinical decision-making; segmentation by severity and grading analysis are necessary to reduce false positives. Training DL models also requires many datasets, which is difficult in the medical domain due to ethical concerns, limited expert annotations, and the scarcity of disease-specific images. Moreover, insufficient data and class imbalance often lead to overfitting and a reduction in performance. To address these limitations, this study proposes a hybrid pre-trained architecture for more accurate automated pulmonary nodule segmentation and classification. The research comprises 3 phases: preprocessing, segmentation, and classification. Initially, the CT lung images are gathered from the openly available LUNA16 dataset. Then, preprocessing is performed on the collected data by noise filtering using a guided filter and data augmentation to balance the dataset. Doing so improves the data quality for learning and classification processes and prevents the model from biased outcomes. Afterward, the Spatial Pyramid Pooling centered U-shaped network (SPPUNet) is employed to segment the lung regions, enabling the classifier to easily identify and analyze nodules, lesions, and other abnormalities. Finally, the classification is performed using the Global Context Attention integrated InceptionV3 (GCAINCPV3) network, which enables medical professionals to determine the nature of the nodule and provide the most appropriate treatment plan for patients. The outcomes demonstrated that the proposed system outperforms existing systems, achieving an accuracy of 99.23%.
