Informatics for health & social care最新文献

Bridging the gap: an infodemiological analysis of public and scientific interest in obesity and digital proxies of lifestyle-related discourse. 弥合差距：公众和科学界对肥胖的兴趣以及生活方式相关话语的数字代理的信息流行病学分析。

IF 2.4

Informatics for health & social care Pub Date : 2026-07-16 DOI: 10.1080/17538157.2026.2695040

Efstathios Kaloudis, Vasiliki Bountziouka

{"title":"Bridging the gap: an infodemiological analysis of public and scientific interest in obesity and digital proxies of lifestyle-related discourse.","authors":"Efstathios Kaloudis, Vasiliki Bountziouka","doi":"10.1080/17538157.2026.2695040","DOIUrl":"https://doi.org/10.1080/17538157.2026.2695040","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-12"},"PeriodicalIF":2.4,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148475062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized deep learning-based drug recommendation system using a sentiment analysis and transformer models for improved predictions. 一个优化的基于深度学习的药物推荐系统，使用情感分析和变压器模型来改进预测。

IF 2.4

Informatics for health & social care Pub Date : 2026-07-16 DOI: 10.1080/17538157.2026.2689326

D Deepa, T Dhiliphan Rajkumar, P Nagaraj

{"title":"An optimized deep learning-based drug recommendation system using a sentiment analysis and transformer models for improved predictions.","authors":"D Deepa, T Dhiliphan Rajkumar, P Nagaraj","doi":"10.1080/17538157.2026.2689326","DOIUrl":"https://doi.org/10.1080/17538157.2026.2689326","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-25"},"PeriodicalIF":2.4,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital health literacy as a driver of continued electronic health record use: insights from necessity and sufficiency analyses. 数字健康素养作为电子健康记录持续使用的驱动因素：必要性和充分性分析的见解。

IF 2.4

Informatics for health & social care Pub Date : 2026-05-26 DOI: 10.1080/17538157.2026.2676934

Gökhan Aydin, Seda Kumru, Halil Balin

{"title":"Digital health literacy as a driver of continued electronic health record use: insights from necessity and sufficiency analyses.","authors":"Gökhan Aydin, Seda Kumru, Halil Balin","doi":"10.1080/17538157.2026.2676934","DOIUrl":"https://doi.org/10.1080/17538157.2026.2676934","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-21"},"PeriodicalIF":2.4,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148038816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction. 修正。

IF 2.4

Informatics for health & social care Pub Date : 2026-04-25 DOI: 10.1080/17538157.2026.2664831

引用次数: 0

Dual perspectives on large language models in rheumatology: physician-rated quality and patient-centered usability of GPT-4o versus DeepSeek-V3. 风湿病学大型语言模型的双重视角：gpt - 40与DeepSeek-V3的医生评价质量和以患者为中心的可用性

IF 2.4

Informatics for health & social care Pub Date : 2026-04-16 DOI: 10.1080/17538157.2026.2654150

Bulent Akyuz, Ilhan Sezer, Ali Nail Demir, Cahit Kacar

{"title":"Dual perspectives on large language models in rheumatology: physician-rated quality and patient-centered usability of GPT-4o versus DeepSeek-V3.","authors":"Bulent Akyuz, Ilhan Sezer, Ali Nail Demir, Cahit Kacar","doi":"10.1080/17538157.2026.2654150","DOIUrl":"https://doi.org/10.1080/17538157.2026.2654150","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-11"},"PeriodicalIF":2.4,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147694684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of artificial intelligence chatbots in the medical field: latent dirichlet allocation analyses. 人工智能聊天机器人在医疗领域的应用：潜在狄利克雷分配分析。

IF 2.4

Informatics for health & social care Pub Date : 2026-04-13 DOI: 10.1080/17538157.2026.2655820

Mingyue Ni, Yunxia Jiang, Mingrun Li, Yunping Zhou

{"title":"Application of artificial intelligence chatbots in the medical field: latent dirichlet allocation analyses.","authors":"Mingyue Ni, Yunxia Jiang, Mingrun Li, Yunping Zhou","doi":"10.1080/17538157.2026.2655820","DOIUrl":"https://doi.org/10.1080/17538157.2026.2655820","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-16"},"PeriodicalIF":2.4,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147680123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Examining the relationship between technostress, openness to change and performance in a smart health ecosystem: a research in a university hospital. 研究智能健康生态系统中技术压力、对变化的开放程度和绩效之间的关系：一项在大学医院进行的研究。

IF 2.4

Informatics for health & social care Pub Date : 2026-03-22 DOI: 10.1080/17538157.2026.2638233

Özlem Gedik, Edibe Asuman Atilla

{"title":"Examining the relationship between technostress, openness to change and performance in a smart health ecosystem: a research in a university hospital.","authors":"Özlem Gedik, Edibe Asuman Atilla","doi":"10.1080/17538157.2026.2638233","DOIUrl":"https://doi.org/10.1080/17538157.2026.2638233","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-26"},"PeriodicalIF":2.4,"publicationDate":"2026-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147501493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A quantitative approach to evaluating open-source EHR systems for Indian healthcare: a metadata perspective. 评估印度医疗保健开源电子病历系统的定量方法：元数据视角。

IF 2.4

Informatics for health & social care Pub Date : 2026-03-16 DOI: 10.1080/17538157.2026.2641129

Biswanath Dutta, Debanjali Bain

{"title":"A quantitative approach to evaluating open-source EHR systems for Indian healthcare: a metadata perspective.","authors":"Biswanath Dutta, Debanjali Bain","doi":"10.1080/17538157.2026.2641129","DOIUrl":"https://doi.org/10.1080/17538157.2026.2641129","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-28"},"PeriodicalIF":2.4,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoT-based Stomach abnormality detection via hybrid MDCNN-Bi-LSTM architecture with statistical and texture features. 基于统计特征和纹理特征的混合MDCNN-Bi-LSTM结构胃异常物联网检测

IF 2.4

Informatics for health & social care Pub Date : 2026-03-12 DOI: 10.1080/17538157.2026.2632846

M Sandhiya, A S Aneetha

{"title":"IoT-based Stomach abnormality detection via hybrid MDCNN-Bi-LSTM architecture with statistical and texture features.","authors":"M Sandhiya, A S Aneetha","doi":"10.1080/17538157.2026.2632846","DOIUrl":"https://doi.org/10.1080/17538157.2026.2632846","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-28"},"PeriodicalIF":2.4,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147438838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The effect of health literacy on attitudes toward substance use. 健康素养对物质使用态度的影响。

IF 2.4

Informatics for health & social care Pub Date : 2026-03-09 DOI: 10.1080/17538157.2026.2640101

Ali Göde, Ramazan KiraÇ, Ali Alu

{"title":"The effect of health literacy on attitudes toward substance use.","authors":"Ali Göde, Ramazan KiraÇ, Ali Alu","doi":"10.1080/17538157.2026.2640101","DOIUrl":"https://doi.org/10.1080/17538157.2026.2640101","url":null,"abstract":"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.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-10"},"PeriodicalIF":2.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0