Health Informatics Journal最新文献

Navigating through regulatory frameworks for digital therapeutics and biomarkers. 浏览数字疗法和生物标志物的监管框架。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-10-09 DOI: 10.1177/14604582251387656

Cinja Koller, Marc Blanchard, Thomas Hügle

{"title":"Navigating through regulatory frameworks for digital therapeutics and biomarkers.","authors":"Cinja Koller, Marc Blanchard, Thomas Hügle","doi":"10.1177/14604582251387656","DOIUrl":"https://doi.org/10.1177/14604582251387656","url":null,"abstract":"Background: Digital health technologies are often subject to regulatory requirements. Regulatory auditing processes are complex but necessary to guarantee quality, efficacy and safety of patients. Evolvements such as digitalized clinical trials, and digital biomarkers require a constant adaption of regulatory frameworks. Objective: This review aims to provide an overview on current regulations and standards for digital therapeutics and digital biomarkers, from technical development to market access. Methods: We conducted an unstructured literature review to identify the relevant guidelines, policies and standards for software based digital therapeutics and digital biomarkers. Results: The principal regulations governing software as a medical device are outlined in Chapter 21 of the Code of Federal Regulations by the US Food and Drug Administration, as well as the European Medical Device Regulation 2017/745. Regulatory pathways, such as the DiGA, are in the process of development, particularly for digital therapeutics, which fall within the purview of software as a medical device. Qualification of (digital) biomarkers is typically voluntary but can play a significant role in the development and approval of digital therapeutics. Conclusions: Fragmented, lacking and diverse regulations around digital biomarkers and digital therapeutics highlight the urge to harmonize and foster regulatory frameworks on an international level.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251387656"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling emotional contagion in COVID-19 misinformation: Computational analysis for public health crisis surveillance. 揭示COVID-19错误信息中的情绪传染：公共卫生危机监测的计算分析。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-10-03 DOI: 10.1177/14604582251381175

Qiuyi Chen, Qian Liu

{"title":"Unveiling emotional contagion in COVID-19 misinformation: Computational analysis for public health crisis surveillance.","authors":"Qiuyi Chen, Qian Liu","doi":"10.1177/14604582251381175","DOIUrl":"https://doi.org/10.1177/14604582251381175","url":null,"abstract":"Objectives: During the early phase of the COVID-19 outbreak, misinformation spread rapidly, hindering effective health communication and fueling xenophobic violence. The politicization of health issues, along with the manipulation by social bots and astroturfing accounts, posed significant challenges. This study aims to investigate how misinformation spreads through social media, involving malicious actors like trolls and bots, and explores emotional contagion during public health crises. Methods: Using a computational methodology that combines semantic modeling, social network analysis, bot identification, emotion analysis, and time series analysis, the study analyzed over 700,000 tweets from February to July 2020. Results: The findings reveal that inauthentic actors amplified negative emotions, particularly among news and political actors, while positive emotions were less prominent. Astroturfing accounts acted as key nodes, perpetuating negative emotional contagion. Conclusion: This study provides a framework for monitoring emotional responses in public health crises, with findings applicable beyond COVID-19 to other public health emergencies.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251381175"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficiency of Electronic Health Record users in the General Health System of Cyprus. 塞浦路斯一般卫生系统中电子健康记录用户的效率。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-07-29 DOI: 10.1177/14604582251353418

Periklis Rompolas, Panicos Masouras, Sotiris Avgousti, Andreas Charalambous

引用次数: 0

The ontology framework and challenges of smart healthcare system transformation using natural language processing and latent Dirichlet allocation. 使用自然语言处理和潜在狄利克雷分配的智能医疗系统转换的本体框架和挑战。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-09-18 DOI: 10.1177/14604582251381280

Shuyan Zhao, Hua Zhong, Beibei Ge, Xiaojing Zhao

{"title":"The ontology framework and challenges of smart healthcare system transformation using natural language processing and latent Dirichlet allocation.","authors":"Shuyan Zhao, Hua Zhong, Beibei Ge, Xiaojing Zhao","doi":"10.1177/14604582251381280","DOIUrl":"https://doi.org/10.1177/14604582251381280","url":null,"abstract":"Objectives: This article aims to develop the ontology framework of smart healthcare system and identify the challenges to construct the smart healthcare system. The ontology framework provides both academics and practitioners a reference to understand and transform the healthcare system. Methods: The publications in the area of the smart healthcare system were extracted from WOS core collection database. Latent Dirichlet Allocation (LDA) was employed to find subjects of publications. Natural language processing (NLP) was used to extract entities from topics explored based on LDA. The developed ontology framework of the smart healthcare system was then presented in OWL format using Protégé software. The challenges in transforming towards the smart healthcare system were identified based on the developed ontology framework. Results: Fourteen challenges are identified through the ontology framework developed by NLP and LDA, including poor system interoperability, data security and data sharing, low adoption of data standards and data scalability, etc. These challenges provide a reference for future healthcare workers to deal with possible risks and difficulties. Conclusions: The ontology framework developed by NLP and LDA provides a unified description and structured knowledge in smart healthcare system, and provides valuable working methods and management basis for scholars and medical workers.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 3","pages":"14604582251381280"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating embodied cognition with the UTAUT model to investigate factors influencing the adoption of home-based health monitoring systems. 结合具身认知与UTAUT模型探讨家庭健康监测系统采用的影响因素。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-07-31 DOI: 10.1177/14604582251363546

Zhen Zhao, Kaifeng Liu, She Lyu, Stephen Jia Wang, Yun Hei Chak, Hailiang Wang

{"title":"Integrating embodied cognition with the UTAUT model to investigate factors influencing the adoption of home-based health monitoring systems.","authors":"Zhen Zhao, Kaifeng Liu, She Lyu, Stephen Jia Wang, Yun Hei Chak, Hailiang Wang","doi":"10.1177/14604582251363546","DOIUrl":"https://doi.org/10.1177/14604582251363546","url":null,"abstract":"Objective: Factors influencing users' adoption of the home-based health monitoring system (HHMS) were examined by integrating embodied cognition with the unified theory of acceptance and use of technology (UTAUT) model. Methods: Data from 459 survey respondents were analyzed using partial least squares structural equation modeling (PLS-SEM). Results: The model explained 59.7% of the variance in behavioral intention to use the HHMS (typical range: 40%-60%). Perceived contextual adaptation, perceived sensorimotor feedback, and perceived body awareness significantly influenced behavioral intention. Perceived body awareness (i.e., an individual's ability to perceive and interpret bodily signals) was identified as a crucial factor affecting performance expectancy, effort expectancy, facilitating conditions, and social influence. Conclusions: The integration of embodied cognition with the UTAUT model contributes to theoretical advancements and demonstrates the importance of body awareness in users' adoption of the HHMS, providing practical guidance for the effective design of HHMS.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 3","pages":"14604582251363546"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A mobile application for home-based care of indwelling medical devices: Protocol for development and pilot implementation based on the self-efficacy framework and the analysis, design, development, implementation, evaluation (ADDIE) model. 嵌入式医疗器械居家护理移动应用：基于自我效能框架和分析、设计、开发、实施、评估（ADDIE）模型的开发与试点方案

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-09-16 DOI: 10.1177/14604582251381236

Dakyung Lee, Anna Lee

{"title":"A mobile application for home-based care of indwelling medical devices: Protocol for development and pilot implementation based on the self-efficacy framework and the analysis, design, development, implementation, evaluation (ADDIE) model.","authors":"Dakyung Lee, Anna Lee","doi":"10.1177/14604582251381236","DOIUrl":"10.1177/14604582251381236","url":null,"abstract":"Objective: This study aims to present the development of a mobile application incorporating accessibility, communication features, and repeated learning opportunities to support patients and caregivers in managing indwelling medical devices at home. Methods: The application development follows the Analysis, Design, Development, Implementation, Evaluation model. This protocol includes a literature review, application structure and prototype development, and pilot study design. The content is grounded in Bandura's self-efficacy theory and includes behavior change techniques to increase self-efficacy in patients and caregivers to manage indwelling medical devices at home. Results: The literature review in the analysis phase identified the need for a personalized interface, alarm function, and a community. The design and development phases produced a comprehensive feature list to guide the intervention protocol, along with the creation of a prototype. A pilot study will be conducted to evaluate the feasibility and potential effectiveness of the mobile application, as well as to refine it based on the feedback received. Conclusion: We expect that this application will reduce the burden on patients and caregivers providing home-based care, improve patient health, and reduce the waste of medical resources such as unnecessary hospitalizations.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 3","pages":"14604582251381236"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ChatGPT's progress over time: A longitudinal enhancing biostatistical problem-solving in medical education. ChatGPT随时间的进展：纵向加强医学教育中生物统计学问题的解决。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-09-19 DOI: 10.1177/14604582251381260

Aleksandra Ignjatović, Marija Anđelković Apostolović, Lazar Stevanović, Pavle Radovanović, Marija Topalović, Tamara Filipović, Suzana Otašević

{"title":"ChatGPT's progress over time: A longitudinal enhancing biostatistical problem-solving in medical education.","authors":"Aleksandra Ignjatović, Marija Anđelković Apostolović, Lazar Stevanović, Pavle Radovanović, Marija Topalović, Tamara Filipović, Suzana Otašević","doi":"10.1177/14604582251381260","DOIUrl":"https://doi.org/10.1177/14604582251381260","url":null,"abstract":"Objective: ChatGPT has been recognised as a potentially transformative tool in higher education by enhancing the teaching and learning process. Cross-sectional evaluations have acknowledged this potential. This study evaluates ChatGPT's performance in solving specific biostatistical problems, focusing on accuracy, stability, and reproducibility, and explores its potential as a reliable educational tool in medical education. Methods: The correlation analysis task from Statistics at Square One by Swinscow and Campbell was chosen for its foundational role in biostatistics. Between October 2023 and March 2024, and July 2024, GPT-3.5 and GPT-4 were tested for accuracy in 12 parameters. Results: A statistically significant change in correct response rates was established in repeated measurements in the period October 2023, March 2024, and July 2024 for GPT-3.5 (Q = 100.99, p < 0.001), GPT-4.0 (Q = 89.55, p < 0.001), respectively. The significant GPT-3.5 improvement was established between March 2024/July 2024 (p = 0.004), and between October 2023 and July 2024 (p = 0.008). The significant GPT-4.0 improvement was established between October 2023 and March 2024 (p = 0.004), and between October 2023 and July 2024 (p = 0.026). Conclusion: Over 9 months, GPT-4 demonstrated rapid and consistent improvements, achieving perfect accuracy by March 2024. Although this study documented ChatGPT's advancement within 9 months, ChatGPT should be positioned as a supplementary tool in higher education classrooms, in the presence of educators, to enhance the learning process.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 3","pages":"14604582251381260"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance of artificial intelligence large language models (Copilot and Gemini) compared to human experts in healthcare policy making: A mixed-methods cross-sectional study. 人工智能大型语言模型（Copilot和Gemini）在医疗保健政策制定方面与人类专家的性能比较：一项混合方法横断面研究。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-09-22 DOI: 10.1177/14604582251381269

Mohsen Khosravi, Reyhane Izadi, Mina Aghamaleki Sarvestani, Hossein Bouzarjomehri, Milad Ahmadi Marzaleh, Ramin Ravangard

{"title":"Performance of artificial intelligence large language models (Copilot and Gemini) compared to human experts in healthcare policy making: A mixed-methods cross-sectional study.","authors":"Mohsen Khosravi, Reyhane Izadi, Mina Aghamaleki Sarvestani, Hossein Bouzarjomehri, Milad Ahmadi Marzaleh, Ramin Ravangard","doi":"10.1177/14604582251381269","DOIUrl":"https://doi.org/10.1177/14604582251381269","url":null,"abstract":"ObjectiveThis study aimed to assess the performance of Artificial Intelligence (AI) compared to human experts in healthcare policymaking.MethodsThis was a mixed-methods cross-sectional study conducted in Iran during the years 2024-2025, comparing, and analyzing the responses of multiple AI Large Language Models (LLMs) including Bing AI Copilot and Gemini and a sample of 15 human experts-using confusion matrix analysis. This analysis provided comprehensive data on the respondents' ability to answer context-specific questions regarding healthcare policy making, evaluated through multiple parameters including sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and overall accuracy.ResultsCopilot demonstrated a sensitivity of 0.867, specificity of 0, PPV of 0.722, NPV of 0, and accuracy of 0.65. In comparison, Gemini exhibited a sensitivity of 0.733, specificity of 0.4, PPV of 0.786, NPV of 0.333, and also an accuracy of 0.65. Additionally, the human experts' responses indicated a sensitivity of 0.5808, specificity of 0.2571, PPV of 0.7189, NPV of 0.1579, and an accuracy of 0.5050.ConclusionThe AI LLMs outperformed human experts in responding to the study questionnaire. The findings demonstrated the considerable potential of the LLMs in enhancing healthcare policy-making, particularly by serving as complementary tools and collaborators alongside humans.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 3","pages":"14604582251381269"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing maternal nutrition: The development of Doojan, a gamified mHealth app for pregnant women. 改善产妇营养：Doojan的开发，这是一款针对孕妇的游戏化移动健康应用程序。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-07-10 DOI: 10.1177/14604582251335182

Lida Moghaddam-Banaem, Rezvan Rahimi, Sabereh Ahmadi, Somayeh Hossainpour

{"title":"Enhancing maternal nutrition: The development of Doojan, a gamified mHealth app for pregnant women.","authors":"Lida Moghaddam-Banaem, Rezvan Rahimi, Sabereh Ahmadi, Somayeh Hossainpour","doi":"10.1177/14604582251335182","DOIUrl":"https://doi.org/10.1177/14604582251335182","url":null,"abstract":"Background: The widespread availability of smartphones has created new opportunities for engaging pregnant women and enhancing their self-management abilities to promote maternal and fetal health through mobile interventions. This study focuses on the design and development of a gamification-based mobile health (mHealth) application aimed at providing nutritional support to pregnant women.Methods: An iterative, user-centered design approach and Agile development method were employed to create the application. The developmental stages included identifying the application's features, the design and development process, and evaluation. End users assessed usability using the System Usability Scale (SUS), while experts evaluated quality using the Mobile Application Rating Scale (MARS).Results: Feedback from experts and end users categorized the application's functionalities into general, specific, and gamification-related functions. Pregnant women rated the application's usability as acceptable (68.25 ± 10.86), and experts rated its quality as acceptable (mean 3.89 out of 5, SD 0.25).Conclusions: The positive evaluation results support the use of this application as a tool for managing gestational nutrition and enhancing self-awareness. Future research should investigate its impact on the nutritional status of pregnant women and their infants.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 3","pages":"14604582251335182"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting functional outcomes after a stroke event by clinical text notes: A comparative study of traditional machine learning and deep learning methods. 通过临床文本笔记预测中风事件后的功能结果：传统机器学习和深度学习方法的比较研究。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-09-17 DOI: 10.1177/14604582251381194

Yu-Hsiang Su, Chih-Fong Tsai

{"title":"Predicting functional outcomes after a stroke event by clinical text notes: A comparative study of traditional machine learning and deep learning methods.","authors":"Yu-Hsiang Su, Chih-Fong Tsai","doi":"10.1177/14604582251381194","DOIUrl":"https://doi.org/10.1177/14604582251381194","url":null,"abstract":"Objective: Accurately predicting functional outcomes after acute ischemic stroke is essential for healthcare institutions to optimize staffing and resource allocation. Although text mining has been applied to build such models, most prior studies emphasize traditional machine learning, with limited comparison to deep learning methods. Methods: Clinical text notes were collected from a Taiwanese hospital to build the experimental dataset. Four textual feature representation techniques were evaluated: bag-of-words (BOW), term frequency-inverse document frequency (TF-IDF), embeddings from language models (ELMo), and bidirectional encoder representations from transformers (BERT). Correspondingly, four predictive models were tested: k-nearest neighbor (KNN), support vector machine (SVM), convolutional neural network (CNN), and long short-term memory (LSTM). Results: The best performance was obtained using BOW features with an SVM classifier. Feature fusion strategies, combining representations such as BOW + TF-IDF and BOW + BERT, also yielded strong performance. Notably, the BOW + TF-IDF combination with SVM achieved the lowest type I error, effectively minimizing the misclassification of patients with poor outcomes. Conclusion: Traditional machine learning methods outperformed deep learning models in this study. Among all combinations, BOW + TF-IDF features with SVM provided the most accurate predictions and lowest risk of false positives in stroke outcome prediction.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 3","pages":"14604582251381194"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0