Clinical eHealth最新文献

{"title":"Personalizing nutrition and recipe recommendation using attention mechanism with an ensemble model","authors":"Shilpa Chaudhari ,&nbsp;Archana Rane ,&nbsp;Amala Rashmi Kumar","doi":"10.1016/j.ceh.2025.03.002","DOIUrl":"10.1016/j.ceh.2025.03.002","url":null,"abstract":"<div><div>Nutrient management in the context of this proposed work aims to quantize the consumption of essential nutrients in an efficient format such that it leads to a healthy and balanced lifestyle. This paper presents an intelligent nutrition management and recipe recommendation system tailored to individuals’ nutritional profiles, using an ensemble model augmented by an attention mechanism. The system quantifies user nutritional deficiencies based on blood analysis and personal preferences, generating personalized food and recipe suggestions to address these gaps. By integrating multiple supervised learning algorithms such as Random Forest, XGBoost, and MLP, the model dynamically prioritizes nutrients relevant to the user’s needs. Leveraging data from the National Institute of Nutrition, recipes are recommended in video format, aiming to enhance users’ health and dietary habits. The proposed model outperforms baseline systems in detecting nutritional deficiencies and offers efficient, personalized recipe recommendations through a user-friendly web and mobile interface.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 66-77"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815165","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}

{"title":"An interpretable machine learning model to predict hospitalizations","authors":"Hagar Elbatanouny ,&nbsp;Hissam Tawfik ,&nbsp;Tarek Khater ,&nbsp;Anatoliy Gorbenko","doi":"10.1016/j.ceh.2025.03.004","DOIUrl":"10.1016/j.ceh.2025.03.004","url":null,"abstract":"<div><div>Hospital management plays a pivotal role in ensuring the efficient delivery of medical services, especially in the face of challenges posed by pandemics such as COVID-19. This paper explores the application of machine learning techniques in addressing the challenge of hospitalization during pandemics. Leveraging a comprehensive dataset sourced from the Mexican government, various supervised learning algorithms including Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron are trained and evaluated to discern factors contributing to hospitalizations. Feature importance analysis and dimensionality reduction techniques are employed to enhance models predictive performance. The best model was Gradient Boosting algorithm with an accuracy of 85.63% and AUC score of 0.8696. The interpretability plots showed that pneumonia had a positive impact on the hospitalization prediction of the model. Our analysis indicates that women aged over 45 with pneumonia and concurrent COVID-19 exhibit the highest likelihood of hospitalization. This study underscores the potential of interpretable machine learning in aiding hospital managers to optimize resource allocation, hospitalization cases, and make data-driven decisions during pandemics.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 53-65"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in digital data acquisition and CAD technology in Dentistry: Innovation, clinical Impact, and promising integration of artificial intelligence","authors":"Mohammed Ahmed Alghauli ,&nbsp;Waad Aljohani ,&nbsp;Shahad Almutairi ,&nbsp;Rola Aljohani ,&nbsp;Ahmed Yaseen Alqutaibi","doi":"10.1016/j.ceh.2025.03.001","DOIUrl":"10.1016/j.ceh.2025.03.001","url":null,"abstract":"<div><div>This review examines recent advancements in digital data acquisition and CAD technology in dentistry, highlighting improvements in communication, AI integration, and predictive analytics in diagnostic and treatment tools. Over the past decade, these innovations have enhanced workflow efficiency, enabling precise planning, automated processes, and faster treatment turnaround times. AI-enhanced CAD systems show significant promise for improving diagnostic accuracy and treatment outcomes. Utilizing these advanced technologies improved dental workflow, particularly the full digital workflow. Intraoral scanning, CBCT data acquisition, facial scanning, smile, and CAD design have revolutionized dental practice, rendering digital dentistry the primary daily routine.</div><div>The future of dentistry is entirely digital; virtual dental arches, virtual smiles, virtual articulators, and virtual patients are the face of the modern dental era. AI aids significantly in data acquisition, diagnosis, planning, and CAD designing. However, the review underscores the need for validation, monitoring, and ethical oversight to ensure safe and effective AI applications in clinical settings. It also emphasizes the importance of practitioners’ understanding of CAD components in CAD-CAM systems, facilitating informed technology selection to optimize treatment efficacy and patient outcomes.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 32-52"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors influencing REducing Delay through edUcation on eXacerbations (REDUX) implementation: A stakeholder analysis","authors":"Xiaoyue Song ,&nbsp;Cynthia Hallensleben ,&nbsp;Haibo Wang ,&nbsp;Jun Guo ,&nbsp;Weihong Zhang ,&nbsp;Hongxia Shen ,&nbsp;Robbert J.J. Gobbens ,&nbsp;Niels H. Chavannes ,&nbsp;Anke Versluis","doi":"10.1016/j.ceh.2025.02.001","DOIUrl":"10.1016/j.ceh.2025.02.001","url":null,"abstract":"<div><div>REducing Delay through edUcation on eXacerbations (REDUX) shows promise in reducing exacerbation recognition and action delays for chronic lung diseases in the Netherlands. However, factors influencing its successful implementation in China remain unclear. To identify the perceived factors influencing nurse-led self-management implementation of REDUX in China, stakeholder analysis using qualitative and quantitative approaches was conducted. A qualitative approach assessed support for REDUX, perceived influencing factors, and preferred intervention delivery mode among patients, healthcare professionals, and policymakers. A quantitative approach identified necessary conditions for developing and implementing a digital-version intervention, involving app developers and cyber-security officers. The study followed COREQ and stakeholder analysis guidelines. Thirty-five patients, healthcare professionals, and policymakers highly supported REDUX. Perceived influencing factors included facilitators (e.g., easy-to-use design, perceived benefits) and barriers (e.g., patients’ affordability, lack of policy support). Preferred intervention delivery modes varied among stakeholders. Eighty-seven app developers and cyber-security officers completed quantitative surveys. The quantitative data showed that the work process of developing the health apps and protecting the users’ security and privacy mostly aligned with the related international guideline recommendations. The study identified key interdependent factors that were perceived as crucial for REDUX implementation success. Healthcare policies should prioritize self-management intervention, and minor action plan adjustments are needed.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 17-25"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring","authors":"Luca Cossu ,&nbsp;Francesco Prendin ,&nbsp;Giacomo Cappon ,&nbsp;David Herzig ,&nbsp;Lia Bally ,&nbsp;Andrea Facchinetti","doi":"10.1016/j.ceh.2025.01.003","DOIUrl":"10.1016/j.ceh.2025.01.003","url":null,"abstract":"<div><h3>Background</h3><div>Post-bariatric hypoglycemia (PBH) is a severe and often overlooked complication of bariatric surgery (BS), characterized by dangerously low blood glucose levels after meals, particularly those high in carbohydrates. Unlike in Type 1 and Type 2 diabetes (T1D, T2D), where decision support systems (DSS) and continuous glucose monitoring (CGM) tools aid blood glucose management, no dedicated DSS exists for PBH. This leaves individuals vulnerable to recurrent, unpredictable hypoglycemia, posing significant health risks. To address this gap, we propose Glu4, an open-source software package designed to predict and notify users of impending PBH events using CGM data.</div></div><div><h3>Methods</h3><div>Glu4 employs a two-step approach to predict<!--> <!-->PBH. A run-to-run algorithm forecasts future glucose levels using past CGM data, identifying potential hypoglycemic events 30 min in advance. An intelligent alarm system alerts users when glucose levels are predicted to drop below a critical threshold, prompting preventive action. A pilot study involving three PBH patients collected real-time glucose data to validate the system’s predictive performance.</div></div><div><h3>Results</h3><div>The pilot study demonstrated that Glu4 reliably predicted impending hypoglycemia in all participants, providing timely alerts 30 min before glucose drops. The system showed a high specificity, with no false alarms being triggered during the monitoring period. The proactive notifications enabled participants to manage their glucose levels more effectively by taking preventive actions such as consuming rescue carbohydrates before the onset of severe hypoglycemia.</div></div><div><h3>Conclusions</h3><div>Glu4 represents a promising tool for managing PBH, leveraging CGM data to deliver accurate, timely alerts that enable proactive intervention. By improving safety and quality of life for individuals with PBH, Glu4 addresses a critical unmet need. Future work will focus on enhancing system capabilities and conducting larger-scale studies to validate its effectiveness and refine its usability for clinical adoption.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing thyroid disease prediction and comorbidity management through advanced machine learning frameworks","authors":"P. Sanju ,&nbsp;N. Syed Siraj Ahmed ,&nbsp;P. Ramachandran ,&nbsp;P. Mohamed Sajid ,&nbsp;R. Jayanthi","doi":"10.1016/j.ceh.2025.01.002","DOIUrl":"10.1016/j.ceh.2025.01.002","url":null,"abstract":"<div><div>Thyroid disease is one of the most prevalent endocrine disorders worldwide, necessitating precise and efficient diagnostic models for improved clinical outcomes. This study proposes a Hybrid Feature Selection and Deep Learning Framework (HFSDLF) that integrates Random Forests with Principal Component Analysis (PCA) and L1 regularization for effective feature selection and classification. Utilizing the UCI Thyroid Dataset, the framework combines the strengths of deep learning-based feature extraction and traditional machine learning classifiers. The Random Forest classifier achieved the highest accuracy of 96.30 %, outperforming other models such as Decision Trees and Logistic Regression, with notable improvements in sensitivity and specificity. The novelty of this work lies in its hybrid approach to feature selection, which reduces dimensionality while retaining the most informative features, and its application of an optimized Random Forest model for enhanced classification accuracy. Comparative analysis with existing methods further highlights the superiority of the proposed framework in terms of accuracy and processing efficiency. This research addresses key limitations of existing approaches and contributes to the field by demonstrating a scalable and interpretable solution for thyroid disease diagnosis. The proposed framework provides a benchmark for future studies, underscoring the importance of hybrid methodologies in medical data analysis.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 7-16"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical prognosis and risk factors of death for COVID-19 patients complicated with coronary heart disease/diabetes/hypertension-a retrospective, real-world study","authors":"Da-Wei Yang ,&nbsp;Hui-Fen Weng ,&nbsp;Jing Li ,&nbsp;Min-Jie Ju ,&nbsp;Hao Wang ,&nbsp;Yi-Chen Jia ,&nbsp;Xiao-Dan Wang ,&nbsp;Jia Fan ,&nbsp;Zuo-qin Yan ,&nbsp;Jian Zhou ,&nbsp;Cui-Cui Chen ,&nbsp;Yin-Zhou Feng ,&nbsp;Xiao-Yan Chen ,&nbsp;Dong-Ni Hou ,&nbsp;Xing-Wei Lu ,&nbsp;Wei Yang ,&nbsp;Yin Wu ,&nbsp;Zheng-Guo Chen ,&nbsp;Tao Bai ,&nbsp;Xiao-Han Hu ,&nbsp;Yuan-Lin Song","doi":"10.1016/j.ceh.2024.12.002","DOIUrl":"10.1016/j.ceh.2024.12.002","url":null,"abstract":"<div><h3>Objectives</h3><div>To explore the clinical prognosis and the risk factors of death from COVID-19 patients complicated with one of the three major comorbidities (coronary heart disease, diabetes, or hypertension) based on real-world data.</div></div><div><h3>Methods</h3><div>This single-centre retrospective real-world study investigated all in-hospital patients who were transferred to the Coronavirus Special Ward of the Elderly Center of Zhongshan Hospital from March to June 2022 with a positive COVID-19 virus nucleic acid test and with at least one of the three comorbidities (coronary heart disease, diabetes or hypertension). Clinical data and laboratory test results of eligible patients were collected. A multivariate logistic regression analysis was performed to explore the risk associated with the prognosis.</div></div><div><h3>Results</h3><div>For the 1,281 PCR-positive patients at the admission included in the analysis, the mean age was 70.5 ± 13.7 years, and 658 (51.4 %) were males. There were 1,092 (85.2 %) patients with hypertension, 477(37.2 %) patients with diabetes, and 124 (9.7 %) patients with coronary heart disease. The length of hospital stay (LOS) was 9.2 ± 5.1 days. Among all admitted patients,1112 (91.5 %) were fully recovered, 77 (6.9 %) were improved, and 29 (2.6 %) died. Over the hospitalization, 172 (13.4 %) PCR-positive patients experienced rebound COVID following initial recovery with a negative PCR test. A multivariate logistic regression analysis showed that vaccination had no protective effects in this study population; Paxlovid was associated with a lower risk of death(OR = 0.98, 95 % CI: 0.95–1.00). Whereas the presence of solid malignancies and nerve system disease were significantly associated with increased risk of death (OR = 1.04, 95 % CI:1.02–1.05; OR = 1.10, 95 % CI:1.05–1.14; OR = 1.08, 95 % CI:1.03–1.13; respectively).</div></div><div><h3>Conclusion</h3><div>The vast majority of the hospitalized COVID patients were fully recovered. Paxlovid was associated with a lower risk of death. In contrast, the presence of solid malignancies and nerve system disease and some treatments were all significantly associated with an increased risk of death.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 26-31"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conversational AI with large language models to increase the uptake of clinical guidance","authors":"Gloria Macia ,&nbsp;Alison Liddell ,&nbsp;Vincent Doyle","doi":"10.1016/j.ceh.2024.12.001","DOIUrl":"10.1016/j.ceh.2024.12.001","url":null,"abstract":"<div><div>The rise of large language models (LLMs) and conversational applications, like ChatGPT, prompts Health Technology Assessment (HTA) bodies, such as NICE, to rethink how healthcare professionals access clinical guidance. Integrating LLMs into systems like Retrieval-Augmented Generation (RAG) offers potential solutions to current LLMs’ problems, like the generation of false or misleading information. The objective of this paper is to design and debate the value of an AI-driven system, similar to ChatGPT, to enhance the uptake of clinical guidance within the National Health Service (NHS) of the UK. Conversational interfaces, powered by LLMs, offer healthcare practitioners clear benefits over traditional ways of getting clinical guidance, such as easy navigation through long documents, blending information from various trusted sources, or expediting evidence-based decisions in situ. But, putting these interfaces into practice brings new challenges for HTA bodies, like assuring quality, addressing data privacy concerns, navigating existing resource constraints, or preparing the organization for innovative practices. Rigorous empirical evaluations are necessary to validate their effectiveness in increasing the uptake of clinical guidance among healthcare practitioners. A feasible evaluation strategy is elucidated in this research while its implementation remains as future work.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 147-152"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a mobile health application for epilepsy self-management: Focus group discussion and validity of study results","authors":"Iin Ernawati ,&nbsp;Nanang Munif Yasin ,&nbsp;Ismail Setyopranoto ,&nbsp;Zullies Ikawati","doi":"10.1016/j.ceh.2024.12.005","DOIUrl":"10.1016/j.ceh.2024.12.005","url":null,"abstract":"<div><div>Mobile health systems in the current digital era can be an opportunity for the development of health services, especially epilepsy, which is expected to help therapy management in monitoring drug therapy. Mobile health-based interventions have now begun to be developed for chronic disease management in managing stress, monitoring drug side effects, adherence to drug use, and seizures in epilepsy patients. To create the mobile health system, it is necessary to explore information not only from the literature but also from experts and patients. Therefore, this study aimed to examine what features/elements are needed in the mobile health system application. This study used a qualitative methodology with focus group discussion (FGD). The discussion process was recorded and transcribed verbatim, and the data was analyzed using thematic analysis with a descriptive interpretation approach. In addition, content validity by experts was also carried out from features or domains found in the literature and during FGD. The results of the FGD showed that the features needed for application development include patient profiles, drug reminders, information about diseases and drugs, medication records, side effects/adverse events records, records of frequency and triggers of seizures, application appearance, and ease of use. Based on the validity content by experts, all domains and features obtained (Items Content Validation Index) I-CVI values &gt; 0.79 and were acceptable. In conclusion, this data can be used to develop the design and features of mobile health system applications for epilepsy patients.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 190-199"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International collaboration in an online digital health education for undergraduate nursing students in China: Results and recommendations for course development from World eHealth Living Lab","authors":"Hongxia Shen ,&nbsp;Cynthia Hallensleben ,&nbsp;Haixing Shi ,&nbsp;Rianne van der Kleij ,&nbsp;Huohuo Dai ,&nbsp;Niels Chavannes","doi":"10.1016/j.ceh.2024.11.001","DOIUrl":"10.1016/j.ceh.2024.11.001","url":null,"abstract":"<div><div>Digital health enhances healthcare accessibility and should be integrated into nursing education to prepare future nurses for the evolving medical systems. An international collaboration between a Chinese medical university and World eHealth Living Lab of Leiden University Medical Center in the Netherlands developed and implemented the online course “Digital Health Empowerment and Nursing Innovation” for undergraduate nursing students in China. The course’s effectiveness was evaluated using a mixed methods approach, including a pre- and post-test assessing students’ scientific innovation ability, a post-test for protocol completion, students’ attitudes and satisfaction. 32 undergraduate nursing students completed the course, achieving a 100 % attendance rate and showing significant improvement in the total score of scientific innovation ability (37.87 ± 6.16 versus 40.97 ± 6.32, <em>P</em> = 0.049). Specifically, the score of thinking innovation improved significantly (17.31 ± 3.28 versus 19.28 ± 3.18, <em>P</em> = 0.017), while application innovation and scientific research practice scores remained unchanged. Participants highly rated the value of protocol writing with 23–25 (total score of 28) and presentation with 41–45 (total score of 48). Additionally, students reported high satisfaction with the aspects of this course including a well-structured schedule with lectures and workshops, feasible and sufficient materials on the online platform, and engaging and helpful teaching methods. Furthermore, suggestions of the course are mainly related to addressing the complexity of the platform, providing timely feedback and evaluation from teachers, and improving (online) interactions.This international collaboration effectively enhanced Chinese nursing students’ scientific innovation ability and thinking innovation, with high satisfaction reported. Future digital health education should emphasize practical research examples to implement innovations. Specifically, active teaching methods, such as practice units and student engagement in digital health innovation research implementation in clinical settings, are recommended for future courses. In areas with limited access to digital health specialists, online platforms can enhance access to high-quality medical education.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 136-146"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}