Healthcare Informatics Research最新文献

{"title":"Effect of Mobile Health Applications on Improving Self-Management Knowledge and Seizure Control in Epilepsy Patients: A Scoping Review.","authors":"Iin Ernawati, Nanang Munif Yasin, Ismail Setyopranoto, Zullies Ikawati","doi":"10.4258/hir.2024.30.2.127","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.127","url":null,"abstract":"<p><strong>Objectives: </strong>Mobile health app-based interventions are increasingly being developed to support chronic disease management, particularly for epilepsy patients. These interventions focus on managing stress, monitoring drug side effects, providing education, and promoting adherence to medication regimens. Therefore, this scoping review aims to assess how mobile health applications improve epilepsy patients' knowledge and seizure control, and to identify the features of these apps that are frequently used and have proven to be beneficial.</p><p><strong>Methods: </strong>This scoping review was conducted using scientific databases such as ScienceDirect, PubMed, and Google Scholar, adhering to the Joanna Briggs Institute guidelines. The review framework consisted of five steps: identifying research questions, finding relevant articles, selecting articles, presenting data, and compiling the results. The literature search included all original articles published in English from 2013 to 2023.</p><p><strong>Results: </strong>Among six articles that discussed mobile applications for epilepsy patients, all featured similar functionalities, including education on epilepsy management and seizure monitoring. Four of the articles highlighted behavioral interventions, such as reminder systems, designed to improve medication adherence. The remaining two articles focused on a side-effect reporting system that enabled doctors or health workers to evaluate and regularly monitor adverse effects.</p><p><strong>Conclusions: </strong>This scoping review reveals that mobile health applications employing a combination of educational and behavioral interventions for epilepsy patients significantly improve knowledge about patient self-management and medication adherence. These interventions can prevent seizures, increase awareness, enable better activity planning, improve safety, and reduce the frequency of seizures and side effects of antiepileptic drugs.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"127-139"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955424","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":"Prevalence of Selected Ophthalmic Diseases Using a Smartphone-Based Fundus Imaging System in Quang Tri and Thai Nguyen, Vietnam.","authors":"Jaewon Kim, Sangchul Yoon, Holden Yoon Seung Kim","doi":"10.4258/hir.2024.30.2.162","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.162","url":null,"abstract":"<p><strong>Objectives: </strong>This study investigated the prevalence of ophthalmic diseases in Quang Tri and Thai Nguyen, Vietnam, utilizing a smartphone-based fundus imaging (SBFI) system.</p><p><strong>Methods: </strong>This cross-sectional study included nearly 10,000 patients who visited community health centers between July and August 2019. All participants underwent visual acuity testing and fundus imaging. We collected demographic data and medical histories, and fundus images were captured using the EYELIKE system. Data were compiled on an online platform, allowing clinicians from other regions to make diagnoses.</p><p><strong>Results: </strong>The study revealed significant variations in visual acuity and the prevalence of ophthalmic diseases between the two regions. Quang Tri had a higher proportion of individuals with good eyesight compared to Thai Nguyen. In Quang Tri, nearly 50% of the population had media haze, while in Thai Nguyen, about one-third of the population was affected. The prevalence of glaucomatous optic nerve and age-related macular degeneration was approximately 1% higher in Quang Tri than in Thai Nguyen. These findings provide valuable insights into the eye health status of these regions, indicating that eye health in Quang Tri was poorer than in Thai Nguyen.</p><p><strong>Conclusions: </strong>The prevalence rates of ophthalmic conditions in this study were within the expected ranges compared to those in other Asian countries, though they were somewhat low. The SBFI method, being simpler and more efficient than the Rapid Assessment of Avoidable Blindness, offers a promising approach for measuring and estimating the prevalence of ophthalmic diseases.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"162-167"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955691","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":"Current Health Data Standardization Project and Future Directions to Ensure Interoperability in Korea.","authors":"AeKyung Kwon, Ho-Young Lee, Soo-Yong Shin, Kwangmo Yang, Yena Sung, Kwangjae Lee, Nam-Soo Byeon, Tae-Hwan Lim, Jae-Ho Lee","doi":"10.4258/hir.2024.30.2.93","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.93","url":null,"abstract":"<p><strong>Objectives: </strong>The need for interoperability at the national level was highlighted in Korea, leading to a consensus on the importance of establishing national standards that align with international technological standards and reflect contemporary needs. This article aims to share insights into the background of the recent national health data standardization policy, the activities of the Health Data Standardization Taskforce, and the future direction of health data standardization in Korea.</p><p><strong>Methods: </strong>To ensure health data interoperability, the Health Data Standardization Taskforce was jointly organized by the public and private sectors in December 2022. The taskforce operated three working groups. It reviewed international trends in interoperability standardization, assessed the current status of health data standardization, discussed its vision, mission, and strategies, engaged in short-term standardization activities, and established a governance system for standardization.</p><p><strong>Results: </strong>On September 15, 2023, the notice of \"Health Data Terminology and Transmission Standards\" in Korea was thoroughly revised to improve the exchange of health information between information systems and ensure interoperability. This notice includes the Korea Core Data for Interoperability (KR CDI) and the Korea Core Data Transmission Standard (HL7 FHIR KR Core), which are outcomes of the taskforce's efforts. Additionally, to reinforce the standardized governance system, the Health-Data Standardization Promotion Committee was established.</p><p><strong>Conclusions: </strong>Active interest and support from medical informatics experts are needed for the development and widespread adoption of health data standards in Korea.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"93-102"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955422","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":"Informatics Competencies of Students in a Doctor of Nursing Practice Program: A Descriptive Study.","authors":"Jeeyae Choi, Seoyoon Woo, Valerie Tarte","doi":"10.4258/hir.2024.30.2.147","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.147","url":null,"abstract":"<p><strong>Objectives: </strong>Health systems that apply artificial intelligence (AI) are transforming the roles of healthcare providers, including those of Doctor of Nursing Practice (DNP) providers. These professionals are required to utilize informatics knowledge and skills to deliver quality care, necessitating a high level of informatics competencies, which should be developed through well-structured courses. The purpose of this study is to assess the informatics competency scale scores of DNP students and to provide recommendations for enhancing the informatics curriculum.</p><p><strong>Methods: </strong>An online informatics course was offered to students enrolled in a Bachelor of Science in Nursing to DNP program, and their informatics competency, which includes three subscales, was evaluated. Online survey data were collected from Fall 2021 to Fall 2022 using the \"Self-Assessment of Informatics Competency Scale for Health Professionals.\"</p><p><strong>Results: </strong>An analysis of 127 student responses revealed that students demonstrated competence in overall informatics competency and in one subscale: \"applied computer skills (clinical informatics).\" They showed proficiency in the \"basic computer skills\" and the \"role\" subscales. However, they reported lower competency in managing data and integrating standard terminology into their practice.</p><p><strong>Conclusions: </strong>The findings offer detailed insights into the current informatics competencies of DNP students and can inform informatics educators on how to enhance their courses. As healthcare institutions increasingly depend on AI applications, it is imperative for informatics educators to include AI-related content in their curricula.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"147-153"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955579","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":"Health and Medical Big Data Forum: Large Language Models in Healthcare.","authors":"Jinwook Choi","doi":"10.4258/hir.2024.30.2.91","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.91","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"91-92"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955426","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":"Status of MyHealthWay and Suggestions for Widespread Implementation, Emphasizing the Utilization and Practical Use of Personal Medical Data.","authors":"Taejun Ha, Seonguk Kang, Na Young Yeo, Tae-Hoon Kim, Woo Jin Kim, Byoung-Kee Yi, Jae-Won Jang, Sang Won Park","doi":"10.4258/hir.2024.30.2.103","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.103","url":null,"abstract":"<p><strong>Objectives: </strong>In the Fourth Industrial Revolution, there is a focus on managing diverse medical data to improve healthcare and prevent disease. The challenges include tracking detailed medical records across multiple institutions and the necessity of linking domestic public medical entities for efficient data sharing. This study explores MyHealthWay, a Korean healthcare platform designed to facilitate the integration and transfer of medical data from various sources, examining its development, importance, and legal implications.</p><p><strong>Methods: </strong>To evaluate the management status and utilization of MyHealthWay, we analyzed data types, security, legal issues, domestic versus international issues, and infrastructure. Additionally, we discussed challenges such as resource and infrastructure constraints, regulatory hurdles, and future considerations for data management.</p><p><strong>Results: </strong>The secure sharing of medical information via MyHealthWay can reduce the distance between patients and healthcare facilities, fostering personalized care and self-management of health. However, this approach faces legal challenges, particularly relating to data standardization and access to personal health information. Legal challenges in data standardization and access, particularly for secondary uses such as research, necessitate improved regulations. There is a crucial need for detailed governmental guidelines and clear data ownership standards at institutional levels.</p><p><strong>Conclusions: </strong>This report highlights the role of Korea's MyHealthWay, which was launched in 2023, in transforming healthcare through systematic data integration. Challenges include data privacy and legal complexities, and there is a need for data standardization and individual empowerment in health data management within a systematic medical big data framework.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"103-112"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955930","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":"Prediction of Diabetes Using Data Mining and Machine Learning Algorithms: A Cross-Sectional Study.","authors":"Hassan Shojaee-Mend, Farnia Velayati, Batool Tayefi, Ebrahim Babaee","doi":"10.4258/hir.2024.30.1.73","DOIUrl":"10.4258/hir.2024.30.1.73","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop a model to predict fasting blood glucose status using machine learning and data mining, since the early diagnosis and treatment of diabetes can improve outcomes and quality of life.</p><p><strong>Methods: </strong>This crosssectional study analyzed data from 3376 adults over 30 years old at 16 comprehensive health service centers in Tehran, Iran who participated in a diabetes screening program. The dataset was balanced using random sampling and the synthetic minority over-sampling technique (SMOTE). The dataset was split into training set (80%) and test set (20%). Shapley values were calculated to select the most important features. Noise analysis was performed by adding Gaussian noise to the numerical features to evaluate the robustness of feature importance. Five different machine learning algorithms, including CatBoost, random forest, XGBoost, logistic regression, and an artificial neural network, were used to model the dataset. Accuracy, sensitivity, specificity, accuracy, the F1-score, and the area under the curve were used to evaluate the model.</p><p><strong>Results: </strong>Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important factors for predicting fasting blood glucose status. Though the models achieved similar predictive ability, the CatBoost model performed slightly better overall with 0.737 area under the curve (AUC).</p><p><strong>Conclusions: </strong>A gradient boosted decision tree model accurately identified the most important risk factors related to diabetes. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important risk factors for diabetes, respectively. This model can support planning for diabetes management and prevention.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 1","pages":"73-82"},"PeriodicalIF":2.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10879823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139740824","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":"Survey of Medical Applications of Federated Learning.","authors":"Geunho Choi, Won Chul Cha, Se Uk Lee, Soo-Yong Shin","doi":"10.4258/hir.2024.30.1.3","DOIUrl":"10.4258/hir.2024.30.1.3","url":null,"abstract":"<p><strong>Objectives: </strong>Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain.</p><p><strong>Methods: </strong>We conducted a literature search using the keywords \"federated learning\" in combination with \"medical,\" \"healthcare,\" or \"clinical\" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security.</p><p><strong>Results: </strong>In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns.</p><p><strong>Conclusions: </strong>FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 1","pages":"3-15"},"PeriodicalIF":2.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10879826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139740871","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":"Deep Learning Model and its Application for the Diagnosis of Exudative Pharyngitis.","authors":"Seo Yi Chng, Paul Jie Wen Tern, Matthew Rui Xian Kan, Lionel Tim-Ee Cheng","doi":"10.4258/hir.2024.30.1.42","DOIUrl":"10.4258/hir.2024.30.1.42","url":null,"abstract":"<p><strong>Objectives: </strong>Telemedicine is firmly established in the healthcare landscape of many countries. Acute respiratory infections are the most common reason for telemedicine consultations. A throat examination is important for diagnosing bacterial pharyngitis, but this is challenging for doctors during a telemedicine consultation. A solution could be for patients to upload images of their throat to a web application. This study aimed to develop a deep learning model for the automated diagnosis of exudative pharyngitis. Thereafter, the model will be deployed online.</p><p><strong>Methods: </strong>We used 343 throat images (139 with exudative pharyngitis and 204 without pharyngitis) in the study. ImageDataGenerator was used to augment the training data. The convolutional neural network models of MobileNetV3, ResNet50, and EfficientNetB0 were implemented to train the dataset, with hyperparameter tuning.</p><p><strong>Results: </strong>All three models were trained successfully; with successive epochs, the loss and training loss decreased, and accuracy and training accuracy increased. The EfficientNetB0 model achieved the highest accuracy (95.5%), compared to MobileNetV3 (82.1%) and ResNet50 (88.1%). The EfficientNetB0 model also achieved high precision (1.00), recall (0.89) and F1-score (0.94).</p><p><strong>Conclusions: </strong>We trained a deep learning model based on EfficientNetB0 that can diagnose exudative pharyngitis. Our model was able to achieve the highest accuracy, at 95.5%, out of all previous studies that used machine learning for the diagnosis of exudative pharyngitis. We have deployed the model on a web application that can be used to augment the doctor's diagnosis of exudative pharyngitis.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 1","pages":"42-48"},"PeriodicalIF":2.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10879828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139740820","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":"Fostering Digital Health in Universities: An Experience of the First Junior Scientific Committee of the Brazilian Congress of Health Informatics.","authors":"Alexandre Negrao Pantaleao, Anna Luísa Mennitti, Felipe Baptista Brunheroto, Vitória Stavis, Laura Teresa Ricoboni, Victor Augusto Fonseca de Castro, Ollivia Frederigue Ferreira, Eura Martins Lage, Deborah Ribeiro Carvalho, Anita Maria da Rocha Fernandes, Juliano de Souza Gaspar","doi":"10.4258/hir.2024.30.1.83","DOIUrl":"10.4258/hir.2024.30.1.83","url":null,"abstract":"<p><strong>Objectives: </strong>Digital health (DH) is a revolution driven by digital technologies to improve health. Despite the importance of DH, curricular updates in healthcare university programs are scarce, and DH remains undervalued. Therefore, this report describes the first Junior Scientific Committee (JSC) focusing on DH at a nationwide congress, with the aim of affirming its importance for promoting DH in universities.</p><p><strong>Methods: </strong>The scientific committee of the Brazilian Congress of Health Informatics (CBIS) extended invitations to students engaged in health-related fields, who were tasked with organizing a warm-up event and a 4-hour session at CBIS. Additionally, they were encouraged to take an active role in a workshop alongside distinguished experts to map out the current state of DH in Brazil.</p><p><strong>Results: </strong>The warm-up event focused on the topic \"Artificial intelligence in healthcare: is a new concept of health about to arise?\" and featured remote discussions by three professionals from diverse disciplines. At CBIS, the JSC's inaugural presentation concentrated on delineating the present state of DH education in Brazil, while the second presentation offered strategies to advance DH, incorporating viewpoints from within and beyond the academic sphere. During the workshop, participants deliberated on the most crucial competencies for future professionals in the DH domain.</p><p><strong>Conclusions: </strong>Forming a JSC proved to be a valuable tool to foster DH, particularly due to the valuable interactions it facilitated between esteemed professionals and students. It also supports the cultivation of leadership skills in DH, a field that has not yet received the recognition it deserves.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 1","pages":"83-89"},"PeriodicalIF":2.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10879825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139740821","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}