Healthcare Informatics Research最新文献

{"title":"Empowering Healthcare through Comprehensive Informatics Education: The Status and Future of Biomedical and Health Informatics Education.","authors":"Kye Hwa Lee, Myung-Gwan Kim, Jae-Ho Lee, Jisan Lee, Insook Cho, Mona Choi, Hyun Wook Han, Myonghwa Park","doi":"10.4258/hir.2024.30.2.113","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.113","url":null,"abstract":"<p><strong>Objectives: </strong>Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development.</p><p><strong>Methods: </strong>We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education. The challenges and future strategies in Korea were also discussed based on the experts' opinions.</p><p><strong>Results: </strong>This review presents international recommendations for establishing education in biomedical and health informatics, as well as global examples at the undergraduate and graduate levels in medical and nursing education. It provides a thorough examination of the best practices, strategies, and competencies in informatics education. The review also assesses the current state of medical informatics and nursing informatics education in Korea. We highlight the challenges faced by academic institutions and conclude with a call to action for educators to enhance the preparation of professionals to effectively utilize technology in any healthcare setting.</p><p><strong>Conclusions: </strong>To adapt to the digitalization of healthcare, systematic and continuous workforce development is essential. Future education should prioritize curriculum innovations and the establishment of integrated education programs, focusing not only on students but also on educators and all healthcare personnel in the field. Addressing these challenges requires collaboration among educational institutions, academic societies, government agencies, and international bodies dedicated to systematic and continuous workforce development.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"113-126"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955425","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":"Corrigendum to: Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach.","authors":"Suncheol Heo, Jae Yong Yu, Eun Ae Kang, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Yebin Chega, Hyojung Jung, Suehyun Lee, Rae Woong Park, Kwangsoo Kim, Yul Hwangbo, Jae-Hyun Lee, Yu Rang Park","doi":"10.4258/hir.2024.30.2.168","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.168","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"168"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955421","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 and Validation of Adaptable Skin Cancer Classification System Using Dynamically Expandable Representation.","authors":"Bong Kyung Jang, Yu Rang Park","doi":"10.4258/hir.2024.30.2.140","DOIUrl":"https://doi.org/10.4258/hir.2024.30.2.140","url":null,"abstract":"<p><strong>Objectives: </strong>Skin cancer is a prevalent type of malignancy, necessitating efficient diagnostic tools. This study aimed to develop an automated skin lesion classification model using the dynamically expandable representation (DER) incremental learning algorithm. This algorithm adapts to new data and expands its classification capabilities, with the goal of creating a scalable and efficient system for diagnosing skin cancer.</p><p><strong>Methods: </strong>The DER model with incremental learning was applied to the HAM10000 and ISIC 2019 datasets. Validation involved two steps: initially, training and evaluating the HAM10000 dataset against a fixed ResNet-50; subsequently, performing external validation of the trained model using the ISIC 2019 dataset. The model's performance was assessed using precision, recall, the F1-score, and area under the precision-recall curve.</p><p><strong>Results: </strong>The developed skin lesion classification model demonstrated high accuracy and reliability across various types of skin lesions, achieving a weighted-average precision, recall, and F1-score of 0.918, 0.808, and 0.847, respectively. The model's discrimination performance was reflected in an average area under the curve (AUC) value of 0.943. Further external validation with the ISIC 2019 dataset confirmed the model's effectiveness, as shown by an AUC of 0.911.</p><p><strong>Conclusions: </strong>This study presents an optimized skin lesion classification model based on the DER algorithm, which shows high performance in disease classification with the potential to expand its classification range. The model demonstrated robust results in external validation, indicating its adaptability to new disease classes.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 2","pages":"140-146"},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955423","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":"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":"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":2.3,"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}