Healthcare Informatics Research最新文献

{"title":"Developing a Training Program for Healthcare Data Professionals in South Korea: A Needs Analysis.","authors":"Yeonju Kim, Yonghwan Moon, Jipmin Jung, Seungwon Jeung, Young-A Ji, Gwihyun Kim, Youngho Lee, Hyekyung Woo","doi":"10.4258/hir.2026.32.1.92","DOIUrl":"10.4258/hir.2026.32.1.92","url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of this study was to investigate practitioners' demands regarding curriculum programs and to suggest directions for future program development aimed at training healthcare data professionals.</p><p><strong>Methods: </strong>An online survey was conducted among 67 healthcare data practitioners who voluntarily participated in a training program designed to foster specialized professionals in healthcare data utilization on October 25, 2024. The collected data were analyzed using SPSS version 27.0.</p><p><strong>Results: </strong>The most common data-related issue encountered by practitioners was concern regarding security and privacy protection, which was reported by 49.3% of participants. With respect to areas requiring improvement in job roles, 34.3% of respondents identified healthcare data analysis and utilization as the most critical domain. Practitioners emphasized the importance of governance-related competencies, and there was a strong demand for education in research ethics and data review processes. In addition, statistically significant differences were observed among groups with respect to the levels of education required.</p><p><strong>Conclusions: </strong>Training programs designed to encourage professionals to utilize healthcare data should be structured as practice-oriented, hands-on programs that integrate both theoretical instruction and practical training. Furthermore, advanced education related to institutional review boards and data review boards should be incorporated as a core component of the curriculum. Finally, it is necessary to develop a comprehensive and detailed interdisciplinary educational program grounded in the five core competencies.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"92-100"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179184","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":"Construction and Application of a Biological Sample Library Information Management System Based on a Data Governance System in China.","authors":"Ke Wang, Xiaoying Fu, Yinghua Chen, Junjian Yu, Naicheng Xv, Jianqiao Peng, Kang Yang","doi":"10.4258/hir.2026.32.1.101","DOIUrl":"10.4258/hir.2026.32.1.101","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop and implement a hospital-based Biobank Information Management System (Version 1.0) within a structured data governance framework, and to evaluate its effectiveness in improving standardized biospecimen management, operational efficiency, and research support capacity in real-world clinical settings.</p><p><strong>Methods: </strong>A Biobank Information Management System (Version 1.0) was independently developed and deployed at Foshan Fosun Chancheng Hospital. The system incorporated data governance principles across the entire biospecimen lifecycle, including donor information management, sample collection, processing, storage, retrieval, and utilization. It successfully obtained national software copyright registration and was fully integrated into routine clinical and research workflows.</p><p><strong>Results: </strong>Post-implementation evaluation demonstrated a 67% reduction in manual operational workload, a 4.2-minute decrease in average sample retrieval time, and a data standardization rate of 90%. In addition, the system implemented role-based access control, standardized data dictionaries, audit trails, and full lifecycle traceability, thereby strengthening compliance with regulatory and ethical requirements. Collectively, these quantitative and functional outcomes indicate substantial improvements in workflow efficiency, data consistency, accuracy, and management transparency compared with traditional manual or semi-digital biobank operations.</p><p><strong>Conclusions: </strong>This study demonstrates that embedding data governance principles into the design and construction of a hospital-based biobank information system provides a sustainable, secure, and standardized approach to biospecimen management. The proposed system offers a practical and replicable reference for healthcare institutions seeking to modernize biobank infrastructure and strengthen biomedical research support.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"32 1","pages":"101-108"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179172","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 an Application for Communication between Rehabilitation Patients and Physicians Based on the Shared Decision-Making Model.","authors":"Jiyoung Kim, Yung Jin Lee, Suehyun Lee","doi":"10.4258/hir.2025.31.4.426","DOIUrl":"10.4258/hir.2025.31.4.426","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of this study was to develop a communication application for rehabilitation patients and physicians based on the shared decision-making (SDM) model. Specifically, an app called REHAB NOTE was designed and implemented for patients undergoing rehabilitation for cancer and central nervous system (CNS) injuries. The REHAB NOTE application aims to facilitate smooth communication between patients and physicians, provide patient-centered medical services, and ultimately enhance rehabilitation treatment effectiveness.</p><p><strong>Methods: </strong>The development of REHAB NOTE followed a structured approach for mobile app creation, including rigorous requirement analysis, architecture design, navigation design, and detailed page layout planning. This systematic process ensured the platform met the specific needs of both rehabilitation patients and healthcare providers.</p><p><strong>Results: </strong>We developed an application-based platform service (REHAB NOTE) that enables rehabilitation patients to view doctors' notes after treatment, document their health status, and share this information with their physicians. The platform was specifically designed for cancer rehabilitation patients and CNS injury rehabilitation patients. It can also be utilized by patients undergoing occupational, physical, and speech therapies.</p><p><strong>Conclusions: </strong>The REHAB NOTE application incorporates concepts from shared decision-making and OpenNotes and is anticipated to positively impact rehabilitation treatment outcomes. Future studies should verify the application's effectiveness. Additionally, modifications and enhancements will be necessary to ensure its applicability to a broader spectrum of rehabilitation patients.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 4","pages":"426-433"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563840","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-Based Death Prediction Model for Chronic Kidney Disease.","authors":"Hyeji Kim, Hyekyung Woo","doi":"10.4258/hir.2025.31.4.396","DOIUrl":"10.4258/hir.2025.31.4.396","url":null,"abstract":"<p><strong>Objectives: </strong>The prevalence of chronic kidney disease (CKD) continues to rise, making it one of the leading causes of death worldwide. Recent advances in medical and health research have progressed beyond traditional statistical methodologies, increasingly leveraging artificial intelligence to identify and predict factors influencing mortality. Further AI-based research is therefore essential to deepen understanding of the determinants of death among CKD patients.</p><p><strong>Methods: </strong>This study used data from the Korea Disease Control and Prevention Agency's in-depth survey of patients discharged between 2016 and 2021. Least absolute shrinkage and selection operator (LASSO) regression, a machine learning technique, was applied to identify significant factors associated with death in CKD patients. These selected variables were then incorporated into a deep learning-based predictive model.</p><p><strong>Results: </strong>Eight factors influencing death were identified, including length of hospital stay (coefficient = 0.023), emergency admission (0.016), age (0.013), severity-adjusted score (0.008), and regional differences (0.003). The developed deep learning model achieved a loss value of 0.1207 and an accuracy of 96.84%.</p><p><strong>Conclusions: </strong>This study identified emergency visits and prolonged hospital stays as key predictors of death in CKD patients. To mitigate these risks, regular monitoring by nephrology specialists and timely initiation of renal replacement therapy are essential. Age also emerged as a critical determinant, emphasizing the importance of age-stratified clinical guidelines amid global aging trends. The high-performing, simplified predictive model based on general characteristics offers a valuable tool for rapid prognosis assessment in primary and secondary healthcare settings.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 4","pages":"396-404"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563803","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":"Identifying Patterns of Depression Comorbidities Using Association Rule Learning: Insights from Maryland Medicaid Data.","authors":"Fei Han, Christine Gill, Elizabeth Blake, Ian Stockwell","doi":"10.4258/hir.2025.31.4.388","DOIUrl":"10.4258/hir.2025.31.4.388","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to identify association rules in patients with multiple chronic conditions, with a focus on patterns involving depression, a highly prevalent psychiatric disorder and a significant risk factor for suicide. Understanding comorbidity patterns in patients with depression is critical for targeting screening efforts, enabling early diagnosis, and improving chronic disease management.</p><p><strong>Methods: </strong>Maryland Medicaid claims data from 2021 to 2022 were analyzed to examine the co-occurrence of depression with 62 other chronic conditions using association rule learning. Analyses were stratified by sex and age group to identify patterns specific to demographic subgroups. Thresholds for case numbers and confidence levels were applied to ensure that identified rules were both clinically meaningful and statistically robust.</p><p><strong>Results: </strong>The study showed a marked increase in the number of association rules with advancing age, particularly among women compared to men. In total, 582 association rules were identified, providing important insights into comorbidity structures.</p><p><strong>Conclusions: </strong>This study demonstrates the utility of association rule learning for detecting clinically relevant patterns of depression comorbidities, including variations by age and sex. The identified rules could inform clinical practice by improving targeted screening, facilitating early diagnosis, and guiding management strategies for patients with multiple chronic conditions.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 4","pages":"388-395"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563830","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":"Role of Medical Editors in the Age of Generative Artificial Intelligence.","authors":"Sun Huh","doi":"10.4258/hir.2025.31.4.317","DOIUrl":"10.4258/hir.2025.31.4.317","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 4","pages":"317-319"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563991","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":"Care Robots for Community-Dwelling Older Adults: An Integrative Review.","authors":"Jisan Lee, Hyeongsuk Lee, Mona Choi, Jung A Kim","doi":"10.4258/hir.2025.31.4.347","DOIUrl":"10.4258/hir.2025.31.4.347","url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of this study was to conduct an integrative review of existing research on care robots for community-dwelling older adults and to suggest directions for future research and technology development in this area.</p><p><strong>Methods: </strong>We focused on robots, including care robots and socially assistive robots, that help older adults living in the community maintain independence at home. Three electronic academic databases (PubMed, CINAHL, and Cochrane) were searched for eligible research articles. The keywords used included elder*, older adult*, robot* care, care robot*, assist* robot*, service robot*, companion* robot*, socia* robot*, home-based, and community-based, among others.</p><p><strong>Results: </strong>A total of 834 research articles were identified, and 40 were ultimately reviewed and analyzed. The studies were categorized into three groups: perceptions and needs related to care robots; cognitive support; and assistance with activities of daily living.</p><p><strong>Conclusions: </strong>It is necessary to develop and implement care robots with diverse functions that can provide practical assistance for the independent daily living of older adults. This will require collaboration among government agencies, public institutions, academia, and private health enterprises. In addition, policies must be established to support the purchase and maintenance costs of care robots to ensure continued access for community-dwelling older adults.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 4","pages":"347-366"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563832","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":"Improving Online Drug Information: Insights from Quality Evaluation and Pharmaceutical System Design.","authors":"Dita Permatasari, Syofyan, Ardhian Agung Yulianto, Wirda Qholbya, Andhini Aurellyta Ridwan, Yoneta Srangenge","doi":"10.4258/hir.2025.31.4.405","DOIUrl":"10.4258/hir.2025.31.4.405","url":null,"abstract":"<p><strong>Objectives: </strong>The integration of digital technology has greatly expanded public access to health and drug-related information through the Internet. However, the rapid proliferation of unverified content on websites targeting the general population raises serious concerns about health misinformation. This study aimed to evaluate the quality of Indonesian drug information websites accessible to the public and to design a verified, web-based drug information system.</p><p><strong>Methods: </strong>A cross-sectional evaluation was conducted using the Quality Evaluation Scoring Tool (QUEST) to assess the quality of publicly available drug information websites in Indonesia. Development of the verified drug information platform followed the Rapid Application Development model, employing a prototyping approach.</p><p><strong>Results: </strong>Among the 14 publicly accessible drug information websites evaluated, 5 (35.71%) were classified as low quality (QUEST score ≤9), 4 (21.42%) as moderate quality (score 10-18), and 5 (35.71%) as high quality (score >18). The drug information website developed by the Faculty of Pharmacy, Universitas Andalas, achieved a high-quality rating, with a QUEST score of 27 (96.43%), although it received the lowest subscore in the Complementarity domain. Higher QUEST scores indicate better information quality.</p><p><strong>Conclusions: </strong>The findings show that nearly half of the websites providing drug information to the Indonesian public are of low quality. The website developed by the Faculty of Pharmacy, Universitas Andalas, demonstrated strong overall quality, but improvements in the Complementarity domain are recommended to further strengthen user engagement and support.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 4","pages":"405-415"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563843","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":"Utilization of Ontology to Develop Artificial Intelligence Systems in the Healthcare Industry.","authors":"Elahe Parsanasab, Alihasan Ahmadipour, Esmaeil Mehraeen","doi":"10.4258/hir.2025.31.4.320","DOIUrl":"10.4258/hir.2025.31.4.320","url":null,"abstract":"<p><strong>Objectives: </strong>Ontologies play a crucial role in healthcare systems due to the diversity of concepts, roles, users, and diagnostic and therapeutic methods. They facilitate the development of knowledge bases and the sharing and representation of information. With the integration of artificial intelligence (AI) into healthcare, ontologies can serve as complementary tools to enhance the quality of services.</p><p><strong>Methods: </strong>This review study examines existing research on the application of ontologies in AI systems within the healthcare industry. By analyzing their applications, benefits, challenges, and limitations, the study seeks to provide a deeper understanding of their impact on advancing AI technologies and improving healthcare processes. In addition, the study offers recommendations for strengthening the development and use of ontologies in intelligent healthcare systems.</p><p><strong>Results: </strong>The findings of this review indicate that ontologies enhance the accuracy of results and support medical decision-making by enabling the semantic exchange of diverse and heterogeneous data. They are essential for the development of decision support systems and for fostering intelligent interactions between patients and healthcare systems. Furthermore, ontologies contribute to healthcare decision-making by semantically analyzing the connections between diseases, geographic regions, and environmental factors.</p><p><strong>Conclusions: </strong>The use of ontologies in healthcare improves data analysis, patient diagnosis, treatment, and decision-making. Ontologies enhance data inference and interoperability in AI systems through data modeling, concept relationship extraction, knowledge enrichment, and information sharing. Given the vast scope of the healthcare domain, the diversity of specialties and data, and the absence of a dedicated ontology development methodology specific to this field, there is a clear need for a tailored and robust methodology.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 4","pages":"320-330"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640729/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563981","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":"Towards Efficient Patient Recruitment for Clinical Trials: Application of a Prompt-Based Learning Model.","authors":"Mojdeh Rahmanian, Seyed Mostafa Fakhrahmad, Seyedeh Zahra Mousavi","doi":"10.4258/hir.2025.31.4.367","DOIUrl":"10.4258/hir.2025.31.4.367","url":null,"abstract":"<p><strong>Objectives: </strong>All clinical trials face a significant bottleneck in identifying eligible participants, particularly due to the complexity of unstructured medical texts. Recent advances in natural language processing, especially the advent of transformer-based models, have shown promise in this domain. In this study, we evaluated the performance of a prompt-based large language model (LLM) for cohort selection from unstructured medical notes.</p><p><strong>Methods: </strong>Medical records were annotated with Med- CAT using the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) ontology. For each trial eligibility criterion, we extracted sentences containing relevant annotated concepts through an ontology-driven summarization process. These summaries were then input into a prompt-based LLM (GPT-3.5-turbo), tasked with classifying eligibility criteria in a zero-shot setting. Model performance was assessed using the 2018 National Natural Language Processing Clinical Challenges (n2c2) dataset, which required the classification of 288 patients' medical records according to 13 eligibility criteria.</p><p><strong>Results: </strong>The proposed prompt-based model achieved overall micro and macro F-measures of 0.9061 and 0.8060, respectively-among the highest scores reported for this dataset.</p><p><strong>Conclusions: </strong>Our results demonstrate that integrating ontology-based extractive summarization with prompt-based LLMs can substantially improve eligibility classification. The summarization step enhanced model focus and interpretability, particularly for long or ambiguous narratives. This pipeline offers a scalable and adaptable framework for clinical trial automation and has the potential for real-world integration with electronic medical record matching systems.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 4","pages":"367-377"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563951","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}