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Reducing EHR navigation time with an advanced hyperlipidemia management Tool: An evaluation of efficiency

International Journal of medical informatics 2025年

作者： Chin-Chen Chen Yu-Chen Liu Guan-Ling Lin Annisa Ristya Rahmant Der-Ming Liou Hsuan-Chia Yang Information Technology Department Cardinal Tien Hospital New Taipei City Taiwan Information Technology Department Yonghe Cardinal Tien Hospital New Taipei City Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan School of Nursing College of Medicine National Taiwan University Taipei Taiwan Department Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science Faculty of Science and Technology Middlesex University London UK Master Program in Medical Informatics National Tsing Hua University Hsinchu City Taiwan International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan

Background Effective management of hyperlipidemia is crucial for preventing cardiovascular diseases. However, traditional monitoring systems often lack efficiency, particularly in navigating electronic health records (EHRs) to identify and track at-risk patients. Objective This study aims to evaluate the impact of a Hyperlipidemia Medication Reminder System (HLD-MRS) implemented in a district hospital to streamline EHR navigation and enhance patient management. Methods A pre-post prospective study was conducted at a district hospital. This study designed the HLD-MRS to integrate patient data, guideline recommendations, and risk factor assessments into a single interface, simplifying EHR navigation. This study compared EHR navigation times and patient tracking rates before and after HLD-MRS implementation across the Cardiology, Endocrinology, and Family Medicine departments. Results Among 750 patients with follow-up appointments, 334 were identified as having HLD or being at high-risk status. The HLD-MRS significantly reduced mean EHR navigation time from 83.9 s (SD = 8.6) to 53.8 s (SD = 6.6) across all departments (p < 0.001). The proportion of actively monitored HLD patients increased from 42.5 % to 56.6 %, while cases with unknown or withheld assessment status decreased from 57.5 % to 28.4 %. Conclusion The HLD-MRS improved efficiency in EHR navigation and increased the frequency of active patient monitoring. This guideline-driven decision support system shows promise for enhancing hyperlipidemia care in high-volume outpatient settings.

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Healthcare strategies and initiatives about COVID19 in Pakistan: Telemedicine a way to look forward

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Computer Methods and programs in Biomedicine Update 2021年 1卷 100008页

作者： Humayun, Ayesha Shahabuddin, Syed Afzal, Saira Malik, Ahmad Azam Atique, Suleman Iqbal, Usman Master Program in Global Health & Development PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan Department of public health and community medicine Shaikh Zayed Federal Post Graduate Medical Institute Shaikh Zayed Medical Complex Lahore Pakistan Shaikh Khalifa Bin Zayed Al-Nahyan Medical and Dental College Shaikh Zayed Medical Complex Lahore Pakistan Dept. of Surgery Aga Khan University Hospital Karachi Pakistan Dept. of Public Health and Preventive Medicine Dept. of Community Medicine and Epidemiology King Edward Medical University Lahore Pakistan Department of Family and Community Medicine Faculty of Medicine in Rabigh King Abdul Aziz University Saudi Arabia Institute of Public Health The University of Lahore Pakistan Department of Health Informatics College of Public Health and Health Informatics University of Ha'il Saudi Arabia

The COVID-19 pandemic is one of the unprecedented devastating catastrophes with severe public health threat globally. Low and middle income countries (LMICs) are trying hard to cope with the rapidly changing global scenario and trying to mitigate this double crisis of pandemic and economic recession. This pandemic, has led to major changes in global and regional health care delivery proceedings with surge in telemedicine to provide the required services and also giving priority to control the disease spread. © 2021

关键词： COVID-19 Global pandemic Guidelines Pakistan Public health Strategies Telemedicine

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Corrigendum to "Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study" [Asia-Pacific J Oncol Nurs 10 (2023) 100195]

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Asia-Pacific journal of oncology nursing 2024年第5期11卷 100437页

作者： Shuo-Chen Chien Chun-You Chen Chia-Hui Chien Usman Iqbal Hsuan-Chia Yang Huei-Chia Hsueh Shuen-Fu Weng Wen-Shan Jian Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Artificial Intelligence Research and Development Center Wan Fang Hospital Taipei Medical University Taipei Taiwan. International Center for Health Information and Technology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Radiation Oncology Wan Fang Hospital Taipei Medical University Taipei Taiwan. Office of Public Affairs Taipei Medical University Taipei Taiwan. Health ICT Department of Health Tasmania Australia. Global Health and Health Security Department College of Public Health Taipei Medical University Taipei Taiwan. Department of Artificial Intelligence in Medicine Professional Master Program Taipei Medical University Taipei Taiwan. Department of Pharmacy Taipei Veterans General Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. School of Gerontology Health Management College of Nursing Taipei Medical University Taipei Taiwan. Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan. Graduate Institute of Data Science Taipei Medical University Taipei Taiwan.

[This corrects the article DOI: 10.1016/***.2023.100195.].

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Acuity of Illness at Initial Presentation Mediates Early Mortality in Adolescent and Young Adult Leukemia

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Blood 2024年 144卷 2347-2347页

作者： Tarun Jain Xu Ji Nicholas Degroote Alexandra Himes Cortland Coxhead Rebecca Williamson Lewis Anjali Khanna Alexandra Cathcart Tamara P. Miller Kristie A. Blum Keiko Tarquinio Sharon M. Castellino Department of Hematology and Medical Oncology Emory University School of Medicine Atlanta GA Department of Pediatrics Division of Hematology Oncology and Bone Marrow Transplant Emory University School of Medicine Atlanta GA Aflac Cancer and Blood Disorders Center Children's Healthcare of Atlanta Atlanta GA Centers for Disease Control and Prevention Atlanta GA Department of Pediatrics Division of Critical Care Medicine Emory University School of Medicine Atlanta GA Department of Pediatrics Emory University School of Medicine Atlanta GA The Children's Hospital of Philadelphia Philadelphia PA Winship Cancer Institute of Emory University Atlanta GA Department of Healthcare Leadership and Management Master of Science in Health Informatics Program Medical University of South Carolina Charleston SC Department of Pediatrics Division of Hematology Oncology and Bone Marrow Transplant Emory University School of Medicine Decatur GA

Background: Age-related disparities in outcomes for pediatric and adolescent leukemia are multifactorial. While the prevalence of pre-existing medical morbidities is low in children and adolescents, cancer-associated organ dysfunction leading to increased acuity of illness at initial presentation has been noted in up to 24% of those with pediatric leukemia. We have noted an association between adolescent and young adult (AYA) age group and acuity of illness at initial presentation (Jain et al, Blood , 2023). The current study aims to examine (1) the association between acuity of illness and overall survival, and (2) whether acuity of illness mediates age-related differences in overall survival. Methods: We conducted a retrospective analysis of 688 patients aged 1-21 years who presented to Children's Healthcare of Atlanta between 2010-2018 with any newly diagnosed leukemia, including B-cell acute lymphoblastic leukemia (ALL), T-cell ALL, acute myeloid leukemia, and other leukemias (chronic myeloid leukemia, juvenile myelomonocytic leukemia, Burkitt's leukemia). We excluded patients who lacked information in the first 72 hours following initial presentation to the hospital due to incomplete records or initial treatment at another facility. Based on manual chart review, high acuity of illness (yes/no) was defined as any ICU admission or ICU-level resource utilization by organ system (i.e., cardiovascular, respiratory, renal, hematologic, and neurologic), regardless of physical location in the hospital. We assessed overall survival with Kaplan-Meier analysis, with censoring defined by date at last follow-up, 5 years post-diagnosis, or December 31, 2020, whichever came first; the analysis was stratified by acuity of illness (high versus low) at initial presentation. Associations between age at presentation and survival were examined using the Cox proportional hazards model, controlling for sex assigned at birth, race/ethnicity, insurance status at presentation, and type

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

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Journal of medical Internet research 2021年第7期23卷 e31085页

作者： Cheng-Sheng Yu Shy-Shin Chang Tzu-Hao Chang Jenny L Wu Yu-Jiun Lin Hsiung-Fei Chien Ray-Jade Chen Department of Family Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Department of Family Medicine Taipei Medical University Hospital Taipei Taiwan. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan. Division of Plastic Surgery Department of Surgery Taipei Medical University Hospital and School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Division of General Surgery Department of Surgery Taipei Medical University Hospital Taipei Taiwan.

[This corrects the article DOI: 10.2196/27806.].

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Correction: How Can Artificial Intelligence Make Medicine More Preemptive?

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Journal of medical Internet research 2020年第8期22卷 e23645页

作者： Usman Iqbal Leo Anthony Celi Yu-Chuan Jack Li Master Program in Global Health & Development PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan. International Center for Health Information Technology Taipei Medical University Taipei Taiwan. Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States. Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center Boston MA United States. Department of Biostatistics Harvard TH Chan School of Public Health Harvard University Boston MA United States. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Dermatology Taipei Municipal Wan-Fang Hospital Taipei Taiwan. International Medical Informatics Association Geneva Switzerland.

[This corrects the article DOI: .].

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Meteorological influences on dengue transmission in Pakistan

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Asian Pacific Journal of Tropical Medicine 2016年第10期9卷 932-938页

作者： Suleman Atique Shabbir Syed Abdul Chien-Yeh Hsu Ting-Wu Chuang Graduate Institute of Biomedical Informatics College of Medical Science and TechnologyTaipei Medical University Master Program in Global Health and Development Taipei Medical University Department of Information Management National Taipei University of Nursing and Health Sciences Department of Molecular Parasitology and Tropical Diseases School of MedicineCollege of MedicineTaipei Medical University

Objective: To identify the influences of local and regional climate phenomena on dengue transmission in Lahore District of Pakistan, from 2006 to 2014. Methods: Time-series models were applied to analyze associations between reported cases of dengue and climatic parameters. The coherence trend of regional climate phenomena(IOD and ENSO) was evaluated with wavelet analysis. Results: The minimum temperature 4 months before the dengue outbreak played the most important role in the Lahore District(P=0.03). A NINO 3.4 index 9 months before the outbreaks exhibited a significant negative effect on dengue transmission(P=0.02). The IOD exhibited a synchronized pattern with dengue outbreak from 2010 to 2012. The ENSO effect(NINO 3.4 index) might have played a more important role after 2012. Conclusions: This study provides preliminary results of climate influences on dengue transmission in the Lahore District of Pakistan. An increasing dengue transmission risk accompanied by frequent climate changes should be noted. Integrating the influences of climate variability into disease prevention strategies should be considered by public health authorities.

关键词： Dengue Fever Pakistan Climate IOD NINO 3.4

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Organizational Benefits of Computerized Physician Order Entry (CPOE) System in Pakistan

Organizational Benefits of Computerized Physician Order Entr...

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International Conference on Platform Technology and Service (PlatCon)

作者： Suleman Atique Yen-Liang Lee Chien-Yeh Hsu Shabbir Syed-Abdul Hsiao-Hsien Rau Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei TW Master program in global health and development National Taipei University of Nursing and Health Sciences Taipei TW

Electronic prescribing i.e. e-prescribing is also known as Computerized Physician Order Entry (CPOE). It is a computer-aided system which offers the health professionals a robust platform for entering the prescription, recommended-tests, suggestions, etc. for the patients electronically rather than manually. Due to paucity of below- normal healthcare infrastructure and facilities in Pakistan which are available around the world in this modern world, there is an observable overburden on the health professionals and practitioners. Computerized Physician order entry (CPOE) system has shown to be very effective in minimizing the medication errors. Majority of the health professionals are of the view that CPOE is beneficial for both patient as well as health professionals themselves. They have shown a great deal of interest in the adoption of this new technology oriented system in our healthcare system. The results state clearly that this system is equally beneficial for organizations who want to adopt this system as perceived by the health professionals. Overall it supports the idea of adoption and implementation of CPOE in healthcare facilities in both public and private healthcare institutes. CPOE must be adopted to ease the healthcare system for patients as well as support healthcare professionals.

关键词： medical services Organizations Biomedical informatics Market research Information management Writing

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