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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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A Secure and Fault-Tolerant Architecture for LoRaWAN Based on Blockchain

A Secure and Fault-Tolerant Architecture for LoRaWAN Based o...

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Cyber Security in Networking Conference (CSNet)

作者： Victor Ribeiro Raimir Holanda Filho Alex Ramos Graduate Program in Applied Informatics University of Fortaleza (UNIFOR) Fortaleza Brazil Computer Science Department Federal Institute of Ceará (IFCE) Fortaleza Brazil

ISBN: (数字)9781728139494

ISBN: (纸本)9781728139500

The Internet of Things (IoT) paradigm aims to bring continuous interaction between small embedded devices and humans. The IoT has the potential to affect our daily lives and bring many benefits to society. Low-Power Wide-Area Networks (LPWAN) is a new IoT technology that offers long distance connectivity for a massive number of devices. LPWAN is a promising solution to enable complex IoT scenarios, such as smart cities and smart healthcare. LoRa is currently one of the leading LPWAN solutions available for public use. Due to the great number of connected devices and, in some cases, sensitive data transmitted in IoT networks, security is one of the main concerns in LPWAN. In this paper, we focus on the issues of key management in LoRaWAN. We propose a secure architecture for key management based on private blockchain and smart contracts in order to increase the levels of security and availability in LoRaWAN environments. In order to show the feasibility of the proposed architecture, a working prototype was implemented using open-source tools and commodity hardware.

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Feasibility study of Information Technology Investment (a case study of ODOO ERP: Project Management Module Implementation in Indonesia Based Company)

Feasibility study of Information Technology Investment (a ca...

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International Conference on ICT For Smart Society (ICISS)

作者： Fredy Jingga Raymond Kosala Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Doctor of Computer Science Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit / Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical.

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Impact of Intention to Use to a Document Management System (Case Study Approach) 3

Impact of Intention to Use to a Document Management System (...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Hartono, Sugiarto Kosala, Raymondus Raymond Supangkat, Suhono Harso Ranti, Benny Information Systems Department Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781538658215

Document management system makes user to access information anytime and anywhere. The purpose of this research is to analyze what variables have impact on the intention to use of document management system. To achieve the objectives conducted by identification problem on the research model developed from the model of Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology to the use of document management system. Furthermore, the data obtained from the respondents tested outer model, inner model test, hypothesis test and to produce a comprehensive analysis will be conducted Importance Performance Analysis. This research will result in a comprehensive analysis and able to provide recommendations and solutions for improvement from the implementation DMS has been done earlier. © 2018 IEEE.

关键词： Information services

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Corrigendum to "Novel model of multiple sclerosis induced by EBV-like virus generate a unique B cell population" [Journal of Neuroimmunology volume 394 (2024) 578408]

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Journal of neuroimmunology 2025年 404卷 578625页

作者： J L Deffenbaugh K-J Jung S P Murphy Y Liu C N Rau C Petersen-Cherubini P L Collins D Chung A E Lovett-Racke Department of Microbial Infection & Immunity The Ohio State University Wexner Medical Center USA. Department of Computer Science and Engineering The Ohio State University USA. Department of Microbial Infection & Immunity The Ohio State University Wexner Medical Center USA Neuroscience Graduate Program The Ohio State University USA. Department of Biomedical Informatics The Ohio State University Wexner Medical Center USA Pelotonia Institute for Immuno-Oncology The Ohio State University Wexner Medical Center USA. Department of Neuroscience The Ohio State University Wexner Medical Center USA. Electronic address: amylovettracke@***.

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Big Data Governance for Building A Smart Cities

Big Data Governance for Building A Smart Cities

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International Conference on ICT For Smart Society (ICISS)

作者： Novan Zulkarnain Raymond Kosala Benny Ranti Suhono Harso Supangkat Computer Science Department BINUS Graduate Program Doctor of Computer Science Information Systems Department Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Computer Science Department Universitas Indonesia Depok Indonesia School of Electrical and Informatics Engineering Institute of Technology Bandung Bandung Indonesia

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data.

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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Turning social news curation into microtask crowdsourcing: a vision and research agenda

Turning social news curation into microtask crowdsourcing: a...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Daniel Schneider António Correia Ramon Chaves Ana Paula Pimentel Marcio Antelio Edson Mello Lucas Marcos Antonio de Almeida Luiz Oliveira Jano Moreira de Souza Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Graduate School of Engineering CEFET-RJ Rio de Janeiro Brazil Polytechnic Institute IPRJ/UERJ Rio de Janeiro Brazil Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Federal Institute of Rio de Janeiro (IFRJ) Rio de Janeiro Brazil

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Over the past decade, online crowdsourcing has established itself as an emerging paradigm that industry and government have been using to harness the cognitive abilities of a multitude of users distributed around the world. In this context, microtask crowdsourcing has become the method of choice for addressing a wide range of diverse problems. Microtasks typically require a minimum of time and cognitive effort, but combined individual efforts have made it possible to accomplish great achievements. The goal of this paper is to contribute to the ongoing effort of understanding whether the same success that microtask crowdsourcing has achieved in other domains can be obtained in the field of social news curation. In particular, we ask whether it is possible to turn online news curation, typically a social and collaborative activity on the Web, into a model in which curatorial activities are mapped into microtasks to be performed by a crowd of online users.

关键词： Crowdsourcing Industries Conferences Government Collaboration Turning Cybernetics

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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

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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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