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Background: In recent years, health care has undergone a rapid and unprecedented digital transformation. In many fields of specialty care, such as rheumatology, this shift is driven by the growing number of patients and limited resources, leading to increased use of digital health technologies (DHTs) to maintain high-quality clinical care. Previous studies examined user acceptance of individual DHTs in rheumatology, such as telemedicine, video consultations, and mHealth. However, it is essential to conduct cross-technology and continuous analyses of user acceptance and DHT use to maximize the benefits for all relevant stakeholders.
Objective: This study aimed to explore the current acceptance, use, and preferences regarding DHTs among patients in rheumatology care in Germany.
Methods: Rheumatology patients from 3 clinics in Germany were surveyed to understand their perspectives on DHTs. The survey included main themes, including acceptance, preferences, COVID-19's impact, potential, and barriers related to DHTs. The data were analyzed using descriptive statistics and correlation analysis.
Results: Out of 337 participants, 53% (179/337) reported using DHTs. Specific technologies included wearables (72/337, 21%), mHealth apps (71/337, 21%), digital therapeutics (32/337, 9%), electronic prescriptions (30/337, 9%), video consultations (15/337, 4%), and at-home blood self-sampling (3/337, 1%). Nearly two-thirds (220/337, 65%) found DHTs useful, and 69% (233/337) held a generally positive attitude toward DHTs. Attitudes shifted positively during the COVID-19 pandemic for 40% (135/337) of participants. Higher education was more prevalent among DHT users (114/179, 63.7%) compared with nonusers (42/151, 27.8%; P=.02). The main potential benefits identified were location-independent use (244/337, 72%) and time-independent use (216/337, 64%). Key barriers included insufficient user knowledge (165/337, 49%) and limited information on DHTs (134/337, 40%).
Conclusions: Patient acceptance and use of DHTs in rheumatology is increasing in Germany. A prospective, standardized monitoring of digital transformation in rheumatology care is highly needed.
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http://dx.doi.org/10.2196/52601 | DOI Listing |
Int J Med Inform
September 2025
School of Psychology & Public Health, La Trobe University, Melbourne, Victoria, Australia.
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Methods: A systematic web-based review was conducted across all 42 Australian universities, drawing on publicly available resources including official handbooks, course catalogues, and subject guides.
Pestic Biochem Physiol
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National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Key Laboratory of Agricultural Environment in Universities of Shandong, College of Resources and Environment, Shandong Agricultural University, 61 Daizong Road, Tai'an 271018, PR China. Electronic address: wj
Difenoconazole (DFC) is a commonly used triazole fungicide known for its high efficiency and environmental persistence. A thorough understanding of its environmental behavior, particularly sorption in soil, is critical to obtain a comprehensive assessment of the ecological risk of DFC. In this study, three soils with distinct physicochemical properties (brown soil, cinnamon soil, and fluvo-aquic soil) were used to elucidate the adsorption mechanisms of DFC on soil.
View Article and Find Full Text PDFMicrobes Infect
September 2025
Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland; ESCMID study group on Molecular Diagnostics and Genomics. Electronic address:
Rapid advancements in artificial intelligence (AI) and machine learning (ML) offer significant potential to transform medical microbiology diagnostics, improving pathogen identification, antimicrobial susceptibility prediction and outbreak detection. To address these opportunities and challenges, the ESCMID workshop, "Artificial Intelligence and Machine Learning in Medical Microbiology Diagnostics", was held in Zurich, Switzerland, from June 2-5, 2025. The course featured expert lectures, practical sessions and panel discussions covering foundational ML concepts and deep learning architectures, data interoperability, quality control processes, model development and validation strategies.
View Article and Find Full Text PDFISA Trans
August 2025
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, PR China; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China. Electronic addr
With the deep digital transformation of traditional manufacturing industry and the continuous automation level improvement of production lines, it is more important to predict the Key Performance Indicators (KPIs) of processes in a timely and accurate manner. The traditional laboratory destructive test method for obtaining KPIs consumes a large amount of time and incurs high costs, which not only fails to provide timely and effective guidance for production processes but also results in significant losses for manufacturing enterprises. To address these issues, an online prediction soft sensor model for KPIs based on a serial-parallel gated recurrent unit with self-attention mechanism (SPGRU-SA) soft sensor model is proposed.
View Article and Find Full Text PDFHealth Serv Res
September 2025
Division of Clinical Informatics and Digital Transformation, Director, Center for Clinical Informatics and Improvement Research, University of California - San Francisco, San Francisco, CA, San Francisco, California, USA.
Objective: To analyze national rates of team-based ordering and evaluate changes in key outcomes following adoption.
Study Setting And Design: We conducted an observational pre-post intervention-comparison study of 249,463 ambulatory physicians across 401 organizations using the Epic EHR. Our intervention was the adoption of team-based ordering, measured as the proportion of orders involving team support.