Objective: "Join," an imaging technology-based telemedicine system, allows simultaneous radiological information sharing between physically remote institutions, virtually connecting advanced medical institutions and rural hospitals. This study aimed to elucidate the health economics effect of Join for neurological telemedicine in rural areas in Hokkaido, Japan.
Methods: Information concerning 189 requests for patient transfer from Furano Kyokai Hospital, a regional rural hospital, to Asahikawa Medical University Hospital (AMUH), an advanced academic medical institution, was retrospectively collected. The Join system was established between Furano Kyokai Hospital and AMUH in February 2019. Data collected from patients between April 2017 and December 2018 were included in the non-Join group, and those collected between February 2019 and October 2020 were included in the Join group. Clinical variables, reasons for patient transfer requests, duration of hospital stay, and medical costs per patient were analyzed between these two groups. Furthermore, clinical characteristics were compared between patients who were transferred and not transferred based on Join.
Results: More patients were discharged < 7 days after transfer to AMUH in the non-Join group compared with the Join group (p = 0.02). When focusing on the Join group, more patients who were not transferred were discharged < 1 week (p < 0.01). On the other hand, more patients required surgery (p = 0.01) when transferred. The ratio of patients whose medical cost was < USD5000 substantially decreased, from 33% for the non-Join group to 13% for the Join group.
Conclusions: An imaging technology-based telemedicine system, Join, contributed to reducing unnecessary neuro-emergency patient transfer in a remote rural area, and telemedicine with an integrated smartphone system allowed medical personnel to effectively triage at a distance neuro-emergency patients requiring advanced tertiary care.
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http://dx.doi.org/10.3171/2022.3.FOCUS228 | DOI Listing |
BMC Med Educ
December 2024
Department of Medical Education, Texas Tech University Health Sciences Center, 3601 4th St. STOP, Lubbock, TX, 79430-6525, USA.
Background: Point-of-care ultrasound (POCUS) education has become an essential component of medical school curricula. Ultrasound represents a highly effective teaching modality to reinforce anatomical knowledge gained during cadaveric dissections. At Texas Tech University Health Sciences Center-School of Medicine (TTUHSC-SOM), POCUS was incorporated into the pre-clerkship curriculum especially during the first year of medical school anatomy course.
View Article and Find Full Text PDFZhongguo Yi Liao Qi Xie Za Zhi
November 2024
Department of Medical Equipment, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233.
Nanophotonics
September 2024
National Mobile Communications Research Laboratory, School of Information Science and Engineering, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 210096, China.
Due to signal shielding caused by building structures, conventional mature positioning technologies such as the Global Positioning System (GPS) are only suitable for outdoor navigation and detection. However, there are many scenarios that urgently require high-precision indoor positioning technologies, such as indoor wireless optical communications (OWCs), navigation in large buildings, and warehouse management. Here, we proposed a millimeter-precision indoor positioning technology based on metalens-integrated camera, which determines the position of the device through imaging of beacon LEDs.
View Article and Find Full Text PDFFront Plant Sci
November 2024
College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan, China.
Wheat, being a crucial global food crop, holds immense significance for food safety and agricultural economic stability, as the quality and condition of its grains are critical factors. Traditional methods of wheat grain detection are inefficient, and the advancements in deep learning offer a novel solution for fast and accurate grain recognition. This study proposes an improved deep learning model based on YOLOv8n, referred to as YOLO-SDL, aiming to achieve efficient wheat grain detection.
View Article and Find Full Text PDFFront Neurorobot
November 2024
School of Humanities and International Education, Xi'an Peihua University, Xi'an, Shaanxi, China.
Introduction: With the development of globalization and the increasing importance of English in international communication, effectively improving English writing skills has become a key focus in language learning. Traditional methods for English writing guidance and error correction have predominantly relied on rule-based approaches or statistical models, such as conventional language models and basic machine learning algorithms. While these methods can aid learners in improving their writing quality to some extent, they often suffer from limitations such as inflexibility, insufficient contextual understanding, and an inability to handle multimodal information.
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