Abstract
In recent years, the healthcare system, along with the technology that surrounds it, has become a sector in much need of development. It has already improved in a wide range of areas thanks to significant and continuous research into the practical implications of biomedical and telemedicine studies. To ensure the continuing technological improvement of hospitals, physicians now also must properly maintain and manage large volumes of patient data. Transferring large amounts of data such as images to IoT servers based on machine-to-machine communication is difficult and time consuming over MQTT and MLLP protocols, and since IoT brokers only handle a limited number of bytes of data, such protocols can only transfer patient information and other text data. It is more difficult to handle the monitoring of ultrasound, MRI, or CT image data via IoT. To address this problem, this study proposes a model in which the system displays images as well as patient data on an IoT dashboard. A Raspberry Pi processes HL7 messages received from medical devices like an ultrasound machine (ULSM) and extracts only the image data for transfer to an FTP server. The Raspberry Pi 3 (RSPI3) forwards the patient information along with a unique encrypted image data link from the FTP server to the IoT server. We have implemented an authentic and NS3-based simulation environment to monitor real-time ultrasound image data on the IoT server and have analyzed the system performance, which has been impressive. This method will enrich the telemedicine facilities both for patients and physicians by assisting with overall monitoring of data.
Funder
King Saud University, Riyadh, Saudi Arabia
Subject
Health Information Management,Health Informatics,Health Policy,Leadership and Management
Reference26 articles.
1. Khan, I.I., Ahmed, M., and Ahmad, K.U. (2018, January 8–10). Towards A Smart Hospital: Automated Non-Invasive Patient’s Discomfort Detection in Ward Using Overhead Camera. Proceedings of the 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Columbia University, NY, USA.
2. Optimizing analysis, visualization, and navigation of large image data sets: One 5000-section CT scan can ruin your whole day;Andriole;Radiology,2011
3. COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images;Muhammad;Inf. Fusion,2021
4. Computer aided abnormality detection for kidney on FPGA based IoT enabled portable ultrasound imaging system;Krishna;Irbm,2016
5. SEAIoT: Scalable e-health architecture based on Internet of things;Said;Int. J. Comput. Appl.,2012
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