Innovation in surgery/operating room driven by Internet of Things on medical devices

2019 ◽  
Vol 33 (10) ◽  
pp. 3469-3477 ◽  
Author(s):  
Yuki Ushimaru ◽  
Tsuyoshi Takahashi ◽  
Yoshihito Souma ◽  
Yoshitomo Yanagimoto ◽  
Hirotsugu Nagase ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Operating Room ◽  
Medical Devices

scholarly journals Medical Device Integration Model Based on the Internet of Things

2015 ◽  
Vol 9 (1) ◽  
pp. 256-261 ◽  
Author(s):  
Aiyu Hao ◽  
Ling Wang
Keyword(s):  
Information System ◽  
Internet Of Things ◽  
Medical Device ◽  
Medical Devices ◽  
Medical Information ◽  
Medical Information System ◽  
The Internet ◽  
Model Based ◽  
Device Integration ◽  
The Internet Of Things

At present, hospitals in our country have basically established the HIS system, which manages registration, treatment, and charge, among many others, of patients. During treatment, patients need to use medical devices repeatedly to acquire all sorts of inspection data. Currently, the output data of the medical devices are often manually input into information system, which is easy to get wrong or easy to cause mismatches between inspection reports and patients. For some small hospitals of which information construction is still relatively weak, the information generated by the devices is still presented in the form of paper reports. When doctors or patients want to have access to the data at a given time again, they can only look at the paper files. Data integration between medical devices has long been a difficult problem for the medical information system, because the data from medical devices lack mandatory unified global standards and have outstanding heterogeneity of devices. In order to protect their own interests, manufacturers use special protocols, etc., thus causing medical devices to still be the "lonely island" of hospital information system. Besides, unfocused application of the data will lead to failure to achieve a reasonable distribution of medical resources. With the deepening of IT construction in hospitals, medical information systems will be bound to develop toward mobile applications, intelligent analysis, and interconnection and interworking, on the premise that there is an effective medical device integration (MDI) technology. To this end, this paper presents a MDI model based on the Internet of Things (IoT). Through abstract classification, this model is able to extract the common characteristics of the devices, resolve the heterogeneous differences between them, and employ a unified protocol to integrate data between devices. And by the IoT technology, it realizes interconnection network of devices and conducts associate matching between the data and the inspection with the terminal device in a timely manner.

scholarly journals Prototype Sistem Monitoring Infus Berbasis IoT (Internet of Things)

2020 ◽  
Vol 4 (2) ◽  
pp. 155-163
Author(s):  
Taufik Akbar ◽  
◽  
Indra Gunawan ◽  
Keyword(s):  
Internet Of Things ◽  
Medical Devices ◽  
Science And Technology ◽  
Medical Science ◽  
The Internet ◽  
Patient Room ◽  
The World ◽  
Weight Sensor ◽  
The Internet Of Things ◽  
Control Infusion

The development of increasingly sophisticated medical science and technology has an impact on the development of science and technology in the field of medical-devices. One of the existing equipment and is often used in hospitals, one of which is an IV. Currently in the world of health, infusion is still controlled manually. Because it takes time if the nurse has to go back and forth throughout the patient room. Not only is it time consuming, but there will be risks if it is too late to treat a patient whose infusion has run out. Technology needs to be used to minimize risks in the medical world, one of which is the application of IoT technology. This study aims to make it easier for nurses to control infusion conditions in real time using the concept of IoT ( the Internet of Things). The method used is the Waterfall method. This research uses hardware consisting of Load Cell with the HX711 module as a weight sensor, NodeMCU V3 as a processor, and Thingspeak Web server as the interface with the user. The results of the measurement of the tool made have an error of 0.25 Gram, sending data to the Thingspeak.com Server requires a good connection for maximum results.

scholarly journals Bio contamination of surfaces and medical devices in controlled environment areas at a hospital in Morocco

2021 ◽  
Vol 319 ◽  
pp. 01080
Author(s):  
Samira Jaouhar ◽  
Abdelhakim El Ouali Lalami ◽  
Khadija Ouarrak ◽  
Jawad Bouzid ◽  
Mohammed Maoulouaa ◽  
...  
Keyword(s):  
Operating Room ◽  
Medical Devices ◽  
Medical Equipment ◽  
Microbiological Quality ◽  
Hospital Environment ◽  
P Value ◽  
Controlled Environment ◽  
Coagulase Negative Staphylococci ◽  
Burn Unit ◽  
Sterilization Service

The hospital environment, especially medical devices and surfaces, represents a secondary reservoir for pathogens. This work aims to evaluate the microbiological quality of surfaces and medical equipment of controlled environment services (burn unit, operating room, and sterilization service) at a hospital in Meknes (center of Morocco). This study was carried out for three months (September-December of 2017). A total of 63 samples were taken by swabbing technique from different surfaces and medical equipment after bio-cleaning. Identification was performed according to conventional bacteriological methods and by microscopic observation for fungi. The study showed that 68% of the surface was contaminated. The operating room recorded a rate of 93% of contamination (p-value <0.01), 83% for sterilization service, and 47% for burn unit. A percentage of 67% of the isolates were identified as Gram-positive bacteria against 32% Gram-negative bacteria (p-value <0.05). Bacterial identification showed Coagulase-negative Staphylococci (45%), Enterobacter cloacae (14%), Micrococcus sp (10%), Klebsiella pneumoniae, peptostreptococcus sp and Pseudomonas fluorescens (7% for each one), Escherichia coli, and Methicillin-resistant Staphylococcus aureus (5% for each one). These results require corrective action represented by rigorous cleaning and disinfection procedures.

scholarly journals Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 157
Author(s):  
Nirmala Devi Kathamuthu ◽  
Annadurai Chinnamuthu ◽  
Nelson Iruthayanathan ◽  
Manikandan Ramachandran ◽  
Amir H. Gandomi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Internet Of Things ◽  
Medical Devices ◽  
Data Transmission ◽  
Privacy Preservation ◽  
Communication Overhead ◽  
Q Learning ◽  
External Threats ◽  
Preservation Method

The healthcare industry is being transformed by the Internet of Things (IoT), as it provides wide connectivity among physicians, medical devices, clinical and nursing staff, and patients to simplify the task of real-time monitoring. As the network is vast and heterogeneous, opportunities and challenges are presented in gathering and sharing information. Focusing on patient information such as health status, medical devices used by such patients must be protected to ensure safety and privacy. Healthcare information is confidentially shared among experts for analyzing healthcare and to provide treatment on time for patients. Cryptographic and biometric systems are widely used, including deep-learning (DL) techniques to authenticate and detect anomalies, andprovide security for medical systems. As sensors in the network are energy-restricted devices, security and efficiency must be balanced, which is the most important concept to be considered while deploying a security system based on deep-learning approaches. Hence, in this work, an innovative framework, the deep Q-learning-based neural network with privacy preservation method (DQ-NNPP), was designed to protect data transmission from external threats with less encryption and decryption time. This method is used to process patient data, which reduces network traffic. This process also reduces the cost and error of communication. Comparatively, the proposed model outperformed some standard approaches, such as thesecure and anonymous biometric based user authentication scheme (SAB-UAS), MSCryptoNet, and privacy-preserving disease prediction (PPDP). Specifically, the proposed method achieved accuracy of 93.74%, sensitivity of 92%, specificity of 92.1%, communication overhead of 67.08%, 58.72 ms encryption time, and 62.72 ms decryption time.

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