Intelligent Transit Healthcare Schema Using Internet of Medical Things (IoMT) Technology for Remote Patient Monitoring

Author(s):  
R. J. S. Jeba Kumar ◽  
J. Roopa Jayasingh ◽  
Deepika Blessy Telagathoti
Author(s):  
Sakhawat Hossain ◽  
Md. Nahid Hasan ◽  
Md. Nazibul Islam ◽  
Mamunur Rashid Mukto ◽  
Md. Shahnewaz Abid ◽  
...  

Internet of Medical Things (IoMT) ensures the ability of healthcare professionals by allowing them to remotely access each patient’s personalized and accurate data. The accuracy, as well as the speed of treatments and diagnosis, is greatly improved as well. IoMT also enables healthcare professionals to monitor the status of their patients’ health in real-time. The behavior of people can be recorded with the intent of getting an online diagnosis, thus managing their one’s health is more effective. Tools like sensors and tracking devices, telemedicine, remote patient monitoring (RPM) and also virtual assistance makes these things happen. Perhaps healthcare professionals are mostly benefited by IoMT in their professions. So, in the case of a pandemic (COVID-19), our proposed application can spread the facilities of IoMT among the general people. The main purpose of this application is to make a system that compresses the number of coronavirus affected people by the extent of awareness. People can track data of confirmed, recovered, and fatal cases globally and locally through this app. People can also get information about the nearest COVID-19 hospitals with google map and get their helpline numbers. All these can be very important for Bangladesh, being a developing country. They can easily make doctor appointments through the system. People can get information about plasma & blood donation and they also can donate their blood and plasma by a requesting process. Users' information about their health can be saved in the cloud system from time to time so that a doctor can easily get all the information. So, our proposed app can help to control the COVID-19 pandemic situation and people will be benefited.


Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.


2021 ◽  
Vol 46 (5) ◽  
pp. 100800
Author(s):  
Abdulaziz Joury ◽  
Tamunoinemi Bob-Manuel ◽  
Alexandra Sanchez ◽  
Fnu Srinithya ◽  
Amber Sleem ◽  
...  

CHEST Journal ◽  
2021 ◽  
Vol 159 (2) ◽  
pp. 477-478
Author(s):  
Neeraj R. Desai ◽  
Edward J. Diamond

2021 ◽  
Vol 38 (3) ◽  
pp. 229-231
Author(s):  
Ahmad A Aalam ◽  
Colton Hood ◽  
Crystal Donelan ◽  
Adam Rutenberg ◽  
Erin M Kane ◽  
...  

COVID-19 has had a significant effect on healthcare resources worldwide, with our knowledge of the natural progression of the disease evolving for the individual patient. To allow for early detection of worsening clinical status, protect hospital capacity and provide extended access for vulnerable patients, our emergency department developed a remote patient monitoring programme for discharged patients with COVID-19. The programme uses a daily emailed secure link to a survey in which patients submit biometric and symptoms data for monitoring. Patients’ meeting criteria are escalated to a physician for a phone or video visit. Here, we describe the development, implementation and preliminary analysis of utilisation of the programme.


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