Wireless remote patient monitoring in older adults

Author(s):  
Mirza M. Baig ◽  
H. GholamHosseini
2015 ◽  
Vol 44 ◽  
pp. 174-182 ◽  
Author(s):  
Jarod T. Giger ◽  
Natalie D. Pope ◽  
H. Bruce Vogt ◽  
Cassity Gutierrez ◽  
Lisa A. Newland ◽  
...  

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
H. A. Kolnick ◽  
Jennifer Miller ◽  
Olivia Dupree ◽  
Lisa Gualtieri

How might clinicians collect the vitals needed for effective scheduled video visits for older adults? This challenge was presented by AARP to graduate students in a Digital Health course at Tufts University School of Medicine. The design thinking process was used to create a product that would meet this need, keeping the needs and constraints of older adults, especially those with chronic conditions or other barriers to health, central to the solution. The initial steps involved understanding and empathizing with the target audience through interviews and by developing personas and scenarios that identified barriers and opportunities. The later steps were to ideate potential solutions, design a prototype, and define product success. The design thinking process led to the design of Home Health Hub, a remote patient monitoring (RPM) platform designed to meet the unique needs of older adults. Additionally, Home Health Hub can conceivably benefit all users of telehealth, regardless of health status—an important need during the COVID-19 pandemic, and in general due to increased use of virtual visits. Home Health Hub is one example of what can be achieved with the dedicated use of design thinking. The design thinking process can benefit public health practice as a whole by encouraging practitioners to delve into a problem to find the root causes and empathize with the needs and constraints of stakeholders to design innovative, human-centered solutions.


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

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