861-P: Glycemic Improvements following Mobile-Enabled Remote Patient Monitoring: A Randomized Control Study

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 861-P
Author(s):  
TONG SHENG ◽  
LINDA PARKS ◽  
SARINE BABIKIAN ◽  
VIKRAM SINGH ◽  
MICHAEL GREENFIELD ◽  
...  
2021 ◽  
Author(s):  
Sarah Eichler ◽  
Sebastian Carnarius ◽  
Edgar Steiger ◽  
Dominik von Stillfried

Aim of the study The aim of the study was to investigate satisfaction, saving of time and the possible reduction of patient visits to practices that use Remote Patient Monitoring (RPM) during treatment compared to usual care. Methods In a case-control study between October 2020 and May 2021, the participating practices were randomized into three groups (two different RPM systems, one control). The doctors were required to enroll patients with acute respiratory infection ≥ 18 years who have a web-enabled device. After a three-month study phase, the doctors were asked to describe the treatment of their patients via online survey. The patients were also questioned. The analysis was carried out descriptively and with group comparisons. Results 51 practices with 121 patients were included. Overall, the results show a positive assessment of digital care on the patient side. As for the doctors, handling and integration of the systems into consisting practice processes seem to be a challenge. Further, the number of patient visits to the practice was not reduced by using the systems and the doctors did not save time, but the relationship to the patients was intensified. Conclusion Even if there were no indications for more efficiency by using the RPM systems, the doctors see great potential to intensify the interaction between doctor and patient. In particular, more intensive contact with patients with chronic diseases (e. g. COPD, long-COVID) could be of long term interest and importance for doctors in outpatient care. Trial Registration: DRKS00023553 Keywords: RPM, outpatient care, chains of infection, respiratory infection  


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 202-OR
Author(s):  
ATHENA PHILIS-TSIMIKAS ◽  
ADDIE L. FORTMANN ◽  
ALESSANDRA BASTIAN ◽  
ARATI KANCHI ◽  
RICARDO ABAD ◽  
...  

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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