Towards Blockchain-Based Secure Data Management for Remote Patient Monitoring

Author(s):  
Md Jobair Hossain Faruk ◽  
Hossain Shahriar ◽  
Maria Valero ◽  
Sweta Sneha ◽  
Sheikh I. Ahamed ◽  
...  
JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 471-478 ◽  
Author(s):  
Robab Abdolkhani ◽  
Kathleen Gray ◽  
Ann Borda ◽  
Ruth DeSouza

Abstract Background Patient-Generated Health Data (PGHD) in remote monitoring programs is a promising source of precise, personalized data, encouraged by expanding growth in the health technologies market. However, PGHD utilization in clinical settings is low. One of the critical challenges that impedes confident clinical use of PGHD is that these data are not managed according to any recognized approach for data quality assurance. Objective This article aims to identify the PGHD management and quality challenges that such an approach must address, as these are expressed by key PGHD stakeholder groups. Materials and Methods In-depth interviews were conducted with 20 experts who have experience in the use of PGHD in remote patient monitoring, including: healthcare providers, health information professionals within clinical settings, and commercial providers of remote monitoring solutions. Participants were asked to describe PGHD management processes in the remote monitoring programs in which they are involved, and to express their perspectives on PGHD quality challenges during the data management stages. Results The remote monitoring programs in the study did not follow clear PGHD management or quality assurance approach. Participants were not fully aware of all the considerations of PGHD quality. Digital health literacy, wearable accuracy, difficulty in data interpretation, and lack of PGHD integration with electronic medical record systems were among the key challenges identified that impact PGHD quality. Conclusion Co-development of PGHD quality guidelines with relevant stakeholders, including patients, is needed to ensure that quality remote monitoring data from wearables is available for use in more precise and personalized patient care.


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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