The Merits and Limitations of Telemedicine, Hospital at Home, and Remote Patient Monitoring

2021 ◽  
pp. 11-33
Author(s):  
Paul Cerrato ◽  
John Halamka
Keyword(s):  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Hospital At Home ◽  
Remote Patient ◽  
At Home

scholarly journals Maximizing VA Remote Patient Monitoring During the COVID-19 Response

2021 ◽  
Author(s):  
Catherine Buck ◽  
Rita Kobb ◽  
Ron Sandreth ◽  
Lisa Alexander ◽  
Sherron Olliff ◽  
...  
Keyword(s):  
Disease Management ◽  
Large Scale ◽  
Patient Monitoring ◽  
The United States ◽  
Health Administration ◽  
Remote Patient Monitoring ◽  
Management Protocol ◽  
Remote Patient ◽  
The Impact ◽  
At Home

Abstract  Objective: The Veterans Health Administration has one of the largest remote patient monitoring programs in the United States and is supported by an enterprise-wide infrastructure for providers, clinicians, staff, Veterans, and caregivers. The COVID-19 pandemic, however, presented new challenges: a sudden need to provide large-scale remote monitoring for a new disease that did not yet have a disease management protocol. VHA needed to be ready within weeks to provide this daily monitoring for hundreds — even thousands — of Veterans.  Methods: The U.S. Department of Veterans Affairs Office of Connected Care already had a comprehensive infrastructure in place for its Remote Patient Monitoring – Home Telehealth (RPM – HT) program. Connected Care activated and built on this infrastructure to support providers, clinicians, and staff in their efforts to rapidly bring RPM – HT to Veterans across the nation when they had COVID-19 symptoms or exposure. To do this, Connected Care activated an emergency management plan, rapidly developed a new COVID-19-specific disease management protocol, added weekend monitoring, and procured critically needed monitoring supplies, such as thermometers and pulse oximeters. Connected Care’s strong foundation allowed for innovation and flexibility, such as the training of non-RPM – HT staff in RPM – HT processes, RPM – HT enrollment within acute care settings, and new strategic partnerships. Outcomes: More than 23,500 Veterans were enrolled for COVID-19-related monitoring from March 2020 to May 2021. At points in December 2020 and January 2021, the number of Veterans being monitored in a single day topped 2,000. Even with this rapid buildup, patient satisfaction levels remained at about 90% in numerous categories. In addition, the percentage of Veterans admitted to VA facilities while on COVID-19-related home monitoring has been extremely low, at 4%, a potential indicator that the monitoring system has been helpful in enabling Veterans who did have the virus to convalesce at home. Further study is needed to determine the impact RPM – HT enrollment for COVID-19 care had on the need for inpatient care. Conclusion: The Office of Connected Care’s established, enterprise-wide RPM – HT business, clinical, and technical infrastructure enabled VHA to enter the COVID-19 public health emergency well-positioned for the rapid deployment and growth of at-home and mobile monitoring. As the COVID-19 emergency made at-home management of Veterans increasingly important, the national RPM – HT program successfully adapted its practices to meet Veteran, caregiver, and staff needs.

scholarly journals A Primary Care Answer to a Pandemic: Keeping a Population of Patients Safe at Home through Chronic Care Management and Remote Patient Monitoring

2020 ◽  
Vol 14 (6) ◽  
pp. 595-601
Author(s):  
David Donohue
Keyword(s):  
Primary Care ◽  
Care Management ◽  
Chronic Care ◽  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Chronic Care Management ◽  
Care Managers ◽  
Remote Patient ◽  
Mental And Physical Health ◽  
At Home

The pandemic caused by the coronavirus disease of 2019 (COVID-19) challenged primary care providers (PCPs) to continue to deliver care for their patients, while also remaining financially stable. Most practices have experienced declining revenue due to fewer in-person patient visits. To help offset this and to continue to provide safe patient care, practices have shifted toward using remote options. Chronic Care Management (CCM) and Remote Patient Monitoring (RPM) are benefits available to Medicare fee-for-service patients, which allow a medical practice to deliver expanded care and generate much-needed revenue. These services can be delivered by clinical staff called care managers. A top health priority for most seniors is to effectively self-isolate to reduce risk of COVID-19, while maintaining mental and physical health. We developed a Safe at Home program, designed to be run by care managers through CCM and RPM, with the use of a remote monitoring technology. Safe at Home tracks signs and symptoms of COVID-19, mental and physical health, and lifestyle behaviors that can affect immune function. We project that this service can complement regular telehealth PCP visits and deliver population health monitoring services, while generating substantial revenue for the practice.

scholarly journals Using Medical Device Standards for Design and Risk Management of Immersive Virtual Reality for At-Home Therapy and Remote Patient Monitoring

10.2196/26942 ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. e26942
Author(s):  
Joseph Peter Salisbury
Keyword(s):  
Risk Management ◽  
Virtual Reality ◽  
Medical Device ◽  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Home Therapy ◽  
User Stories ◽  
Risk Management Process ◽  
Remote Patient ◽  
At Home

Numerous virtual reality (VR) systems have received regulatory clearance as therapeutic medical devices for in-clinic and at-home use. These systems enable remote patient monitoring of clinician-prescribed rehabilitation exercises, although most of these systems are nonimmersive. With the expanding availability of affordable and easy-to-use head-mounted display (HMD)-based VR, there is growing interest in immersive VR therapies. However, HMD-based VR presents unique risks. Following standards for medical device development, the objective of this paper is to demonstrate a risk management process for a generic immersive VR system for remote patient monitoring of at-home therapy. Regulations, standards, and guidance documents applicable to therapeutic VR design are reviewed to provide necessary background. Generic requirements for an immersive VR system for home use and remote patient monitoring are identified using predicate analysis and specified for both patients and clinicians using user stories. To analyze risk, failure modes and effects analysis, adapted for medical device risk management, is performed on the generic user stories and a set of risk control measures is proposed. Many therapeutic applications of VR would be regulated as a medical device if they were to be commercially marketed. Understanding relevant standards for design and risk management early in the development process can help expedite the availability of innovative VR therapies that are safe and effective.

scholarly journals Using Medical Device Standards for Design and Risk Management of Immersive Virtual Reality for At-Home Therapy and Remote Patient Monitoring (Preprint)

2021 ◽  
Author(s):  
Joseph Peter Salisbury
Keyword(s):  
Risk Management ◽  
Virtual Reality ◽  
Medical Device ◽  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Home Therapy ◽  
User Stories ◽  
Risk Management Process ◽  
Remote Patient ◽  
At Home

UNSTRUCTURED Numerous virtual reality (VR) systems have received regulatory clearance as therapeutic medical devices for in-clinic and at-home use. These systems enable remote patient monitoring of clinician-prescribed rehabilitation exercises, although most of these systems are nonimmersive. With the expanding availability of affordable and easy-to-use head-mounted display (HMD)-based VR, there is growing interest in immersive VR therapies. However, HMD-based VR presents unique risks. Following standards for medical device development, the objective of this paper is to demonstrate a risk management process for a generic immersive VR system for remote patient monitoring of at-home therapy. Regulations, standards, and guidance documents applicable to therapeutic VR design are reviewed to provide necessary background. Generic requirements for an immersive VR system for home use and remote patient monitoring are identified using predicate analysis and specified for both patients and clinicians using user stories. To analyze risk, failure modes and effects analysis, adapted for medical device risk management, is performed on the generic user stories and a set of risk control measures is proposed. Many therapeutic applications of VR would be regulated as a medical device if they were to be commercially marketed. Understanding relevant standards for design and risk management early in the development process can help expedite the availability of innovative VR therapies that are safe and effective.

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

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.

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