scholarly journals Remote Patient Monitoring for COVID-19 Patients: Comparisons and Framework for Reporting

2021 ◽  
Author(s):  
David Joyce ◽  
Aoife De Brún ◽  
Sophie Mulcahy Symmons ◽  
Robert Fox ◽  
Eilish McAuliffe
Keyword(s):  
Patient Monitoring ◽  
Patient Adherence ◽  
Opportunity To Learn ◽  
Rapid Review ◽  
Patient Acceptance ◽  
Sufficient Information ◽  
Remote Patient Monitoring ◽  
Tabular Format ◽  
Remote Patient ◽  
Specific Implementation

Abstract BackgroundRemote patient monitoring (RPM) has been implemented for COVID-19 patients by various health services at speed, without the opportunity to learn one from another. A lack of standardised reporting has hindered evaluation of RPM.AimsThe aims of this overview of RPM for COVID-19 patients are twofold: (1) to provide tabulated, descriptive information for a range of implementations to facilitate familiarization, learning and comparison; and based on this(2) to develop a framework for reporting to improve reporting consistency as a first step towards the development of reporting guidelines for RPM.MethodA rapid review of the literature for RPM for COVID-19 patients was conducted seeking studies that provided details of a specific implementation of RPM with sufficient information to compare one with another. The content of these articles was then reviewed and synthesised to a tabular format under common headings to facilitate ready comparison and to highlight omissions in reporting. Reporting consistencies and inconsistencies between the studies were then considered to develop a framework for reporting.ResultsThe studies suggested key common characteristics outlined under four headings: context, technology, process, and metrics. These were further divided into subheadings to provide a consistent tabular format to aid familiarization. Consideration of consistencies and inconsistencies in reporting suggests the following criteria be used for reporting: Dates, Rationale, Patients, Medical team, Technology provider, Communication mode, Patient equipment, Patient training, Staff training, Markers, Data Input Frequency, Thresholds for Escalation, Discharge and Metrics for: RPM Enrollment, Escalation, Patient acceptance, Staff acceptance, and Patient adherence.

Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure (Nov-RPM-HF) Trial: Rationale of Trial Design (Preprint)

2021 ◽  
Author(s):  
Ankit Bhatia ◽  
Gregory Ewald ◽  
Thomas Maddox
Keyword(s):  
Heart Failure ◽  
Data Collection ◽  
Patient Monitoring ◽  
Patient Adherence ◽  
Performance Status ◽  
Prospective Trial ◽  
Emergency Department Visits ◽  
Remote Patient Monitoring ◽  
Home Based ◽  
Remote Patient

UNSTRUCTURED Heart Failure (HF) remains a leading cause of mortality, and a major driver of healthcare utilization. Effective outpatient management requires the ability to identify and manage impending HF decompensation. Remote patient monitoring (RPM) aims to further address this current need in HF care. To date, RPM approaches employing noninvasive, home-based patient sensors have failed to demonstrate clinical efficacy. The Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure Trial (Nov-RPM-HF) aims to address current noninvasive RPM limitations. Nov-RPM-HF will evaluate a clinician-codesigned RPM platform employing emerging data collection and presentation tools. These tools include: (1) a ballistocardiograph to monitor nocturnal patient biometrics, such as heart and respiratory rate, (2) clinical alerts for abnormal biometrics, and (3) longitudinal data presentation for clinician review. Nov-RPM-HF is a 100-patient single-center prospective trial, evaluating patients over 6 months. Outcomes will include: (1) patient adherence to data collection, (2) patient/clinician-perceived utility of the RPM platform, (3) medication changes- including the titration of guideline-directed medical therapy to target doses, (4) HF symptoms/performance status, and (5) unplanned HF hospitalizations or emergency department visits. The results will help to inform the role of noninvasive RPM as a viable clinical management strategy in HF care.

scholarly journals Evaluating a remote patient monitoring program for automated peritoneal dialysis

2020 ◽  
Vol 40 (4) ◽  
pp. 377-383
Author(s):  
Juan G Ariza ◽  
Surrey M Walton ◽  
Mauricio Sanabria ◽  
Alfonso Bunch ◽  
Jasmin Vesga ◽  
...  
Keyword(s):  
Peritoneal Dialysis ◽  
Clinical Outcomes ◽  
Patient Monitoring ◽  
Patient Adherence ◽  
Monitoring Program ◽  
Cost Savings ◽  
Sensitivity Analyses ◽  
Remote Patient Monitoring ◽  
Automated Peritoneal Dialysis ◽  
Remote Patient

Background: The benefits of automated peritoneal dialysis (APD) have been established, but patient adherence to treatment remains a concern. Remote patient monitoring (RPM) programs are a potential solution; however, the cost implications are not well established. This study modeled, from the payer perspective, expected net costs and clinical consequences of a novel RPM program in Colombia. Methods: Amarkov model was used to project costs and clinical outcomes for APD patients with and without RPM. Clinical inputs were directly estimated from Renal Care Services data or taken from the literature. Dialysis costs were estimated from national fees. Inpatient costs were obtained from a recent Colombian study. The model projected overall direct costs and several clinical outcomes. Deterministic and probabilistic sensitivity analyses (DSA and PSA) were also conducted to characterize uncertainty in the results. Results: The model projected that the implementation of an RPM program costing US$35 per month in a cohort of 100 APD patients over 1 year would save US$121,233. The model also projected 31 additional months free of complications, 27 fewer hospitalizations, 518 fewer hospitalization days, and 6 fewer peritonitis episodes. In the DSA, results were most sensitive to hospitalization rates and days of hospitalization, but cost savings were robust. The PSA found there was a 91% chance for the RPM program to be cost saving. Conclusion: The results of the model suggest that RPM is cost-effective in APD patients which should be verified by a rigorous prospective cost analysis.

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.

Leadless and Wireless Cardiac Devices: The Next Frontier in Remote Patient Monitoring

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

scholarly journals Emerging Role of Remote Patient Monitoring in Pulmonary Care

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