Diabetes Management Through Remote Patient Monitoring: The Importance of Patient Activation and Engagement with the Technology

2019 ◽  
Vol 25 (10) ◽  
pp. 952-959 ◽  
Author(s):  
Dejun Su ◽  
Tzeyu L. Michaud ◽  
Paul Estabrooks ◽  
Robert J. Schwab ◽  
Leslie A. Eiland ◽  
...  
Keyword(s):  
Patient Monitoring ◽  
Diabetes Management ◽  
Patient Activation ◽  
Remote Patient Monitoring ◽  
Remote Patient

Association Between Weight Loss and Glycemic Outcomes: A Post Hoc Analysis of a Remote Patient Monitoring Program for Diabetes Management

2020 ◽  
Vol 26 (5) ◽  
pp. 621-628 ◽  
Author(s):  
Tzeyu L. Michaud ◽  
Mohammad Siahpush ◽  
Paul Estabrooks ◽  
Robert J. Schwab ◽  
Tricia D. LeVan ◽  
...  
Keyword(s):  
Weight Loss ◽  
Patient Monitoring ◽  
Diabetes Management ◽  
Monitoring Program ◽  
Remote Patient Monitoring ◽  
Post Hoc Analysis ◽  
Remote Patient ◽  
Glycemic Outcomes ◽  
Post Hoc

Program completion and glycemic control in a remote patient monitoring program for diabetes management: Does gender matter?

2020 ◽  
Vol 159 ◽  
pp. 107944
Author(s):  
Tzeyu L. Michaud ◽  
Mohammad Siahpush ◽  
Keyonna M. King ◽  
Athena K. Ramos ◽  
Regina E. Robbins ◽  
...  
Keyword(s):  
Glycemic Control ◽  
Patient Monitoring ◽  
Diabetes Management ◽  
Monitoring Program ◽  
Program Completion ◽  
Remote Patient Monitoring ◽  
Remote Patient

Cost analysis of a remote patient monitoring programme for post-discharge patients with type 2 diabetes

2021 ◽  
pp. 1357633X2098539
Author(s):  
Tzeyu L Michaud ◽  
Jennie L Hill ◽  
Paul A Estabrooks ◽  
Dejun Su
Keyword(s):  
Type 2 Diabetes ◽  
Cost Analysis ◽  
Patient Monitoring ◽  
Diabetes Management ◽  
Economic Feasibility ◽  
Remote Patient Monitoring ◽  
Post Discharge ◽  
Scenario Analyses ◽  
Remote Patient

Introduction Assessing costs of an evidence-based health promotion programme is crucial to understand the economic feasibility of adopting or sustaining the programme. This study conducted a cost analysis of a remote patient monitoring (RPM) programme to enhance the post-discharge management of type 2 diabetes. Methods Using retrospective data collected during RPM implementation from September 2014 to February 2018, we estimated the costs of implementing an RPM in the primary care setting. Measures included total and average annual costs, costs per participant who was enrolled or completed the programme, and costs per person-day. We further conducted sensitivity and scenario analyses to examine variations in estimated programme costs associated with varying programme efficiencies and alternative personnel compositions of the RPM team. Results The total RPM implementation costs were estimated at US$4,374,544 with an average annual programme costs of US$1,249,870, which translated to US$3207 per participant ( n = 1364) completing the three-month programme. The per person-day cost was averaged at US$24 (182,932 person-days). Sensitivity and scenario analyses results indicate that the sustainment costs were approximately US$1.6 million annually and the per-person-day costs were between US$21 and US$29 with each nurse coach on average serving a panel of 62–93 patients. Conclusion The implementation and sustainment costs of an RPM programme, estimated under various assumptions of programme efficiency and care team compositions, as exemplified in this study, will help healthcare organizations make informed decisions in budgeting for and sustaining telehealth programmes to enhance diabetes management.

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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