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Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

The first review focuses on randomized controlled trials (RCTs) using RPM as an intervention to treat or improve AECOPD. The second review investigates studies integrating machine learning with RPM to predict AECOPD. This comprehensive approach enables us to provide a novel understanding of digitally enabled AECOPD interventions.

Henry Mark Granger Glyde, Caitlin Morgan, Tom M A Wilkinson, Ian T Nabney, James W Dodd

J Med Internet Res 2024;26:e52143

Operational Implementation of Remote Patient Monitoring Within a Large Ambulatory Health System: Multimethod Qualitative Case Study

Operational Implementation of Remote Patient Monitoring Within a Large Ambulatory Health System: Multimethod Qualitative Case Study

In this study, early adopters of RPM technology were clinicians in the metro New York practice network (Manhattan, Brooklyn, and western Long Island) who used RPM between 2018 and 2021, prior to the health system’s system-wide RPM initiative.

Katharine Lawrence, Nina Singh, Zoe Jonassen, Lisa L Groom, Veronica Alfaro Arias, Soumik Mandal, Antoinette Schoenthaler, Devin Mann, Oded Nov, Graham Dove

JMIR Hum Factors 2023;10:e45166

The Next Frontier of Remote Patient Monitoring: Hospital at Home

The Next Frontier of Remote Patient Monitoring: Hospital at Home

To our knowledge, no literature exists evaluating the cost-effectiveness of RPM programs for Ha H; however, studies evaluating RPM for chronic conditions, such as HF, COPD, and hypertension, have shown signs of cost-effectiveness [22].

David Whitehead, Jared Conley

J Med Internet Res 2023;25:e42335