Remote patient monitoring, often shortened to RPM, is the use of connected devices, wearables, home sensors, apps, or software check-ins to collect health information outside the clinic and make it available to care teams over time. In healthcare, that can include blood pressure, glucose, pulse oximetry, weight, heart rhythm, physical activity, symptoms, medication use, or other measurements gathered between visits.
Why RPM Matters
Traditional care often sees patients intermittently even when their condition changes continuously. RPM helps close that gap by extending observation into the home and daily life. That is especially useful for chronic disease, post-discharge follow-up, medication titration, and early detection of deterioration that might otherwise go unnoticed until the next appointment or hospital admission.
RPM is not just about more data. It is about turning between-visit signals into something clinicians can act on without being overwhelmed.
Where AI Fits
AI helps RPM by filtering noise, spotting patterns, estimating change from a patient’s own baseline, and prioritizing which alerts deserve attention. Instead of sending every raw measurement to a care team, a stronger system highlights the combinations of signals most likely to matter. That is why RPM often overlaps with digital biomarkers, photoplethysmography, time series forecasting, anomaly detection, and clinical decision support.
What To Watch For
Bad RPM creates alert fatigue, unclear thresholds, and more documentation burden than clinical value. Good RPM is selective, clinically governed, and linked to an escalation pathway. The question is not whether a device can capture data. The question is whether the care team knows what to do when the data changes.
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Related concepts: Digital Biomarker, Photoplethysmography, Continuous Glucose Monitoring, Time Series Forecasting, Anomaly Detection, Sensor Fusion, Digital Mobility Outcome, and Clinical Decision Support.