AI in Remote Patient Monitoring: Promise and Precaution

AI in Remote Patient Monitoring: Promise and Precaution

Remote patient monitoring (RPM) has quietly harbored artificial intelligence (AI) for years, long before technology like ChatGPT made headlines. These were not the sophisticated large language models we know today. Rather, they were rule-based protocols that determined when to alert clinicians about concerning patient data. Now, as modern AI transforms healthcare delivery, RPM sits at the center of a fascinating evolution that promises both tremendous opportunity and significant responsibility.To understand AI’s current role in RPM, it helps to trace the technology’s path. Early RPM programs focused on high-acuity patients where there was a high short-term chance of a harmful (and expensive) event — think recently discharged heart failure patients with dedicated nurses monitoring their vital signs in near real time. 

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Today’s RPM has evolved further toward chronic disease management, monitoring medium-risk patients where the goal is preventing future complications rather than managing immediate crises. A hypertensive patient’s stroke risk over five years presents a very different monitoring challenge than a transplant recipient’s six-month survival outlook. This change in scale has created the perfect environment for AI to demonstrate its value — not by replacing human judgment, but by enhancing efficiency and catching patterns that might slip by clinicians and care teams, particularly during busy days that are now essentially the norm.



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